research paper on teaching learning process

• Tutorial Review
• Open access
• Published: 24 January 2018

Teaching the science of learning

• Yana Weinstein   ORCID: orcid.org/0000-0002-5144-968X 1 ,
• Christopher R. Madan 2 , 3 &
• Megan A. Sumeracki 4  

Cognitive Research: Principles and Implications volume  3 , Article number:  2 ( 2018 ) Cite this article

259k Accesses

98 Citations

764 Altmetric

Metrics details

The science of learning has made a considerable contribution to our understanding of effective teaching and learning strategies. However, few instructors outside of the field are privy to this research. In this tutorial review, we focus on six specific cognitive strategies that have received robust support from decades of research: spaced practice, interleaving, retrieval practice, elaboration, concrete examples, and dual coding. We describe the basic research behind each strategy and relevant applied research, present examples of existing and suggested implementation, and make recommendations for further research that would broaden the reach of these strategies.

Significance

Education does not currently adhere to the medical model of evidence-based practice (Roediger, 2013 ). However, over the past few decades, our field has made significant advances in applying cognitive processes to education. From this work, specific recommendations can be made for students to maximize their learning efficiency (Dunlosky, Rawson, Marsh, Nathan, & Willingham, 2013 ; Roediger, Finn, & Weinstein, 2012 ). In particular, a review published 10 years ago identified a limited number of study techniques that have received solid evidence from multiple replications testing their effectiveness in and out of the classroom (Pashler et al., 2007 ). A recent textbook analysis (Pomerance, Greenberg, & Walsh, 2016 ) took the six key learning strategies from this report by Pashler and colleagues, and found that very few teacher-training textbooks cover any of these six principles – and none cover them all, suggesting that these strategies are not systematically making their way into the classroom. This is the case in spite of multiple recent academic (e.g., Dunlosky et al., 2013 ) and general audience (e.g., Dunlosky, 2013 ) publications about these strategies. In this tutorial review, we present the basic science behind each of these six key principles, along with more recent research on their effectiveness in live classrooms, and suggest ideas for pedagogical implementation. The target audience of this review is (a) educators who might be interested in integrating the strategies into their teaching practice, (b) science of learning researchers who are looking for open questions to help determine future research priorities, and (c) researchers in other subfields who are interested in the ways that principles from cognitive psychology have been applied to education.

While the typical teacher may not be exposed to this research during teacher training, a small cohort of teachers intensely interested in cognitive psychology has recently emerged. These teachers are mainly based in the UK, and, anecdotally (e.g., Dennis (2016), personal communication), appear to have taken an interest in the science of learning after reading Make it Stick (Brown, Roediger, & McDaniel, 2014 ; see Clark ( 2016 ) for an enthusiastic review of this book on a teacher’s blog, and “Learning Scientists” ( 2016c ) for a collection). In addition, a grassroots teacher movement has led to the creation of “researchED” – a series of conferences on evidence-based education (researchED, 2013 ). The teachers who form part of this network frequently discuss cognitive psychology techniques and their applications to education on social media (mainly Twitter; e.g., Fordham, 2016 ; Penfound, 2016 ) and on their blogs, such as Evidence Into Practice ( https://evidenceintopractice.wordpress.com/ ), My Learning Journey ( http://reflectionsofmyteaching.blogspot.com/ ), and The Effortful Educator ( https://theeffortfuleducator.com/ ). In general, the teachers who write about these issues pay careful attention to the relevant literature, often citing some of the work described in this review.

These informal writings, while allowing teachers to explore their approach to teaching practice (Luehmann, 2008 ), give us a unique window into the application of the science of learning to the classroom. By examining these blogs, we can not only observe how basic cognitive research is being applied in the classroom by teachers who are reading it, but also how it is being misapplied, and what questions teachers may be posing that have gone unaddressed in the scientific literature. Throughout this review, we illustrate each strategy with examples of how it can be implemented (see Table  1 and Figs.  1 , 2 , 3 , 4 , 5 , 6 and 7 ), as well as with relevant teacher blog posts that reflect on its application, and draw upon this work to pin-point fruitful avenues for further basic and applied research.

Spaced practice schedule for one week. This schedule is designed to represent a typical timetable of a high-school student. The schedule includes four one-hour study sessions, one longer study session on the weekend, and one rest day. Notice that each subject is studied one day after it is covered in school, to create spacing between classes and study sessions. Copyright note: this image was produced by the authors

a Blocked practice and interleaved practice with fraction problems. In the blocked version, students answer four multiplication problems consecutively. In the interleaved version, students answer a multiplication problem followed by a division problem and then an addition problem, before returning to multiplication. For an experiment with a similar setup, see Patel et al. ( 2016 ). Copyright note: this image was produced by the authors. b Illustration of interleaving and spacing. Each color represents a different homework topic. Interleaving involves alternating between topics, rather than blocking. Spacing involves distributing practice over time, rather than massing. Interleaving inherently involves spacing as other tasks naturally “fill” the spaces between interleaved sessions. Copyright note: this image was produced by the authors, adapted from Rohrer ( 2012 )

Concept map illustrating the process and resulting benefits of retrieval practice. Retrieval practice involves the process of withdrawing learned information from long-term memory into working memory, which requires effort. This produces direct benefits via the consolidation of learned information, making it easier to remember later and causing improvements in memory, transfer, and inferences. Retrieval practice also produces indirect benefits of feedback to students and teachers, which in turn can lead to more effective study and teaching practices, with a focus on information that was not accurately retrieved. Copyright note: this figure originally appeared in a blog post by the first and third authors ( http://www.learningscientists.org/blog/2016/4/1-1 )

Illustration of “how” and “why” questions (i.e., elaborative interrogation questions) students might ask while studying the physics of flight. To help figure out how physics explains flight, students might ask themselves the following questions: “How does a plane take off?”; “Why does a plane need an engine?”; “How does the upward force (lift) work?”; “Why do the wings have a curved upper surface and a flat lower surface?”; and “Why is there a downwash behind the wings?”. Copyright note: the image of the plane was downloaded from Pixabay.com and is free to use, modify, and share

Three examples of physics problems that would be categorized differently by novices and experts. The problems in ( a ) and ( c ) look similar on the surface, so novices would group them together into one category. Experts, however, will recognize that the problems in ( b ) and ( c ) both relate to the principle of energy conservation, and so will group those two problems into one category instead. Copyright note: the figure was produced by the authors, based on figures in Chi et al. ( 1981 )

Example of how to enhance learning through use of a visual example. Students might view this visual representation of neural communications with the words provided, or they could draw a similar visual representation themselves. Copyright note: this figure was produced by the authors

Example of word properties associated with visual, verbal, and motor coding for the word “SPOON”. A word can evoke multiple types of representation (“codes” in dual coding theory). Viewing a word will automatically evoke verbal representations related to its component letters and phonemes. Words representing objects (i.e., concrete nouns) will also evoke visual representations, including information about similar objects, component parts of the object, and information about where the object is typically found. In some cases, additional codes can also be evoked, such as motor-related properties of the represented object, where contextual information related to the object’s functional intention and manipulation action may also be processed automatically when reading the word. Copyright note: this figure was produced by the authors and is based on Aylwin ( 1990 ; Fig.  2 ) and Madan and Singhal ( 2012a , Fig.  3 )

Spaced practice

The benefits of spaced (or distributed) practice to learning are arguably one of the strongest contributions that cognitive psychology has made to education (Kang, 2016 ). The effect is simple: the same amount of repeated studying of the same information spaced out over time will lead to greater retention of that information in the long run, compared with repeated studying of the same information for the same amount of time in one study session. The benefits of distributed practice were first empirically demonstrated in the 19 th century. As part of his extensive investigation into his own memory, Ebbinghaus ( 1885/1913 ) found that when he spaced out repetitions across 3 days, he could almost halve the number of repetitions necessary to relearn a series of 12 syllables in one day (Chapter 8). He thus concluded that “a suitable distribution of [repetitions] over a space of time is decidedly more advantageous than the massing of them at a single time” (Section 34). For those who want to read more about Ebbinghaus’s contribution to memory research, Roediger ( 1985 ) provides an excellent summary.

Since then, hundreds of studies have examined spacing effects both in the laboratory and in the classroom (Kang, 2016 ). Spaced practice appears to be particularly useful at large retention intervals: in the meta-analysis by Cepeda, Pashler, Vul, Wixted, and Rohrer ( 2006 ), all studies with a retention interval longer than a month showed a clear benefit of distributed practice. The “new theory of disuse” (Bjork & Bjork, 1992 ) provides a helpful mechanistic explanation for the benefits of spacing to learning. This theory posits that memories have both retrieval strength and storage strength. Whereas retrieval strength is thought to measure the ease with which a memory can be recalled at a given moment, storage strength (which cannot be measured directly) represents the extent to which a memory is truly embedded in the mind. When studying is taking place, both retrieval strength and storage strength receive a boost. However, the extent to which storage strength is boosted depends upon retrieval strength, and the relationship is negative: the greater the current retrieval strength, the smaller the gains in storage strength. Thus, the information learned through “cramming” will be rapidly forgotten due to high retrieval strength and low storage strength (Bjork & Bjork, 2011 ), whereas spacing out learning increases storage strength by allowing retrieval strength to wane before restudy.

Teachers can introduce spacing to their students in two broad ways. One involves creating opportunities to revisit information throughout the semester, or even in future semesters. This does involve some up-front planning, and can be difficult to achieve, given time constraints and the need to cover a set curriculum. However, spacing can be achieved with no great costs if teachers set aside a few minutes per class to review information from previous lessons. The second method involves putting the onus to space on the students themselves. Of course, this would work best with older students – high school and above. Because spacing requires advance planning, it is crucial that the teacher helps students plan their studying. For example, teachers could suggest that students schedule study sessions on days that alternate with the days on which a particular class meets (e.g., schedule review sessions for Tuesday and Thursday when the class meets Monday and Wednesday; see Fig.  1 for a more complete weekly spaced practice schedule). It important to note that the spacing effect refers to information that is repeated multiple times, rather than the idea of studying different material in one long session versus spaced out in small study sessions over time. However, for teachers and particularly for students planning a study schedule, the subtle difference between the two situations (spacing out restudy opportunities, versus spacing out studying of different information over time) may be lost. Future research should address the effects of spacing out studying of different information over time, whether the same considerations apply in this situation as compared to spacing out restudy opportunities, and how important it is for teachers and students to understand the difference between these two types of spaced practice.

It is important to note that students may feel less confident when they space their learning (Bjork, 1999 ) than when they cram. This is because spaced learning is harder – but it is this “desirable difficulty” that helps learning in the long term (Bjork, 1994 ). Students tend to cram for exams rather than space out their learning. One explanation for this is that cramming does “work”, if the goal is only to pass an exam. In order to change students’ minds about how they schedule their studying, it might be important to emphasize the value of retaining information beyond a final exam in one course.

Ideas for how to apply spaced practice in teaching have appeared in numerous teacher blogs (e.g., Fawcett, 2013 ; Kraft, 2015 ; Picciotto, 2009 ). In England in particular, as of 2013, high-school students need to be able to remember content from up to 3 years back on cumulative exams (General Certificate of Secondary Education (GCSE) and A-level exams; see CIFE, 2012 ). A-levels in particular determine what subject students study in university and which programs they are accepted into, and thus shape the path of their academic career. A common approach for dealing with these exams has been to include a “revision” (i.e., studying or cramming) period of a few weeks leading up to the high-stakes cumulative exams. Now, teachers who follow cognitive psychology are advocating a shift of priorities to spacing learning over time across the 3 years, rather than teaching a topic once and then intensely reviewing it weeks before the exam (Cox, 2016a ; Wood, 2017 ). For example, some teachers have suggested using homework assignments as an opportunity for spaced practice by giving students homework on previous topics (Rose, 2014 ). However, questions remain, such as whether spaced practice can ever be effective enough to completely alleviate the need or utility of a cramming period (Cox, 2016b ), and how one can possibly figure out the optimal lag for spacing (Benney, 2016 ; Firth, 2016 ).

There has been considerable research on the question of optimal lag, and much of it is quite complex; two sessions neither too close together (i.e., cramming) nor too far apart are ideal for retention. In a large-scale study, Cepeda, Vul, Rohrer, Wixted, and Pashler ( 2008 ) examined the effects of the gap between study sessions and the interval between study and test across long periods, and found that the optimal gap between study sessions was contingent on the retention interval. Thus, it is not clear how teachers can apply the complex findings on lag to their own classrooms.

A useful avenue of research would be to simplify the research paradigms that are used to study optimal lag, with the goal of creating a flexible, spaced-practice framework that teachers could apply and tailor to their own teaching needs. For example, an Excel macro spreadsheet was recently produced to help teachers plan for lagged lessons (Weinstein-Jones & Weinstein, 2017 ; see Weinstein & Weinstein-Jones ( 2017 ) for a description of the algorithm used in the spreadsheet), and has been used by teachers to plan their lessons (Penfound, 2017 ). However, one teacher who found this tool helpful also wondered whether the more sophisticated plan was any better than his own method of manually selecting poorly understood material from previous classes for later review (Lovell, 2017 ). This direction is being actively explored within personalized online learning environments (Kornell & Finn, 2016 ; Lindsey, Shroyer, Pashler, & Mozer, 2014 ), but teachers in physical classrooms might need less technologically-driven solutions to teach cohorts of students.

It seems teachers would greatly appreciate a set of guidelines for how to implement spacing in the curriculum in the most effective, but also the most efficient manner. While the cognitive field has made great advances in terms of understanding the mechanisms behind spacing, what teachers need more of are concrete evidence-based tools and guidelines for direct implementation in the classroom. These could include more sophisticated and experimentally tested versions of the software described above (Weinstein-Jones & Weinstein, 2017 ), or adaptable templates of spaced curricula. Moreover, researchers need to evaluate the effectiveness of these tools in a real classroom environment, over a semester or academic year, in order to give pedagogically relevant evidence-based recommendations to teachers.

Interleaving

Another scheduling technique that has been shown to increase learning is interleaving. Interleaving occurs when different ideas or problem types are tackled in a sequence, as opposed to the more common method of attempting multiple versions of the same problem in a given study session (known as blocking). Interleaving as a principle can be applied in many different ways. One such way involves interleaving different types of problems during learning, which is particularly applicable to subjects such as math and physics (see Fig.  2 a for an example with fractions, based on a study by Patel, Liu, & Koedinger, 2016 ). For example, in a study with college students, Rohrer and Taylor ( 2007 ) found that shuffling math problems that involved calculating the volume of different shapes resulted in better test performance 1 week later than when students answered multiple problems about the same type of shape in a row. This pattern of results has also been replicated with younger students, for example 7 th grade students learning to solve graph and slope problems (Rohrer, Dedrick, & Stershic, 2015 ). The proposed explanation for the benefit of interleaving is that switching between different problem types allows students to acquire the ability to choose the right method for solving different types of problems rather than learning only the method itself, and not when to apply it.

Do the benefits of interleaving extend beyond problem solving? The answer appears to be yes. Interleaving can be helpful in other situations that require discrimination, such as inductive learning. Kornell and Bjork ( 2008 ) examined the effects of interleaving in a task that might be pertinent to a student of the history of art: the ability to match paintings to their respective painters. Students who studied different painters’ paintings interleaved at study were more successful on a later identification test than were participants who studied the paintings blocked by painter. Birnbaum, Kornell, Bjork, and Bjork ( 2013 ) proposed the discriminative-contrast hypothesis to explain that interleaving enhances learning by allowing the comparison between exemplars of different categories. They found support for this hypothesis in a set of experiments with bird categorization: participants benefited from interleaving and also from spacing, but not when the spacing interrupted side-by-side comparisons of birds from different categories.

Another type of interleaving involves the interleaving of study and test opportunities. This type of interleaving has been applied, once again, to problem solving, whereby students alternate between attempting a problem and viewing a worked example (Trafton & Reiser, 1993 ); this pattern appears to be superior to answering a string of problems in a row, at least with respect to the amount of time it takes to achieve mastery of a procedure (Corbett, Reed, Hoffmann, MacLaren, & Wagner, 2010 ). The benefits of interleaving study and test opportunities – rather than blocking study followed by attempting to answer problems or questions – might arise due to a process known as “test-potentiated learning”. That is, a study opportunity that immediately follows a retrieval attempt may be more fruitful than when that same studying was not preceded by retrieval (Arnold & McDermott, 2013 ).

For problem-based subjects, the interleaving technique is straightforward: simply mix questions on homework and quizzes with previous materials (which takes care of spacing as well); for languages, mix vocabulary themes rather than blocking by theme (Thomson & Mehring, 2016 ). But interleaving as an educational strategy ought to be presented to teachers with some caveats. Research has focused on interleaving material that is somewhat related (e.g., solving different mathematical equations, Rohrer et al., 2015 ), whereas students sometimes ask whether they should interleave material from different subjects – a practice that has not received empirical support (Hausman & Kornell, 2014 ). When advising students how to study independently, teachers should thus proceed with caution. Since it is easy for younger students to confuse this type of unhelpful interleaving with the more helpful interleaving of related information, it may be best for teachers of younger grades to create opportunities for interleaving in homework and quiz assignments rather than putting the onus on the students themselves to make use of the technique. Technology can be very helpful here, with apps such as Quizlet, Memrise, Anki, Synap, Quiz Champ, and many others (see also “Learning Scientists”, 2017 ) that not only allow instructor-created quizzes to be taken by students, but also provide built-in interleaving algorithms so that the burden does not fall on the teacher or the student to carefully plan which items are interleaved when.

An important point to consider is that in educational practice, the distinction between spacing and interleaving can be difficult to delineate. The gap between the scientific and classroom definitions of interleaving is demonstrated by teachers’ own writings about this technique. When they write about interleaving, teachers often extend the term to connote a curriculum that involves returning to topics multiple times throughout the year (e.g., Kirby, 2014 ; see “Learning Scientists” ( 2016a ) for a collection of similar blog posts by several other teachers). The “interleaving” of topics throughout the curriculum produces an effect that is more akin to what cognitive psychologists call “spacing” (see Fig.  2 b for a visual representation of the difference between interleaving and spacing). However, cognitive psychologists have not examined the effects of structuring the curriculum in this way, and open questions remain: does repeatedly circling back to previous topics throughout the semester interrupt the learning of new information? What are some effective techniques for interleaving old and new information within one class? And how does one determine the balance between old and new information?

Retrieval practice

While tests are most often used in educational settings for assessment, a lesser-known benefit of tests is that they actually improve memory of the tested information. If we think of our memories as libraries of information, then it may seem surprising that retrieval (which happens when we take a test) improves memory; however, we know from a century of research that retrieving knowledge actually strengthens it (see Karpicke, Lehman, & Aue, 2014 ). Testing was shown to strengthen memory as early as 100 years ago (Gates, 1917 ), and there has been a surge of research in the last decade on the mnemonic benefits of testing, or retrieval practice . Most of the research on the effectiveness of retrieval practice has been done with college students (see Roediger & Karpicke, 2006 ; Roediger, Putnam, & Smith, 2011 ), but retrieval-based learning has been shown to be effective at producing learning for a wide range of ages, including preschoolers (Fritz, Morris, Nolan, & Singleton, 2007 ), elementary-aged children (e.g., Karpicke, Blunt, & Smith, 2016 ; Karpicke, Blunt, Smith, & Karpicke, 2014 ; Lipko-Speed, Dunlosky, & Rawson, 2014 ; Marsh, Fazio, & Goswick, 2012 ; Ritchie, Della Sala, & McIntosh, 2013 ), middle-school students (e.g., McDaniel, Thomas, Agarwal, McDermott, & Roediger, 2013 ; McDermott, Agarwal, D’Antonio, Roediger, & McDaniel, 2014 ), and high-school students (e.g., McDermott et al., 2014 ). In addition, the effectiveness of retrieval-based learning has been extended beyond simple testing to other activities in which retrieval practice can be integrated, such as concept mapping (Blunt & Karpicke, 2014 ; Karpicke, Blunt, et al., 2014 ; Ritchie et al., 2013 ).

A debate is currently ongoing as to the effectiveness of retrieval practice for more complex materials (Karpicke & Aue, 2015 ; Roelle & Berthold, 2017 ; Van Gog & Sweller, 2015 ). Practicing retrieval has been shown to improve the application of knowledge to new situations (e.g., Butler, 2010 ; Dirkx, Kester, & Kirschner, 2014 ); McDaniel et al., 2013 ; Smith, Blunt, Whiffen, & Karpicke, 2016 ); but see Tran, Rohrer, and Pashler ( 2015 ) and Wooldridge, Bugg, McDaniel, and Liu ( 2014 ), for retrieval practice studies that showed limited or no increased transfer compared to restudy. Retrieval practice effects on higher-order learning may be more sensitive than fact learning to encoding factors, such as the way material is presented during study (Eglington & Kang, 2016 ). In addition, retrieval practice may be more beneficial for higher-order learning if it includes more scaffolding (Fiechter & Benjamin, 2017 ; but see Smith, Blunt, et al., 2016 ) and targeted practice with application questions (Son & Rivas, 2016 ).

How does retrieval practice help memory? Figure  3 illustrates both the direct and indirect benefits of retrieval practice identified by the literature. The act of retrieval itself is thought to strengthen memory (Karpicke, Blunt, et al., 2014 ; Roediger & Karpicke, 2006 ; Smith, Roediger, & Karpicke, 2013 ). For example, Smith et al. ( 2013 ) showed that if students brought information to mind without actually producing it (covert retrieval), they remembered the information just as well as if they overtly produced the retrieved information (overt retrieval). Importantly, both overt and covert retrieval practice improved memory over control groups without retrieval practice, even when feedback was not provided. The fact that bringing information to mind in the absence of feedback or restudy opportunities improves memory leads researchers to conclude that it is the act of retrieval – thinking back to bring information to mind – that improves memory of that information.

The benefit of retrieval practice depends to a certain extent on successful retrieval (see Karpicke, Lehman, et al., 2014 ). For example, in Experiment 4 of Smith et al. ( 2013 ), students successfully retrieved 72% of the information during retrieval practice. Of course, retrieving 72% of the information was compared to a restudy control group, during which students were re-exposed to 100% of the information, creating a bias in favor of the restudy condition. Yet retrieval led to superior memory later compared to the restudy control. However, if retrieval success is extremely low, then it is unlikely to improve memory (e.g., Karpicke, Blunt, et al., 2014 ), particularly in the absence of feedback. On the other hand, if retrieval-based learning situations are constructed in such a way that ensures high levels of success, the act of bringing the information to mind may be undermined, thus making it less beneficial. For example, if a student reads a sentence and then immediately covers the sentence and recites it out loud, they are likely not retrieving the information but rather just keeping the information in their working memory long enough to recite it again (see Smith, Blunt, et al., 2016 for a discussion of this point). Thus, it is important to balance success of retrieval with overall difficulty in retrieving the information (Smith & Karpicke, 2014 ; Weinstein, Nunes, & Karpicke, 2016 ). If initial retrieval success is low, then feedback can help improve the overall benefit of practicing retrieval (Kang, McDermott, & Roediger, 2007 ; Smith & Karpicke, 2014 ). Kornell, Klein, and Rawson ( 2015 ), however, found that it was the retrieval attempt and not the correct production of information that produced the retrieval practice benefit – as long as the correct answer was provided after an unsuccessful attempt, the benefit was the same as for a successful retrieval attempt in this set of studies. From a practical perspective, it would be helpful for teachers to know when retrieval attempts in the absence of success are helpful, and when they are not. There may also be additional reasons beyond retrieval benefits that would push teachers towards retrieval practice activities that produce some success amongst students; for example, teachers may hesitate to give students retrieval practice exercises that are too difficult, as this may negatively affect self-efficacy and confidence.

In addition to the fact that bringing information to mind directly improves memory for that information, engaging in retrieval practice can produce indirect benefits as well (see Roediger et al., 2011 ). For example, research by Weinstein, Gilmore, Szpunar, and McDermott ( 2014 ) demonstrated that when students expected to be tested, the increased test expectancy led to better-quality encoding of new information. Frequent testing can also serve to decrease mind-wandering – that is, thoughts that are unrelated to the material that students are supposed to be studying (Szpunar, Khan, & Schacter, 2013 ).

Practicing retrieval is a powerful way to improve meaningful learning of information, and it is relatively easy to implement in the classroom. For example, requiring students to practice retrieval can be as simple as asking students to put their class materials away and try to write out everything they know about a topic. Retrieval-based learning strategies are also flexible. Instructors can give students practice tests (e.g., short-answer or multiple-choice, see Smith & Karpicke, 2014 ), provide open-ended prompts for the students to recall information (e.g., Smith, Blunt, et al., 2016 ) or ask their students to create concept maps from memory (e.g., Blunt & Karpicke, 2014 ). In one study, Weinstein et al. ( 2016 ) looked at the effectiveness of inserting simple short-answer questions into online learning modules to see whether they improved student performance. Weinstein and colleagues also manipulated the placement of the questions. For some students, the questions were interspersed throughout the module, and for other students the questions were all presented at the end of the module. Initial success on the short-answer questions was higher when the questions were interspersed throughout the module. However, on a later test of learning from that module, the original placement of the questions in the module did not matter for performance. As with spaced practice, where the optimal gap between study sessions is contingent on the retention interval, the optimum difficulty and level of success during retrieval practice may also depend on the retention interval. Both groups of students who answered questions performed better on the delayed test compared to a control group without question opportunities during the module. Thus, the important thing is for instructors to provide opportunities for retrieval practice during learning. Based on previous research, any activity that promotes the successful retrieval of information should improve learning.

Retrieval practice has received a lot of attention in teacher blogs (see “Learning Scientists” ( 2016b ) for a collection). A common theme seems to be an emphasis on low-stakes (Young, 2016 ) and even no-stakes (Cox, 2015 ) testing, the goal of which is to increase learning rather than assess performance. In fact, one well-known charter school in the UK has an official homework policy grounded in retrieval practice: students are to test themselves on subject knowledge for 30 minutes every day in lieu of standard homework (Michaela Community School, 2014 ). The utility of homework, particularly for younger children, is often a hotly debated topic outside of academia (e.g., Shumaker, 2016 ; but see Jones ( 2016 ) for an opposing viewpoint and Cooper ( 1989 ) for the original research the blog posts were based on). Whereas some research shows clear links between homework and academic achievement (Valle et al., 2016 ), other researchers have questioned the effectiveness of homework (Dettmers, Trautwein, & Lüdtke, 2009 ). Perhaps amending homework to involve retrieval practice might make it more effective; this remains an open empirical question.

One final consideration is that of test anxiety. While retrieval practice can be very powerful at improving memory, some research shows that pressure during retrieval can undermine some of the learning benefit. For example, Hinze and Rapp ( 2014 ) manipulated pressure during quizzing to create high-pressure and low-pressure conditions. On the quizzes themselves, students performed equally well. However, those in the high-pressure condition did not perform as well on a criterion test later compared to the low-pressure group. Thus, test anxiety may reduce the learning benefit of retrieval practice. Eliminating all high-pressure tests is probably not possible, but instructors can provide a number of low-stakes retrieval opportunities for students to help increase learning. The use of low-stakes testing can serve to decrease test anxiety (Khanna, 2015 ), and has recently been shown to negate the detrimental impact of stress on learning (Smith, Floerke, & Thomas, 2016 ). This is a particularly important line of inquiry to pursue for future research, because many teachers who are not familiar with the effectiveness of retrieval practice may be put off by the implied pressure of “testing”, which evokes the much maligned high-stakes standardized tests (e.g., McHugh, 2013 ).

Elaboration

Elaboration involves connecting new information to pre-existing knowledge. Anderson ( 1983 , p.285) made the following claim about elaboration: “One of the most potent manipulations that can be performed in terms of increasing a subject’s memory for material is to have the subject elaborate on the to-be-remembered material.” Postman ( 1976 , p. 28) defined elaboration most parsimoniously as “additions to nominal input”, and Hirshman ( 2001 , p. 4369) provided an elaboration on this definition (pun intended!), defining elaboration as “A conscious, intentional process that associates to-be-remembered information with other information in memory.” However, in practice, elaboration could mean many different things. The common thread in all the definitions is that elaboration involves adding features to an existing memory.

One possible instantiation of elaboration is thinking about information on a deeper level. The levels (or “depth”) of processing framework, proposed by Craik and Lockhart ( 1972 ), predicts that information will be remembered better if it is processed more deeply in terms of meaning, rather than shallowly in terms of form. The leves of processing framework has, however, received a number of criticisms (Craik, 2002 ). One major problem with this framework is that it is difficult to measure “depth”. And if we are not able to actually measure depth, then the argument can become circular: is it that something was remembered better because it was studied more deeply, or do we conclude that it must have been studied more deeply because it is remembered better? (See Lockhart & Craik, 1990 , for further discussion of this issue).

Another mechanism by which elaboration can confer a benefit to learning is via improvement in organization (Bellezza, Cheesman, & Reddy, 1977 ; Mandler, 1979 ). By this view, elaboration involves making information more integrated and organized with existing knowledge structures. By connecting and integrating the to-be-learned information with other concepts in memory, students can increase the extent to which the ideas are organized in their minds, and this increased organization presumably facilitates the reconstruction of the past at the time of retrieval.

Elaboration is such a broad term and can include so many different techniques that it is hard to claim that elaboration will always help learning. There is, however, a specific technique under the umbrella of elaboration for which there is relatively strong evidence in terms of effectiveness (Dunlosky et al., 2013 ; Pashler et al., 2007 ). This technique is called elaborative interrogation, and involves students questioning the materials that they are studying (Pressley, McDaniel, Turnure, Wood, & Ahmad, 1987 ). More specifically, students using this technique would ask “how” and “why” questions about the concepts they are studying (see Fig.  4 for an example on the physics of flight). Then, crucially, students would try to answer these questions – either from their materials or, eventually, from memory (McDaniel & Donnelly, 1996 ). The process of figuring out the answer to the questions – with some amount of uncertainty (Overoye & Storm, 2015 ) – can help learning. When using this technique, however, it is important that students check their answers with their materials or with the teacher; when the content generated through elaborative interrogation is poor, it can actually hurt learning (Clinton, Alibali, & Nathan, 2016 ).

Students can also be encouraged to self-explain concepts to themselves while learning (Chi, De Leeuw, Chiu, & LaVancher, 1994 ). This might involve students simply saying out loud what steps they need to perform to solve an equation. Aleven and Koedinger ( 2002 ) conducted two classroom studies in which students were either prompted by a “cognitive tutor” to provide self-explanations during a problem-solving task or not, and found that the self-explanations led to improved performance. According to the authors, this approach could scale well to real classrooms. If possible and relevant, students could even perform actions alongside their self-explanations (Cohen, 1981 ; see also the enactment effect, Hainselin, Picard, Manolli, Vankerkore-Candas, & Bourdin, 2017 ). Instructors can scaffold students in these types of activities by providing self-explanation prompts throughout to-be-learned material (O’Neil et al., 2014 ). Ultimately, the greatest potential benefit of accurate self-explanation or elaboration is that the student will be able to transfer their knowledge to a new situation (Rittle-Johnson, 2006 ).

The technical term “elaborative interrogation” has not made it into the vernacular of educational bloggers (a search on https://educationechochamberuncut.wordpress.com , which consolidates over 3,000 UK-based teacher blogs, yielded zero results for that term). However, a few teachers have blogged about elaboration more generally (e.g., Hobbiss, 2016 ) and deep questioning specifically (e.g., Class Teaching, 2013 ), just without using the specific terminology. This strategy in particular may benefit from a more open dialog between researchers and teachers to facilitate the use of elaborative interrogation in the classroom and to address possible barriers to implementation. In terms of advancing the scientific understanding of elaborative interrogation in a classroom setting, it would be informative to conduct a larger-scale intervention to see whether having students elaborate during reading actually helps their understanding. It would also be useful to know whether the students really need to generate their own elaborative interrogation (“how” and “why”) questions, versus answering questions provided by others. How long should students persist to find the answers? When is the right time to have students engage in this task, given the levels of expertise required to do it well (Clinton et al., 2016 )? Without knowing the answers to these questions, it may be too early for us to instruct teachers to use this technique in their classes. Finally, elaborative interrogation takes a long time. Is this time efficiently spent? Or, would it be better to have the students try to answer a few questions, pool their information as a class, and then move to practicing retrieval of the information?

Concrete examples

Providing supporting information can improve the learning of key ideas and concepts. Specifically, using concrete examples to supplement content that is more conceptual in nature can make the ideas easier to understand and remember. Concrete examples can provide several advantages to the learning process: (a) they can concisely convey information, (b) they can provide students with more concrete information that is easier to remember, and (c) they can take advantage of the superior memorability of pictures relative to words (see “Dual Coding”).

Words that are more concrete are both recognized and recalled better than abstract words (Gorman, 1961 ; e.g., “button” and “bound,” respectively). Furthermore, it has been demonstrated that information that is more concrete and imageable enhances the learning of associations, even with abstract content (Caplan & Madan, 2016 ; Madan, Glaholt, & Caplan, 2010 ; Paivio, 1971 ). Following from this, providing concrete examples during instruction should improve retention of related abstract concepts, rather than the concrete examples alone being remembered better. Concrete examples can be useful both during instruction and during practice problems. Having students actively explain how two examples are similar and encouraging them to extract the underlying structure on their own can also help with transfer. In a laboratory study, Berry ( 1983 ) demonstrated that students performed well when given concrete practice problems, regardless of the use of verbalization (akin to elaborative interrogation), but that verbalization helped students transfer understanding from concrete to abstract problems. One particularly important area of future research is determining how students can best make the link between concrete examples and abstract ideas.

Since abstract concepts are harder to grasp than concrete information (Paivio, Walsh, & Bons, 1994 ), it follows that teachers ought to illustrate abstract ideas with concrete examples. However, care must be taken when selecting the examples. LeFevre and Dixon ( 1986 ) provided students with both concrete examples and abstract instructions and found that when these were inconsistent, students followed the concrete examples rather than the abstract instructions, potentially constraining the application of the abstract concept being taught. Lew, Fukawa-Connelly, Mejí-Ramos, and Weber ( 2016 ) used an interview approach to examine why students may have difficulty understanding a lecture. Responses indicated that some issues were related to understanding the overarching topic rather than the component parts, and to the use of informal colloquialisms that did not clearly follow from the material being taught. Both of these issues could have potentially been addressed through the inclusion of a greater number of relevant concrete examples.

One concern with using concrete examples is that students might only remember the examples – especially if they are particularly memorable, such as fun or gimmicky examples – and will not be able to transfer their understanding from one example to another, or more broadly to the abstract concept. However, there does not seem to be any evidence that fun relevant examples actually hurt learning by harming memory for important information. Instead, fun examples and jokes tend to be more memorable, but this boost in memory for the joke does not seem to come at a cost to memory for the underlying concept (Baldassari & Kelley, 2012 ). However, two important caveats need to be highlighted. First, to the extent that the more memorable content is not relevant to the concepts of interest, learning of the target information can be compromised (Harp & Mayer, 1998 ). Thus, care must be taken to ensure that all examples and gimmicks are, in fact, related to the core concepts that the students need to acquire, and do not contain irrelevant perceptual features (Kaminski & Sloutsky, 2013 ).

The second issue is that novices often notice and remember the surface details of an example rather than the underlying structure. Experts, on the other hand, can extract the underlying structure from examples that have divergent surface features (Chi, Feltovich, & Glaser, 1981 ; see Fig.  5 for an example from physics). Gick and Holyoak ( 1983 ) tried to get students to apply a rule from one problem to another problem that appeared different on the surface, but was structurally similar. They found that providing multiple examples helped with this transfer process compared to only using one example – especially when the examples provided had different surface details. More work is also needed to determine how many examples are sufficient for generalization to occur (and this, of course, will vary with contextual factors and individual differences). Further research on the continuum between concrete/specific examples and more abstract concepts would also be informative. That is, if an example is not concrete enough, it may be too difficult to understand. On the other hand, if the example is too concrete, that could be detrimental to generalization to the more abstract concept (although a diverse set of very concrete examples may be able to help with this). In fact, in a controversial article, Kaminski, Sloutsky, and Heckler ( 2008 ) claimed that abstract examples were more effective than concrete examples. Later rebuttals of this paper contested whether the abstract versus concrete distinction was clearly defined in the original study (see Reed, 2008 , for a collection of letters on the subject). This ideal point along the concrete-abstract continuum might also interact with development.

Finding teacher blog posts on concrete examples proved to be more difficult than for the other strategies in this review. One optimistic possibility is that teachers frequently use concrete examples in their teaching, and thus do not think of this as a specific contribution from cognitive psychology; the one blog post we were able to find that discussed concrete examples suggests that this might be the case (Boulton, 2016 ). The idea of “linking abstract concepts with concrete examples” is also covered in 25% of teacher-training textbooks used in the US, according to the report by Pomerance et al. ( 2016 ); this is the second most frequently covered of the six strategies, after “posing probing questions” (i.e., elaborative interrogation). A useful direction for future research would be to establish how teachers are using concrete examples in their practice, and whether we can make any suggestions for improvement based on research into the science of learning. For example, if two examples are better than one (Bauernschmidt, 2017 ), are additional examples also needed, or are there diminishing returns from providing more examples? And, how can teachers best ensure that concrete examples are consistent with prior knowledge (Reed, 2008 )?

Dual coding

Both the memory literature and folk psychology support the notion of visual examples being beneficial—the adage of “a picture is worth a thousand words” (traced back to an advertising slogan from the 1920s; Meider, 1990 ). Indeed, it is well-understood that more information can be conveyed through a simple illustration than through several paragraphs of text (e.g., Barker & Manji, 1989 ; Mayer & Gallini, 1990 ). Illustrations can be particularly helpful when the described concept involves several parts or steps and is intended for individuals with low prior knowledge (Eitel & Scheiter, 2015 ; Mayer & Gallini, 1990 ). Figure  6 provides a concrete example of this, illustrating how information can flow through neurons and synapses.

In addition to being able to convey information more succinctly, pictures are also more memorable than words (Paivio & Csapo, 1969 , 1973 ). In the memory literature, this is referred to as the picture superiority effect , and dual coding theory was developed in part to explain this effect. Dual coding follows from the notion of text being accompanied by complementary visual information to enhance learning. Paivio ( 1971 , 1986 ) proposed dual coding theory as a mechanistic account for the integration of multiple information “codes” to process information. In this theory, a code corresponds to a modal or otherwise distinct representation of a concept—e.g., “mental images for ‘book’ have visual, tactual, and other perceptual qualities similar to those evoked by the referent objects on which the images are based” (Clark & Paivio, 1991 , p. 152). Aylwin ( 1990 ) provides a clear example of how the word “dog” can evoke verbal, visual, and enactive representations (see Fig.  7 for a similar example for the word “SPOON”, based on Aylwin, 1990 (Fig.  2 ) and Madan & Singhal, 2012a (Fig.  3 )). Codes can also correspond to emotional properties (Clark & Paivio, 1991 ; Paivio, 2013 ). Clark and Paivio ( 1991 ) provide a thorough review of dual coding theory and its relation to education, while Paivio ( 2007 ) provides a comprehensive treatise on dual coding theory. Broadly, dual coding theory suggests that providing multiple representations of the same information enhances learning and memory, and that information that more readily evokes additional representations (through automatic imagery processes) receives a similar benefit.

Paivio and Csapo ( 1973 ) suggest that verbal and imaginal codes have independent and additive effects on memory recall. Using visuals to improve learning and memory has been particularly applied to vocabulary learning (Danan, 1992 ; Sadoski, 2005 ), but has also shown success in other domains such as in health care (Hartland, Biddle, & Fallacaro, 2008 ). To take advantage of dual coding, verbal information should be accompanied by a visual representation when possible. However, while the studies discussed all indicate that the use of multiple representations of information is favorable, it is important to acknowledge that each representation also increases cognitive load and can lead to over-saturation (Mayer & Moreno, 2003 ).

Given that pictures are generally remembered better than words, it is important to ensure that the pictures students are provided with are helpful and relevant to the content they are expected to learn. McNeill, Uttal, Jarvin, and Sternberg ( 2009 ) found that providing visual examples decreased conceptual errors. However, McNeill et al. also found that when students were given visually rich examples, they performed more poorly than students who were not given any visual example, suggesting that the visual details can at times become a distraction and hinder performance. Thus, it is important to consider that images used in teaching are clear and not ambiguous in their meaning (Schwartz, 2007 ).

Further broadening the scope of dual coding theory, Engelkamp and Zimmer ( 1984 ) suggest that motor movements, such as “turning the handle,” can provide an additional motor code that can improve memory, linking studies of motor actions (enactment) with dual coding theory (Clark & Paivio, 1991 ; Engelkamp & Cohen, 1991 ; Madan & Singhal, 2012c ). Indeed, enactment effects appear to primarily occur during learning, rather than during retrieval (Peterson & Mulligan, 2010 ). Along similar lines, Wammes, Meade, and Fernandes ( 2016 ) demonstrated that generating drawings can provide memory benefits beyond what could otherwise be explained by visual imagery, picture superiority, and other memory enhancing effects. Providing convergent evidence, even when overt motor actions are not critical in themselves, words representing functional objects have been shown to enhance later memory (Madan & Singhal, 2012b ; Montefinese, Ambrosini, Fairfield, & Mammarella, 2013 ). This indicates that motoric processes can improve memory similarly to visual imagery, similar to memory differences for concrete vs. abstract words. Further research suggests that automatic motor simulation for functional objects is likely responsible for this memory benefit (Madan, Chen, & Singhal, 2016 ).

When teachers combine visuals and words in their educational practice, however, they may not always be taking advantage of dual coding – at least, not in the optimal manner. For example, a recent discussion on Twitter centered around one teacher’s decision to have 7 th Grade students replace certain words in their science laboratory report with a picture of that word (e.g., the instructions read “using a syringe …” and a picture of a syringe replaced the word; Turner, 2016a ). Other teachers argued that this was not dual coding (Beaven, 2016 ; Williams, 2016 ), because there were no longer two different representations of the information. The first teacher maintained that dual coding was preserved, because this laboratory report with pictures was to be used alongside the original, fully verbal report (Turner, 2016b ). This particular implementation – having students replace individual words with pictures – has not been examined in the cognitive literature, presumably because no benefit would be expected. In any case, we need to be clearer about implementations for dual coding, and more research is needed to clarify how teachers can make use of the benefits conferred by multiple representations and picture superiority.

Critically, dual coding theory is distinct from the notion of “learning styles,” which describe the idea that individuals benefit from instruction that matches their modality preference. While this idea is pervasive and individuals often subjectively feel that they have a preference, evidence indicates that the learning styles theory is not supported by empirical findings (e.g., Kavale, Hirshoren, & Forness, 1998 ; Pashler, McDaniel, Rohrer, & Bjork, 2008 ; Rohrer & Pashler, 2012 ). That is, there is no evidence that instructing students in their preferred learning style leads to an overall improvement in learning (the “meshing” hypothesis). Moreover, learning styles have come to be described as a myth or urban legend within psychology (Coffield, Moseley, Hall, & Ecclestone, 2004 ; Hattie & Yates, 2014 ; Kirschner & van Merriënboer, 2013 ; Kirschner, 2017 ); skepticism about learning styles is a common stance amongst evidence-informed teachers (e.g., Saunders, 2016 ). Providing evidence against the notion of learning styles, Kraemer, Rosenberg, and Thompson-Schill ( 2009 ) found that individuals who scored as “verbalizers” and “visualizers” did not perform any better on experimental trials matching their preference. Instead, it has recently been shown that learning through one’s preferred learning style is associated with elevated subjective judgements of learning, but not objective performance (Knoll, Otani, Skeel, & Van Horn, 2017 ). In contrast to learning styles, dual coding is based on providing additional, complementary forms of information to enhance learning, rather than tailoring instruction to individuals’ preferences.

Genuine educational environments present many opportunities for combining the strategies outlined above. Spacing can be particularly potent for learning if it is combined with retrieval practice. The additive benefits of retrieval practice and spacing can be gained by engaging in retrieval practice multiple times (also known as distributed practice; see Cepeda et al., 2006 ). Interleaving naturally entails spacing if students interleave old and new material. Concrete examples can be both verbal and visual, making use of dual coding. In addition, the strategies of elaboration, concrete examples, and dual coding all work best when used as part of retrieval practice. For example, in the concept-mapping studies mentioned above (Blunt & Karpicke, 2014 ; Karpicke, Blunt, et al., 2014 ), creating concept maps while looking at course materials (e.g., a textbook) was not as effective for later memory as creating concept maps from memory. When practicing elaborative interrogation, students can start off answering the “how” and “why” questions they pose for themselves using class materials, and work their way up to answering them from memory. And when interleaving different problem types, students should be practicing answering them rather than just looking over worked examples.

But while these ideas for strategy combinations have empirical bases, it has not yet been established whether the benefits of the strategies to learning are additive, super-additive, or, in some cases, incompatible. Thus, future research needs to (a) better formalize the definition of each strategy (particularly critical for elaboration and dual coding), (b) identify best practices for implementation in the classroom, (c) delineate the boundary conditions of each strategy, and (d) strategically investigate interactions between the six strategies we outlined in this manuscript.

Aleven, V. A., & Koedinger, K. R. (2002). An effective metacognitive strategy: learning by doing and explaining with a computer-based cognitive tutor. Cognitive Science, 26 , 147–179.

Article   Google Scholar  

Anderson, J. R. (1983). A spreading activation theory of memory. Journal of Verbal Learning and Verbal Behavior, 22 , 261–295.

Arnold, K. M., & McDermott, K. B. (2013). Test-potentiated learning: distinguishing between direct and indirect effects of tests. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39 , 940–945.

PubMed   Google Scholar  

Aylwin, S. (1990). Imagery and affect: big questions, little answers. In P. J. Thompson, D. E. Marks, & J. T. E. Richardson (Eds.), Imagery: Current developments . New York: International Library of Psychology.

Google Scholar  

Baldassari, M. J., & Kelley, M. (2012). Make’em laugh? The mnemonic effect of humor in a speech. Psi Chi Journal of Psychological Research, 17 , 2–9.

Barker, P. G., & Manji, K. A. (1989). Pictorial dialogue methods. International Journal of Man-Machine Studies, 31 , 323–347.

Bauernschmidt, A. (2017). GUEST POST: two examples are better than one. [Blog post]. The Learning Scientists Blog . Retrieved from http://www.learningscientists.org/blog/2017/5/30-1 . Accessed 25 Dec 2017.

Beaven, T. (2016). @doctorwhy @FurtherEdagogy @doc_kristy Right, I thought the whole point of dual coding was to use TWO codes: pics + words of the SAME info? [Tweet]. Retrieved from https://twitter.com/TitaBeaven/status/807504041341308929 . Accessed 25 Dec 2017.

Bellezza, F. S., Cheesman, F. L., & Reddy, B. G. (1977). Organization and semantic elaboration in free recall. Journal of Experimental Psychology: Human Learning and Memory, 3 , 539–550.

Benney, D. (2016). (Trying to apply) spacing in a content heavy subject [Blog post]. Retrieved from https://mrbenney.wordpress.com/2016/10/16/trying-to-apply-spacing-in-science/ . Accessed 25 Dec 2017.

Berry, D. C. (1983). Metacognitive experience and transfer of logical reasoning. Quarterly Journal of Experimental Psychology, 35A , 39–49.

Birnbaum, M. S., Kornell, N., Bjork, E. L., & Bjork, R. A. (2013). Why interleaving enhances inductive learning: the roles of discrimination and retrieval. Memory & Cognition, 41 , 392–402.

Bjork, R. A. (1999). Assessing our own competence: heuristics and illusions. In D. Gopher & A. Koriat (Eds.), Attention and peformance XVII. Cognitive regulation of performance: Interaction of theory and application (pp. 435–459). Cambridge, MA: MIT Press.

Bjork, R. A. (1994). Memory and metamemory considerations in the training of human beings. In J. Metcalfe & A. Shimamura (Eds.), Metacognition: Knowing about knowing (pp. 185–205). Cambridge, MA: MIT Press.

Bjork, R. A., & Bjork, E. L. (1992). A new theory of disuse and an old theory of stimulus fluctuation. From learning processes to cognitive processes: Essays in honor of William K. Estes, 2 , 35–67.

Bjork, E. L., & Bjork, R. A. (2011). Making things hard on yourself, but in a good way: creating desirable difficulties to enhance learning. Psychology and the real world: Essays illustrating fundamental contributions to society , 56–64.

Blunt, J. R., & Karpicke, J. D. (2014). Learning with retrieval-based concept mapping. Journal of Educational Psychology, 106 , 849–858.

Boulton, K. (2016). What does cognitive overload look like in the humanities? [Blog post]. Retrieved from https://educationechochamberuncut.wordpress.com/2016/03/05/what-does-cognitive-overload-look-like-in-the-humanities-kris-boulton-2/ . Accessed 25 Dec 2017.

Brown, P. C., Roediger, H. L., & McDaniel, M. A. (2014). Make it stick . Cambridge, MA: Harvard University Press.

Book   Google Scholar  

Butler, A. C. (2010). Repeated testing produces superior transfer of learning relative to repeated studying. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36 , 1118–1133.

Caplan, J. B., & Madan, C. R. (2016). Word-imageability enhances association-memory by recruiting hippocampal activity. Journal of Cognitive Neuroscience, 28 , 1522–1538.

Article   PubMed   Google Scholar  

Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: a review and quantitative synthesis. Psychological Bulletin, 132 , 354–380.

Cepeda, N. J., Vul, E., Rohrer, D., Wixted, J. T., & Pashler, H. (2008). Spacing effects in learning a temporal ridgeline of optimal retention. Psychological Science, 19 , 1095–1102.

Chi, M. T., De Leeuw, N., Chiu, M. H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18 , 439–477.

Chi, M. T., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5 , 121–152.

CIFE. (2012). No January A level and other changes. Retrieved from http://www.cife.org.uk/cife-general-news/no-january-a-level-and-other-changes/ . Accessed 25 Dec 2017.

Clark, D. (2016). One book on learning that every teacher, lecturer & trainer should read (7 reasons) [Blog post]. Retrieved from http://donaldclarkplanb.blogspot.com/2016/03/one-book-on-learning-that-every-teacher.html . Accessed 25 Dec 2017.

Clark, J. M., & Paivio, A. (1991). Dual coding theory and education. Educational Psychology Review, 3 , 149–210.

Class Teaching. (2013). Deep questioning [Blog post]. Retrieved from https://classteaching.wordpress.com/2013/07/12/deep-questioning/ . Accessed 25 Dec 2017.

Clinton, V., Alibali, M. W., & Nathan, M. J. (2016). Learning about posterior probability: do diagrams and elaborative interrogation help? The Journal of Experimental Education, 84 , 579–599.

Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Learning styles and pedagogy in post-16 learning: a systematic and critical review . London: Learning & Skills Research Centre.

Cohen, R. L. (1981). On the generality of some memory laws. Scandinavian Journal of Psychology, 22 , 267–281.

Cooper, H. (1989). Synthesis of research on homework. Educational Leadership, 47 , 85–91.

Corbett, A. T., Reed, S. K., Hoffmann, R., MacLaren, B., & Wagner, A. (2010). Interleaving worked examples and cognitive tutor support for algebraic modeling of problem situations. In Proceedings of the Thirty-Second Annual Meeting of the Cognitive Science Society (pp. 2882–2887).

Cox, D. (2015). No stakes testing – not telling students their results [Blog post]. Retrieved from https://missdcoxblog.wordpress.com/2015/06/06/no-stakes-testing-not-telling-students-their-results/ . Accessed 25 Dec 2017.

Cox, D. (2016a). Ditch revision. Teach it well [Blog post]. Retrieved from https://missdcoxblog.wordpress.com/2016/01/09/ditch-revision-teach-it-well/ . Accessed 25 Dec 2017.

Cox, D. (2016b). ‘They need to remember this in three years time’: spacing & interleaving for the new GCSEs [Blog post]. Retrieved from https://missdcoxblog.wordpress.com/2016/03/25/they-need-to-remember-this-in-three-years-time-spacing-interleaving-for-the-new-gcses/ . Accessed 25 Dec 2017.

Craik, F. I. (2002). Levels of processing: past, present… future? Memory, 10 , 305–318.

Craik, F. I., & Lockhart, R. S. (1972). Levels of processing: a framework for memory research. Journal of Verbal Learning and Verbal Behavior, 11 , 671–684.

Danan, M. (1992). Reversed subtitling and dual coding theory: new directions for foreign language instruction. Language Learning, 42 , 497–527.

Dettmers, S., Trautwein, U., & Lüdtke, O. (2009). The relationship between homework time and achievement is not universal: evidence from multilevel analyses in 40 countries. School Effectiveness and School Improvement, 20 , 375–405.

Dirkx, K. J., Kester, L., & Kirschner, P. A. (2014). The testing effect for learning principles and procedures from texts. The Journal of Educational Research, 107 , 357–364.

Dunlosky, J. (2013). Strengthening the student toolbox: study strategies to boost learning. American Educator, 37 (3), 12–21.

Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14 , 4–58.

Ebbinghaus, H. (1913). Memory (HA Ruger & CE Bussenius, Trans.). New York: Columbia University, Teachers College. (Original work published 1885) . Retrieved from http://psychclassics.yorku.ca/Ebbinghaus/memory8.htm . Accessed 25 Dec 2017.

Eglington, L. G., & Kang, S. H. (2016). Retrieval practice benefits deductive inference. Educational Psychology Review , 1–14.

Eitel, A., & Scheiter, K. (2015). Picture or text first? Explaining sequential effects when learning with pictures and text. Educational Psychology Review, 27 , 153–180.

Engelkamp, J., & Cohen, R. L. (1991). Current issues in memory of action events. Psychological Research, 53 , 175–182.

Engelkamp, J., & Zimmer, H. D. (1984). Motor programme information as a separable memory unit. Psychological Research, 46 , 283–299.

Fawcett, D. (2013). Can I be that little better at……using cognitive science/psychology/neurology to plan learning? [Blog post]. Retrieved from http://reflectionsofmyteaching.blogspot.com/2013/09/can-i-be-that-little-better-atusing.html . Accessed 25 Dec 2017.

Fiechter, J. L., & Benjamin, A. S. (2017). Diminishing-cues retrieval practice: a memory-enhancing technique that works when regular testing doesn’t. Psychonomic Bulletin & Review , 1–9.

Firth, J. (2016). Spacing in teaching practice [Blog post]. Retrieved from http://www.learningscientists.org/blog/2016/4/12-1 . Accessed 25 Dec 2017.

Fordham, M. [mfordhamhistory]. (2016). Is there a meaningful distinction in psychology between ‘thinking’ & ‘critical thinking’? [Tweet]. Retrieved from https://twitter.com/mfordhamhistory/status/809525713623781377 . Accessed 25 Dec 2017.

Fritz, C. O., Morris, P. E., Nolan, D., & Singleton, J. (2007). Expanding retrieval practice: an effective aid to preschool children’s learning. The Quarterly Journal of Experimental Psychology, 60 , 991–1004.

Gates, A. I. (1917). Recitation as a factory in memorizing. Archives of Psychology, 6.

Gick, M. L., & Holyoak, K. J. (1983). Schema induction and analogical transfer. Cognitive Psychology, 15 , 1–38.

Gorman, A. M. (1961). Recognition memory for nouns as a function of abstractedness and frequency. Journal of Experimental Psychology, 61 , 23–39.

Hainselin, M., Picard, L., Manolli, P., Vankerkore-Candas, S., & Bourdin, B. (2017). Hey teacher, don’t leave them kids alone: action is better for memory than reading. Frontiers in Psychology , 8 .

Harp, S. F., & Mayer, R. E. (1998). How seductive details do their damage. Journal of Educational Psychology, 90 , 414–434.

Hartland, W., Biddle, C., & Fallacaro, M. (2008). Audiovisual facilitation of clinical knowledge: A paradigm for dispersed student education based on Paivio’s dual coding theory. AANA Journal, 76 , 194–198.

Hattie, J., & Yates, G. (2014). Visible learning and the science of how we learn . New York: Routledge.

Hausman, H., & Kornell, N. (2014). Mixing topics while studying does not enhance learning. Journal of Applied Research in Memory and Cognition, 3 , 153–160.

Hinze, S. R., & Rapp, D. N. (2014). Retrieval (sometimes) enhances learning: performance pressure reduces the benefits of retrieval practice. Applied Cognitive Psychology, 28 , 597–606.

Hirshman, E. (2001). Elaboration in memory. In N. J. Smelser & P. B. Baltes (Eds.), International encyclopedia of the social & behavioral sciences (pp. 4369–4374). Oxford: Pergamon.

Chapter   Google Scholar  

Hobbiss, M. (2016). Make it meaningful! Elaboration [Blog post]. Retrieved from https://hobbolog.wordpress.com/2016/06/09/make-it-meaningful-elaboration/ . Accessed 25 Dec 2017.

Jones, F. (2016). Homework – is it really that useless? [Blog post]. Retrieved from http://www.learningscientists.org/blog/2016/4/5-1 . Accessed 25 Dec 2017.

Kaminski, J. A., & Sloutsky, V. M. (2013). Extraneous perceptual information interferes with children’s acquisition of mathematical knowledge. Journal of Educational Psychology, 105 (2), 351–363.

Kaminski, J. A., Sloutsky, V. M., & Heckler, A. F. (2008). The advantage of abstract examples in learning math. Science, 320 , 454–455.

Kang, S. H. (2016). Spaced repetition promotes efficient and effective learning policy implications for instruction. Policy Insights from the Behavioral and Brain Sciences, 3 , 12–19.

Kang, S. H. K., McDermott, K. B., & Roediger, H. L. (2007). Test format and corrective feedback modify the effects of testing on long-term retention. European Journal of Cognitive Psychology, 19 , 528–558.

Karpicke, J. D., & Aue, W. R. (2015). The testing effect is alive and well with complex materials. Educational Psychology Review, 27 , 317–326.

Karpicke, J. D., Blunt, J. R., Smith, M. A., & Karpicke, S. S. (2014). Retrieval-based learning: The need for guided retrieval in elementary school children. Journal of Applied Research in Memory and Cognition, 3 , 198–206.

Karpicke, J. D., Lehman, M., & Aue, W. R. (2014). Retrieval-based learning: an episodic context account. In B. H. Ross (Ed.), Psychology of Learning and Motivation (Vol. 61, pp. 237–284). San Diego, CA: Elsevier Academic Press.

Karpicke, J. D., Blunt, J. R., & Smith, M. A. (2016). Retrieval-based learning: positive effects of retrieval practice in elementary school children. Frontiers in Psychology, 7 .

Kavale, K. A., Hirshoren, A., & Forness, S. R. (1998). Meta-analytic validation of the Dunn and Dunn model of learning-style preferences: a critique of what was Dunn. Learning Disabilities Research & Practice, 13 , 75–80.

Khanna, M. M. (2015). Ungraded pop quizzes: test-enhanced learning without all the anxiety. Teaching of Psychology, 42 , 174–178.

Kirby, J. (2014). One scientific insight for curriculum design [Blog post]. Retrieved from https://pragmaticreform.wordpress.com/2014/05/05/scientificcurriculumdesign/ . Accessed 25 Dec 2017.

Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers & Education, 106 , 166–171.

Kirschner, P. A., & van Merriënboer, J. J. G. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48 , 169–183.

Knoll, A. R., Otani, H., Skeel, R. L., & Van Horn, K. R. (2017). Learning style, judgments of learning, and learning of verbal and visual information. British Journal of Psychology, 108 , 544-563.

Kornell, N., & Bjork, R. A. (2008). Learning concepts and categories is spacing the “enemy of induction”? Psychological Science, 19 , 585–592.

Kornell, N., & Finn, B. (2016). Self-regulated learning: an overview of theory and data. In J. Dunlosky & S. Tauber (Eds.), The Oxford Handbook of Metamemory (pp. 325–340). New York: Oxford University Press.

Kornell, N., Klein, P. J., & Rawson, K. A. (2015). Retrieval attempts enhance learning, but retrieval success (versus failure) does not matter. Journal of Experimental Psychology: Learning, Memory, and Cognition, 41 , 283–294.

Kraemer, D. J. M., Rosenberg, L. M., & Thompson-Schill, S. L. (2009). The neural correlates of visual and verbal cognitive styles. Journal of Neuroscience, 29 , 3792–3798.

Article   PubMed   PubMed Central   Google Scholar  

Kraft, N. (2015). Spaced practice and repercussions for teaching. Retrieved from http://nathankraft.blogspot.com/2015/08/spaced-practice-and-repercussions-for.html . Accessed 25 Dec 2017.

Learning Scientists. (2016a). Weekly Digest #3: How teachers implement interleaving in their curriculum [Blog post]. Retrieved from http://www.learningscientists.org/blog/2016/3/28/weekly-digest-3 . Accessed 25 Dec 2017.

Learning Scientists. (2016b). Weekly Digest #13: how teachers implement retrieval in their classrooms [Blog post]. Retrieved from http://www.learningscientists.org/blog/2016/6/5/weekly-digest-13 . Accessed 25 Dec 2017.

Learning Scientists. (2016c). Weekly Digest #40: teachers’ implementation of principles from “Make It Stick” [Blog post]. Retrieved from http://www.learningscientists.org/blog/2016/12/18-1 . Accessed 25 Dec 2017.

Learning Scientists. (2017). Weekly Digest #54: is there an app for that? Studying 2.0 [Blog post]. Retrieved from http://www.learningscientists.org/blog/2017/4/9/weekly-digest-54 . Accessed 25 Dec 2017.

LeFevre, J.-A., & Dixon, P. (1986). Do written instructions need examples? Cognition and Instruction, 3 , 1–30.

Lew, K., Fukawa-Connelly, T., Mejí-Ramos, J. P., & Weber, K. (2016). Lectures in advanced mathematics: Why students might not understand what the mathematics professor is trying to convey. Journal of Research in Mathematics Education, 47 , 162–198.

Lindsey, R. V., Shroyer, J. D., Pashler, H., & Mozer, M. C. (2014). Improving students’ long-term knowledge retention through personalized review. Psychological Science, 25 , 639–647.

Lipko-Speed, A., Dunlosky, J., & Rawson, K. A. (2014). Does testing with feedback help grade-school children learn key concepts in science? Journal of Applied Research in Memory and Cognition, 3 , 171–176.

Lockhart, R. S., & Craik, F. I. (1990). Levels of processing: a retrospective commentary on a framework for memory research. Canadian Journal of Psychology, 44 , 87–112.

Lovell, O. (2017). How do we know what to put on the quiz? [Blog Post]. Retrieved from http://www.ollielovell.com/olliesclassroom/know-put-quiz/ . Accessed 25 Dec 2017.

Luehmann, A. L. (2008). Using blogging in support of teacher professional identity development: a case study. The Journal of the Learning Sciences, 17 , 287–337.

Madan, C. R., Glaholt, M. G., & Caplan, J. B. (2010). The influence of item properties on association-memory. Journal of Memory and Language, 63 , 46–63.

Madan, C. R., & Singhal, A. (2012a). Motor imagery and higher-level cognition: four hurdles before research can sprint forward. Cognitive Processing, 13 , 211–229.

Madan, C. R., & Singhal, A. (2012b). Encoding the world around us: motor-related processing influences verbal memory. Consciousness and Cognition, 21 , 1563–1570.

Madan, C. R., & Singhal, A. (2012c). Using actions to enhance memory: effects of enactment, gestures, and exercise on human memory. Frontiers in Psychology, 3 .

Madan, C. R., Chen, Y. Y., & Singhal, A. (2016). ERPs differentially reflect automatic and deliberate processing of the functional manipulability of objects. Frontiers in Human Neuroscience, 10 .

Mandler, G. (1979). Organization and repetition: organizational principles with special reference to rote learning. In L. G. Nilsson (Ed.), Perspectives on Memory Research (pp. 293–327). New York: Academic Press.

Marsh, E. J., Fazio, L. K., & Goswick, A. E. (2012). Memorial consequences of testing school-aged children. Memory, 20 , 899–906.

Mayer, R. E., & Gallini, J. K. (1990). When is an illustration worth ten thousand words? Journal of Educational Psychology, 82 , 715–726.

Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38 , 43–52.

McDaniel, M. A., & Donnelly, C. M. (1996). Learning with analogy and elaborative interrogation. Journal of Educational Psychology, 88 , 508–519.

McDaniel, M. A., Thomas, R. C., Agarwal, P. K., McDermott, K. B., & Roediger, H. L. (2013). Quizzing in middle-school science: successful transfer performance on classroom exams. Applied Cognitive Psychology, 27 , 360–372.

McDermott, K. B., Agarwal, P. K., D’Antonio, L., Roediger, H. L., & McDaniel, M. A. (2014). Both multiple-choice and short-answer quizzes enhance later exam performance in middle and high school classes. Journal of Experimental Psychology: Applied, 20 , 3–21.

McHugh, A. (2013). High-stakes tests: bad for students, teachers, and education in general [Blog post]. Retrieved from https://teacherbiz.wordpress.com/2013/07/01/high-stakes-tests-bad-for-students-teachers-and-education-in-general/ . Accessed 25 Dec 2017.

McNeill, N. M., Uttal, D. H., Jarvin, L., & Sternberg, R. J. (2009). Should you show me the money? Concrete objects both hurt and help performance on mathematics problems. Learning and Instruction, 19 , 171–184.

Meider, W. (1990). “A picture is worth a thousand words”: from advertising slogan to American proverb. Southern Folklore, 47 , 207–225.

Michaela Community School. (2014). Homework. Retrieved from http://mcsbrent.co.uk/homework-2/ . Accessed 25 Dec 2017.

Montefinese, M., Ambrosini, E., Fairfield, B., & Mammarella, N. (2013). The “subjective” pupil old/new effect: is the truth plain to see? International Journal of Psychophysiology, 89 , 48–56.

O’Neil, H. F., Chung, G. K., Kerr, D., Vendlinski, T. P., Buschang, R. E., & Mayer, R. E. (2014). Adding self-explanation prompts to an educational computer game. Computers In Human Behavior, 30 , 23–28.

Overoye, A. L., & Storm, B. C. (2015). Harnessing the power of uncertainty to enhance learning. Translational Issues in Psychological Science, 1 , 140–148.

Paivio, A. (1971). Imagery and verbal processes . New York: Holt, Rinehart and Winston.

Paivio, A. (1986). Mental representations: a dual coding approach . New York: Oxford University Press.

Paivio, A. (2007). Mind and its evolution: a dual coding theoretical approach . Mahwah: Erlbaum.

Paivio, A. (2013). Dual coding theory, word abstractness, and emotion: a critical review of Kousta et al. (2011). Journal of Experimental Psychology: General, 142 , 282–287.

Paivio, A., & Csapo, K. (1969). Concrete image and verbal memory codes. Journal of Experimental Psychology, 80 , 279–285.

Paivio, A., & Csapo, K. (1973). Picture superiority in free recall: imagery or dual coding? Cognitive Psychology, 5 , 176–206.

Paivio, A., Walsh, M., & Bons, T. (1994). Concreteness effects on memory: when and why? Journal of Experimental Psychology: Learning, Memory, and Cognition, 20 , 1196–1204.

Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: concepts and evidence. Psychological Science in the Public Interest, 9 , 105–119.

Pashler, H., Bain, P. M., Bottge, B. A., Graesser, A., Koedinger, K., McDaniel, M., & Metcalfe, J. (2007). Organizing instruction and study to improve student learning. IES practice guide. NCER 2007–2004. National Center for Education Research .

Patel, R., Liu, R., & Koedinger, K. (2016). When to block versus interleave practice? Evidence against teaching fraction addition before fraction multiplication. In Proceedings of the 38th Annual Meeting of the Cognitive Science Society, Philadelphia, PA .

Penfound, B. (2017). Journey to interleaved practice #2 [Blog Post]. Retrieved from https://fullstackcalculus.com/2017/02/03/journey-to-interleaved-practice-2/ . Accessed 25 Dec 2017.

Penfound, B. [BryanPenfound]. (2016). Does blocked practice/learning lessen cognitive load? Does interleaved practice/learning provide productive struggle? [Tweet]. Retrieved from https://twitter.com/BryanPenfound/status/808759362244087808 . Accessed 25 Dec 2017.

Peterson, D. J., & Mulligan, N. W. (2010). Enactment and retrieval. Memory & Cognition, 38 , 233–243.

Picciotto, H. (2009). Lagging homework [Blog post]. Retrieved from http://blog.mathedpage.org/2013/06/lagging-homework.html . Accessed 25 Dec 2017.

Pomerance, L., Greenberg, J., & Walsh, K. (2016). Learning about learning: what every teacher needs to know. Retrieved from http://www.nctq.org/dmsView/Learning_About_Learning_Report . Accessed 25 Dec 2017.

Postman, L. (1976). Methodology of human learning. In W. K. Estes (Ed.), Handbook of learning and cognitive processes (Vol. 3). Hillsdale: Erlbaum.

Pressley, M., McDaniel, M. A., Turnure, J. E., Wood, E., & Ahmad, M. (1987). Generation and precision of elaboration: effects on intentional and incidental learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13 , 291–300.

Reed, S. K. (2008). Concrete examples must jibe with experience. Science, 322 , 1632–1633.

researchED. (2013). How it all began. Retrieved from http://www.researched.org.uk/about/our-story/ . Accessed 25 Dec 2017.

Ritchie, S. J., Della Sala, S., & McIntosh, R. D. (2013). Retrieval practice, with or without mind mapping, boosts fact learning in primary school children. PLoS One, 8 (11), e78976.

Rittle-Johnson, B. (2006). Promoting transfer: effects of self-explanation and direct instruction. Child Development, 77 , 1–15.

Roediger, H. L. (1985). Remembering Ebbinghaus. [Retrospective review of the book On Memory , by H. Ebbinghaus]. Contemporary Psychology, 30 , 519–523.

Roediger, H. L. (2013). Applying cognitive psychology to education translational educational science. Psychological Science in the Public Interest, 14 , 1–3.

Roediger, H. L., & Karpicke, J. D. (2006). The power of testing memory: basic research and implications for educational practice. Perspectives on Psychological Science, 1 , 181–210.

Roediger, H. L., Putnam, A. L., & Smith, M. A. (2011). Ten benefits of testing and their applications to educational practice. In J. Mester & B. Ross (Eds.), The psychology of learning and motivation: cognition in education (pp. 1–36). Oxford: Elsevier.

Roediger, H. L., Finn, B., & Weinstein, Y. (2012). Applications of cognitive science to education. In Della Sala, S., & Anderson, M. (Eds.), Neuroscience in education: the good, the bad, and the ugly . Oxford, UK: Oxford University Press.

Roelle, J., & Berthold, K. (2017). Effects of incorporating retrieval into learning tasks: the complexity of the tasks matters. Learning and Instruction, 49 , 142–156.

Rohrer, D. (2012). Interleaving helps students distinguish among similar concepts. Educational Psychology Review, 24(3), 355–367.

Rohrer, D., Dedrick, R. F., & Stershic, S. (2015). Interleaved practice improves mathematics learning. Journal of Educational Psychology, 107 , 900–908.

Rohrer, D., & Pashler, H. (2012). Learning styles: Where’s the evidence? Medical Education, 46 , 34–35.

Rohrer, D., & Taylor, K. (2007). The shuffling of mathematics problems improves learning. Instructional Science, 35 , 481–498.

Rose, N. (2014). Improving the effectiveness of homework [Blog post]. Retrieved from https://evidenceintopractice.wordpress.com/2014/03/20/improving-the-effectiveness-of-homework/ . Accessed 25 Dec 2017.

Sadoski, M. (2005). A dual coding view of vocabulary learning. Reading & Writing Quarterly, 21 , 221–238.

Saunders, K. (2016). It really is time we stopped talking about learning styles [Blog post]. Retrieved from http://martingsaunders.com/2016/10/it-really-is-time-we-stopped-talking-about-learning-styles/ . Accessed 25 Dec 2017.

Schwartz, D. (2007). If a picture is worth a thousand words, why are you reading this essay? Social Psychology Quarterly, 70 , 319–321.

Shumaker, H. (2016). Homework is wrecking our kids: the research is clear, let’s ban elementary homework. Salon. Retrieved from http://www.salon.com/2016/03/05/homework_is_wrecking_our_kids_the_research_is_clear_lets_ban_elementary_homework . Accessed 25 Dec 2017.

Smith, A. M., Floerke, V. A., & Thomas, A. K. (2016). Retrieval practice protects memory against acute stress. Science, 354 , 1046–1048.

Smith, M. A., Blunt, J. R., Whiffen, J. W., & Karpicke, J. D. (2016). Does providing prompts during retrieval practice improve learning? Applied Cognitive Psychology, 30 , 784–802.

Smith, M. A., & Karpicke, J. D. (2014). Retrieval practice with short-answer, multiple-choice, and hybrid formats. Memory, 22 , 784–802.

Smith, M. A., Roediger, H. L., & Karpicke, J. D. (2013). Covert retrieval practice benefits retention as much as overt retrieval practice. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39 , 1712–1725.

Son, J. Y., & Rivas, M. J. (2016). Designing clicker questions to stimulate transfer. Scholarship of Teaching and Learning in Psychology, 2 , 193–207.

Szpunar, K. K., Khan, N. Y., & Schacter, D. L. (2013). Interpolated memory tests reduce mind wandering and improve learning of online lectures. Proceedings of the National Academy of Sciences, 110 , 6313–6317.

Thomson, R., & Mehring, J. (2016). Better vocabulary study strategies for long-term learning. Kwansei Gakuin University Humanities Review, 20 , 133–141.

Trafton, J. G., & Reiser, B. J. (1993). Studying examples and solving problems: contributions to skill acquisition . Technical report, Naval HCI Research Lab, Washington, DC, USA.

Tran, R., Rohrer, D., & Pashler, H. (2015). Retrieval practice: the lack of transfer to deductive inferences. Psychonomic Bulletin & Review, 22 , 135–140.

Turner, K. [doc_kristy]. (2016a). My dual coding (in red) and some y8 work @AceThatTest they really enjoyed practising the technique [Tweet]. Retrieved from https://twitter.com/doc_kristy/status/807220355395977216 . Accessed 25 Dec 2017.

Turner, K. [doc_kristy]. (2016b). @FurtherEdagogy @doctorwhy their work is revision work, they already have the words on a different page, to compliment not replace [Tweet]. Retrieved from https://twitter.com/doc_kristy/status/807360265100599301 . Accessed 25 Dec 2017.

Valle, A., Regueiro, B., Núñez, J. C., Rodríguez, S., Piñeiro, I., & Rosário, P. (2016). Academic goals, student homework engagement, and academic achievement in elementary school. Frontiers in Psychology, 7 .

Van Gog, T., & Sweller, J. (2015). Not new, but nearly forgotten: the testing effect decreases or even disappears as the complexity of learning materials increases. Educational Psychology Review, 27 , 247–264.

Wammes, J. D., Meade, M. E., & Fernandes, M. A. (2016). The drawing effect: evidence for reliable and robust memory benefits in free recall. Quarterly Journal of Experimental Psychology, 69 , 1752–1776.

Weinstein, Y., Gilmore, A. W., Szpunar, K. K., & McDermott, K. B. (2014). The role of test expectancy in the build-up of proactive interference in long-term memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40 , 1039–1048.

Weinstein, Y., Nunes, L. D., & Karpicke, J. D. (2016). On the placement of practice questions during study. Journal of Experimental Psychology: Applied, 22 , 72–84.

Weinstein, Y., & Weinstein-Jones, F. (2017). Topic and quiz spacing spreadsheet: a planning tool for teachers [Blog Post]. Retrieved from http://www.learningscientists.org/blog/2017/5/11-1 . Accessed 25 Dec 2017.

Weinstein-Jones, F., & Weinstein, Y. (2017). Topic spacing spreadsheet for teachers [Excel macro]. Zenodo. http://doi.org/10.5281/zenodo.573764 . Accessed 25 Dec 2017.

Williams, D. [FurtherEdagogy]. (2016). @doctorwhy @doc_kristy word accompanying the visual? I’m unclear how removing words benefit? Would a flow chart better suit a scientific exp? [Tweet]. Retrieved from https://twitter.com/FurtherEdagogy/status/807356800509104128 . Accessed 25 Dec 2017.

Wood, B. (2017). And now for something a little bit different….[Blog post]. Retrieved from https://justateacherstandinginfrontofaclass.wordpress.com/2017/04/20/and-now-for-something-a-little-bit-different/ . Accessed 25 Dec 2017.

Wooldridge, C. L., Bugg, J. M., McDaniel, M. A., & Liu, Y. (2014). The testing effect with authentic educational materials: a cautionary note. Journal of Applied Research in Memory and Cognition, 3 , 214–221.

Young, C. (2016). Mini-tests. Retrieved from https://colleenyoung.wordpress.com/revision-activities/mini-tests/ . Accessed 25 Dec 2017.

Download references

Acknowledgements

Not applicable.

YW and MAS were partially supported by a grant from The IDEA Center.

Availability of data and materials

Author information, authors and affiliations.

Department of Psychology, University of Massachusetts Lowell, Lowell, MA, USA

Yana Weinstein

Department of Psychology, Boston College, Chestnut Hill, MA, USA

Christopher R. Madan

School of Psychology, University of Nottingham, Nottingham, UK

Department of Psychology, Rhode Island College, Providence, RI, USA

Megan A. Sumeracki

You can also search for this author in PubMed   Google Scholar

Contributions

YW took the lead on writing the “Spaced practice”, “Interleaving”, and “Elaboration” sections. CRM took the lead on writing the “Concrete examples” and “Dual coding” sections. MAS took the lead on writing the “Retrieval practice” section. All authors edited each others’ sections. All authors were involved in the conception and writing of the manuscript. All authors gave approval of the final version.

Corresponding author

Correspondence to Yana Weinstein .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

YW and MAS run a blog, “The Learning Scientists Blog”, which is cited in the tutorial review. The blog does not make money. Free resources on the strategies described in this tutorial review are provided on the blog. Occasionally, YW and MAS are invited by schools/school districts to present research findings from cognitive psychology applied to education.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and permissions

About this article

Cite this article.

Weinstein, Y., Madan, C.R. & Sumeracki, M.A. Teaching the science of learning. Cogn. Research 3 , 2 (2018). https://doi.org/10.1186/s41235-017-0087-y

Download citation

Received : 20 December 2016

Accepted : 02 December 2017

Published : 24 January 2018

DOI : https://doi.org/10.1186/s41235-017-0087-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• DOI: 10.62966/ijose.vi.776
• Corpus ID: 269531230

Use of Interactive Learning Technology in Improving Literacy Skills in Elementary Students

• Wiwik Tri Handayani
• Published in International Journal of… 29 April 2024
• Education, Computer Science
• International Journal of Students Education

11 References

Related papers.

Showing 1 through 3 of 0 Related Papers

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 20 June 2024

Students’ perception of peer teaching in engineering education: a mixed–method case study

• Constantin Cătălin Dosoftei 1 &
• Lidia Alexa 1  

Humanities and Social Sciences Communications volume  11 , Article number:  793 ( 2024 ) Cite this article

56 Accesses

Metrics details

• Science, technology and society

Background : Engineering education is constantly evolving and adapting to meet the demand for diverse skills and competencies in graduates, in response to the changing global economy and technological advancements. This requires shifting from a traditional content-oriented and professor-focused approach towards a more interactive, student-centered approach in which students actively engage in all process stages. The study’s main objective was to examine the students’ perceptions of peer teaching and better understand the method’s perceived advantages and disadvantages. The research was conducted over two academic years (2021 and 2022) and involved 96 students. The research incorporated quantitative and qualitative data collected through online questionnaires completed by the students at the end of the semester. The results showed a cumulative positive response rate for all close-ended questions of over 60%. The correlation analysis revealed medium positive relationships among the variables, including self-confidence, academic performance, communication and active listening, teamwork, knowledge consolidation, student-teacher benefits, and teaching activity. The thematic analysis of the open-ended questions showed that 87% of the respondents perceived the peer-teaching experience as positive and valuable. The main advantages listed by students were better communication, practicality, increased attention and interaction, and overcoming student-teacher anxiety. The main disadvantage was the perceived lack of structure and experience in coordinating laboratory work. The study results indicate that peer-based instructional methods can lead to more effective dissemination of knowledge among students, as evidenced by the high percentage of respondents who reported improved comprehension through peer-to-peer explanations. At the same time, the efficacy of this approach is contingent upon the instructor’s preparation and support, which facilitates the learning process and enhances the classroom’s social dynamics.

Similar content being viewed by others

A meta-analysis to gauge the impact of pedagogies employed in mixed-ability high school biology classrooms

Nudge or not, university teachers have mixed feelings about online teaching

Validity and reliability of a questionnaire developed to explore quality assurance components for teaching and learning in vocational and technical education

Introduction.

The engineering educational landscape has undergone significant shifts in recent years, with a growing emphasis on developing a broad range of skills and competencies in engineering students beyond technical expertise. The demand for engineers with diverse skills and competencies has risen in response to the increasing complexity of the global economy and technological advancement (Jamieson and Lohman, 2012 ).

This poses multiple challenges for more traditional and content-focused engineering education institutions, which predominately use lectures and demonstrations, teaching methods that no longer meet students’ 21st-century competencies and academic needs (Orji and Ogbuanya, 2018 ). To meet this demand, educational institutions have had to adapt curricula and teaching methods to better prepare students for success in the modern workforce.

Changes have been made in the instructional process in engineering schools worldwide, recognising the need for a more holistic approach to preparing engineers for the challenges and opportunities of the modern world. This has involved a shift from the content-oriented and instructor-focused approach (Lindblom-Ylänne et al. 2006 ) towards a more hands-on, active learning approach, such as cooperative learning and peer teaching, which effectively develops critical thinking, problem-solving, and communication skills in engineers, essential for success in the 21st century (Lima et al. 2017 ; Hartikainen et al. 2019 ; Tomkin et al. 2019 ; Tullis and Goldstone, 2020 ).

In this study, we investigate using a specific active learning technique, namely student peer teaching, in the context of an elective laboratory class on Hydropneumatics Drives offered within the bachelor’s degree program in Automation and Applied Informatics at the Faculty of Automatic Control and Computer Engineering. The course aims to give students a comprehensive understanding of pneumatic drives and their advantages over mechanical, hydraulic, or electrical equipment. The study was conducted over two consecutive academic years, 2021 and 2022, and focuses specifically on the laboratory component of the course.

The current paper is structured into five sections, each providing a comprehensive overview of the study’s objectives and methods. The first section examines the literature on peer learning and peer teaching in higher education. The second section presents the research setting and the specific peer teaching process and activities utilized in the Hydropneumatics Drives laboratory. The third section describes the methodology employed throughout the study, including the techniques and methods used to collect and analyse data. The fourth section presents the study’s findings, including a detailed discussion of the outcomes. Finally, the fifth section concludes the paper by highlighting the study’s limitations, providing recommendations for future research, and discussing the implications of the findings for Higher Education Institution (HEI) professors.

Peer learning and peer teaching

The word “peer” comes from the Latin word “par,” meaning equal and describes someone who is a member of the same social group, profession, or age range as oneself. Learning with and from one’s peers is a natural and common human activity, and this type of learning has been proven to be very beneficial for all parties involved (Meeuwisse et al. 2010 ; Soldner et al. 2012 ; Snyder et al. 2016 ; Gong et al. 2020 ).

Peer learning can be defined as “the use of teaching and learning strategies in which students learn with and from each other without the immediate intervention of a teacher” (Boud et al. 1999 ).

Peer learning is becoming increasingly popular in various disciplines and contexts because it offers many advantages for students as it allows them to learn by explaining their ideas to others and engaging in activities where they can learn from their peers. It creates a non-competitive empowering environment (Egbochuku and Obiunu, 2006 ) and helps them to develop skills such as organizing and planning learning activities, working effectively in teams, providing and receiving feedback, and evaluating their learning (Boud, 2001 ; Bene and Bergus, 2014 ; Williams and Reddy, 2016 ).

One critical benefit of peer learning is that it allows students to take on active roles in their education rather than being passive recipients of information. This can increase motivation and engagement, as students are more likely to be invested in the material when actively participating in the learning process (Glynn et al. 2006 ; Lucas, 2009 ; Rusli et al. 2020 ). Multiple previous studies have demonstrated that these programs not only enhance students’ self-assurance and better equip them for assessments/exams but also enhance academic achievements and encourage further academic pursuits (Altintas et al. 2016 ; Rohrbeck et al. 2003 ; Elshami et al. 2020 ; Williams and Reddy, 2016 ; Porter et al. 2013 ).

At the same time, peer teaching is a mutually beneficial process for both student learners and student teachers as it allows for revising and deepening knowledge (Boud, 2001 ; Capstick, 2004 ; Ramaswamy et al. 2001 ; Tullis and Goldstone, 2020 ; Boud, 2001 ). Student-teachers can improve their communication skills by explaining complex ideas to others, which is crucial for working in groups and with colleagues. The need to explain the material to others can increase both the willingness to acquire knowledge (Daud and Ali, 2014 ) and actual learning by allowing one to understand better, clarify, and internalize the information, identify misconceptions, and gain new perspectives (Webb, et al. 2009 ; Bene and Bergus, 2014 ; Erlich and Shaughnessy, 2014 ).

A widely recognized educational tool, the Learning Pyramid, suggests that the most effective way to learn and gain skills so necessary in engineering is by practicing or actively participating - with the most significant value of 90% retention, teaching the material to someone else - with 70% retention, and discussing the material with others - with 50% retention (Al-Badrawy, 2017 ; Gabor et al. 2022 ).

These methods have been the subject of significant research in recent years, with studies showing that they can effectively enhance student engagement, motivation, and achievement in various educational contexts (Felder and Silverman, 1988 ; Prince, 2004 ; Roseth et al. 2008 ; Secomb, 2008 ).

The studies focusing on engineering education revealed that active learning methods and, significantly, peer teaching effectively improved engineering students’ conceptual understanding and problem-solving skills (Felder and Silverman, 1988 ; Smith, et al. 2009 ), overall academic performance, and attitude toward learning (Prince, 2004 ; Freeman, et al. 2014 ; Tullis and Goldstone, 2020 ; Bene and Bergus, 2014 ; Hailikari et al. 2021 ).

The research setting

In the era of rapid technological advancement, engineering graduates are expected to have a strong innovative mindset and be equipped to tackle complex challenges posed by new technologies. The quality of education students receive during their studies, including acquiring essential skills and competencies, will play a significant role in meeting these demands. Revamping the way laboratory hours are conducted, using new and effective methods, provides students with an in-depth understanding of their chosen engineering field and boosts their confidence in their abilities. Therefore, laboratories are considered essential to engineering programs and are used as part of an active learning strategy (Rodgers, et al. 2020 ).

The experimental laboratory is critical to engineering education as it enables students to apply theoretical concepts to real-life scenarios. By allowing students to observe, measure, and analyse real-world phenomena, they gain a deeper understanding of engineering principles. Hands-on learning opportunities and exposure to the dynamic engineering field through the laboratory can significantly enhance student engagement and intrinsic motivation (Snětinová and Kácovský, 2019 ).

The Hydropneumatics Drives laboratory is used to test and study pneumatic drive systems. Pneumatic drives use pressurized gas, typically air, to power and control mechanical devices. These systems are used in various applications, including manufacturing, material handling, and automation. In a hydropneumatics drives laboratory, future engineers might test and analyse the performance of pneumatic actuators, valves, and other components and study the design and operation of pneumatic drive systems.

The Hydropneumatics Drives laboratory is a “hands-on” lab where the students learn through interactive activities or exercises that allow them to gain practical experience by performing a task or series of tasks, typically using didactic or industrial equipment and/or software for pneumatic circuit designing. With access to the latest equipment and technology, students can conduct experiments and research that would otherwise not be possible, providing a more authentic and valuable learning experience. In parallel with the new setting and equipment, in 2021, reciprocal peer teaching was introduced as a learning method. This approach can be effective in helping students to better understand concepts and retain information, as it allows them to actively engage with the material and learn from a peer who may have a different perspective or approach to the subject (Deslauriers et al. 2011 ).

Additionally, reciprocal peer teaching helps students develop essential soft skills, such as communication, critical thinking, problem-solving, teamwork, and collaboration, which are becoming more important in the new economic and industrial context (Tullis and Goldstone, 2020 ).

The laboratory is conducted with a group of students (usually four groups) formed out of 12–15, divided into three teams according to the student’s preferences. It is necessary to make an appointment for each student to take on the laboratory teacher role. Each student must take on this role at least once. After a complete rotation, when each student has taken on this role, for the remaining laboratory sessions, it is up to them to select the role, no longer being a requirement.

Considering an experimental laboratory’s complexity, the student-teachers must prepare for the working lab in advance. This training consists of two parts: first, they must read the laboratory description independently. In the second part, all student-teachers meet with the professor to highlight essential things from the next lab, starting with the learning goals and students’ expectations and ending with the results of the experiments and conclusions drawn from the results of the laboratory assignment. The entire process is presented in Fig. 1 .

The workflow in the experimental laboratory for Student Peer Teaching.

Through performing experiments and collecting data before the lab, student-teachers can apply and reinforce their understanding of scientific concepts and principles, which they will present and discuss with their colleagues in time of the laboratory. In the equipment portfolio, there are transparent or cut-away versions of teaching equipment which are imperative for understanding the principles of operation of a particular piece of equipment. These allow students to visualize the concepts they are learning about and can be used to demonstrate the principles of operation in a safe and controlled environment. It also allows peer teachers in the laboratory to focus on specific parts of the equipment, making the explanation more detailed and accurate for their colleagues to understand. Another tool for learning is the animation of working for each piece of equipment available from the equipment producer or the Internet.

However, the student-teachers can still use various other online resources to enhance their explanations and make them more detailed and precise, so that their colleagues can better understand.

During the labs, when the weight centre is shifted from the professor to the student-teachers, the professor can observe the entire learning process and act as a guide and facilitator, helping student-teachers present the procedures of the laboratory and providing guidance as needed. This is the basis of the pedagogy of engagement in which the professor assumes the role of designing and facilitating the learning experiences (Smith et al. 2005 ).

The study’s main objective was to examine the computer science student’s perception of peer teaching and better understand the method’s perceived advantages and disadvantages in a Hydropneumatics Drives laboratory context.

To evaluate the effectiveness of the chosen method of instruction, the research team aimed to provide answers to two research questions:

How do students perceive the peer-teaching experience?

What are the perceived advantages and disadvantages of the method from the student’s perspective?

To achieve this objective, the research team employed a pragmatic approach, incorporating both quantitative and qualitative data, to gain deeper insight into students’ views on the peer-teaching process.

Participants

The study participants were computer science students enrolled in the Hydropneumatics Drives laboratory course during two consecutive academic years: 2021 and 2022. There were 96 students in total, 42 students in the 2021 academic year and 54 students in the 2022 academic year. All students participated in the peer teaching process as both student teachers and learners, and therefore, they all had to complete the two questionnaires.

As seen from Tables 1 and 2 , 59 students completed the student-learner questionnaire, representing a 61% response rate, while 62 students completed the student–teacher questionnaire, representing a 65% response rate.

For context, 30.5% of the respondents completed the course in the 1st Semester of 2021, while 69.5% completed the course in the 1st Semester of 2022.

The student–teacher questionnaire is presented in Table 2 .

For the second questionnaire, 27.4% of the respondents completed the course in the 1st Semester of 2021, and 72.6% of the students completed the course in the 1st Semester of 2022.

Data collection

In the data collection stage, the students completed two questionnaires, one from the student-learner perspective and one from the student-teacher perspective. The students were asked to complete the online survey at the end of the semester, and the data was collected via Google Forms.

Both questionnaires had two parts, one that included close-ended questions using a 5-point liker scale (1 = Strongly disagree; 2 = Disagree; 3 = Neither agree nor disagree; 4 = Agree; 5 = Strongly agree) and open-ended questions regarding the advantages and disadvantages of the instructional process and recommendations.

The student-learners questionnaire included:

13 close-ended questions using a 5-point Likert scale for each evaluation criteria (see Table 3 ).

One open-ended question referred to the perceived advantages and disadvantages of the peer teaching method.

The student–teachers’ questionnaire included:

10 close-ended questions using a 5-point Likert scale for each evaluation criteria (see Table 4 ).

4 open-ended questions referred to the reasons they chose/did not choose to teach more than one seminar, the difficulties they faced, and the things they would do differently if given the opportunity.

Data analysis

The analysis was based on two main categories: quantitative and qualitative.

The quantitative analysis was executed using multiple tests in SPSS, while the qualitative one used manual coding in Excel on both student-teacher and student-learner questionnaires, with the same analysis steps being considered. For the quantitative analysis, 13 questions were designed to be studied on the student-learners scale and 10 for the student-teachers scale, respectively. As a first step, to validate the questionnaire items, a Reliability Analysis was run and the Cronbach’s Alpha values indicate there is a correspondence between the questions selected and they are relevant for the survey. The Alpha values being compared with the 0.8 threshold (0.891 > 0.8 for student-learners), (0.818 > 0.8 for student-teachers). Based on the validated items, a series of Descriptive Statistics determined an average cumulative positive impact on student-teachers of 74.66% based on the interval (62.9–95.1%) and the same average cumulative positive impact had a value of 80.12% (60.4% - 100%) for the student-learners scale. The last step in the quantitative section was to apply a correlation analysis to measure the strength of the relationship between the variables. Testing the Pearson Correlation Coefficient with a significance level chosen ( p -value < 0.05), a group of positive, strong relationships (r > 0.5) were described on both scales. For the student-learner questionnaire, 6 relationships are identified, with Pearson Correlation values between (r = 0.516 – r = 0.625) and 14 relationships for the student-teacher scale, having values between (r = 0.503 – r = 0.654).

The qualitative analysis reports four main themes grouped as two factors on each scale: the student-teacher scale describes Advantages and Disadvantages, and the student-learners define Difficulties and Improvements. The first step outlines going through the open-ended questions and manually coding the responses into keywords. Following this, each keyword, based on frequency, is grouped within its relevant theme.

Results and discussions

Insights from the quantitative analysis.

As presented in Table 3 , the cumulative positive response rate for all close-ended questions was over 60%.

According to the table, the positive response regarding the laboratory room was 100%, followed by the laboratory equipment used, with a cumulative positive value of 98.3%. The results show the importance of the laboratory setting and equipment for technical labs. The correlation analysis also supports this, as medium positive relationships ( > 0.5) between the following variables were identified: a strong relationship between laboratory equipment (Q10) and laboratory room (Q12) (r = 0.625) and a medium relationship between the number of students (Q11) and laboratory room (Q12) (r = 0.622). Other relevant statistical relationships were between the following variables: a medium relationship between student-teaching methods (Q2) and training (Q8) (r = 0.546), a medium relationship between preparation and knowledge (Q9), and an explanation by a colleague of the material (Q4) (r = 0.516), a medium relationship between expectations (Q6) and training (Q8) (r = 0.593) and medium relationship between expectations (Q6) and peer-led laboratories (Q13) (r = 0.572).

According to the data in Table 4 , a positive impact was observed for all questions regarding the respondents’ opinions, with a cumulative value of the first two response options (Agree and Strongly Agree) exceeding 60%.

According to the Pearson correlation coefficient (r), medium relationships exist among the items used in the analysis. Medium relationships were identified between self-confidence (Q1) and each of the following variables: academic performance (Q2) (r = 0.580), communication and active listening (Q3) (r = 0.580), student-teacher benefits (Q6) (r = 0.654), and teaching activity (Q7) (r = 0.513). Additionally, medium relationships were identified for academic performance with the following variables: communication and active listening (Q3) (r = 0.543), knowledge consolidation (Q4) (r = 0.522), teamwork (Q5) (r = 0.524), and student-teacher benefits (Q6) (r = 0.605). For communication and active listening skills, medium relationships were identified with the following variables: knowledge consolidation (Q4) (r = 0.576), teamwork (Q5) (r = 0.594), and teaching activity (Q7) (r = 0.503). For the knowledge consolidation variable, medium relationships were identified with teamwork (Q5) (r = 0.573) and student-learner benefits (Q10) (r = 0.554). A medium relationship was also identified between student-teacher benefits (Q6) and teaching activity (Q7) (r = 0.642).

Insights from the qualitative analysis

The responses from the open-ended questions were transcribed, divided into meaningful fragments, coded manually, and analysed using a thematic analysis, which represents the process of “identifying, analysing, and reporting patterns (themes) within data” (Braun and Clarke, 2006 ). The first step of the process consisted of a review of the initial transcribed versions done by the authors. The goal was to better understand the students’ perceptions regarding the overall value of the peer-teaching process and the method’s strengths and weaknesses. The open-ended question in the student–learner questionnaire referred to the perceived advantages and disadvantages of the peer teaching method, and several themes emerged predominantly from the 57 valid answers received. Detailed information on the number of themes and sample responses from the respondents is presented in Tables 5 to 8 .

When asked whether they think the method has proved valuable, 87% answered that the peer-teaching experience was positive and valuable mainly because they felt more comfortable interacting and asking questions. Two students considered that there was no value in the instructional method, and two gave neutral answers (both yes and no). The thematic analysis of the main advantages of the peer-teaching process listed by students is presented in Table 5 .

The main disadvantages of the method perceived by student-learners are presented in Table 6 .

In terms of disadvantages perceived by students, some listed the difference in expertise between student-teachers and professors, leading to students not trusting their peer teachers and, consequently, not learning as much from peers as they do from professors. This result is in line with other studies (Boud et al. 2001 ; Lelis, 2017 ; Sim, 2003 ). Several students mentioned this is a valuable instructional method, but it should be used occasionally. The results also highlight the relevance of several contextual factors, such as individualized teaching-learning style, confidence level, or motivation, that significantly impact the learning-teaching process (Ramm et al. 2015 ; Zarifnejad et al. 2018 ).

The open-ended questions in the student-teacher questionnaire asked about the reasons they chose/did not choose to teach more than one seminar, the difficulties they faced, and the things they would do differently if given the opportunity.

Out of the 62 students who completed the questionnaire, only 34% chose to teach a second time. Over 80% of the students who chose not to teach again did this due to busy academic schedules and inability to participate in the training sessions with the professor (10), impossibility due to activity format and team organization (10), lack of perceived incentives (2), lack of enjoyment of teaching activity (2), perceived lack of talent and lack of confidence (2). An important aspect to mention is the extra work and time student-teachers must put into participating in and delivering the class.

Out of the 34% who decided to teach more than one laboratory, most listed that they learn better when explaining the subject to a colleague because they feel a certain responsibility toward their peers.

However, the main advantages perceived by most peer teachers, regardless of whether they taught more than one laboratory, revolved around two aspects: gaining an in-depth understanding of the subject and developing better communication and presentation skills, both elements confirmed by previous studies on the matter (Tullis and Goldstone, 2020 ; Daud and Ali, 2014 ; Smith, et al. 2009 ).

In terms of the difficulties encountered in the teaching process, 20 students declared that they encountered no difficulties; for the other answers, the main categories identified are listed in Table 7 .

Although the instructional method has multiple benefits, the study revealed a series of drawbacks and challenges.

The first refers to the level of expertise and the need for consistent preparation to deliver quality content. Student responses reinforce the findings of prior research that emphasize the importance for peer-teachers to thoroughly understand the subject matter in order to teach effectively (Stigmar, 2016 ; Menezes and Premnath, 2016 ). The lack of confidence and perceived authority among their peers have also been listed in previous studies as challenges of the method (Irvine et al. 2018 ), as students are sometimes unsure of the tone to use to appear knowledgeable on the subject without seeming arrogant.

When asked what they would do differently if given the opportunity to teach again, nine out of 62 students said that they wouldn’t change anything, while the rest of the 53 listed aspects are included in the categories presented in Table 8 .

After looking across all the comments and comparing the perspectives from both roles, student and teacher, some interesting results arose on the perceived value of the peer teaching instructional method. First, from the student-learner perspective, the aspect of increased engagement and better communication mentioned by students participating in the study was listed by other studies as well (Boud, 2001 ; Lelis, 2017 ; Bulte et al. 2007 ; Lucas, 2009 ; Tullis and Goldstone, 2020 ). Another relevant aspect refers to the student-teacher benefits, namely, learning better by explaining the subject to a colleague. Through teaching, they gained an in-depth understanding of the subject and developed better communication and presentation skills.

The positive impact is also highlighted by the fact that students who participated in the laboratory sessions in the previous academic year showed an increased interest in pursuing bachelor thesis projects related to the pneumatic automation field over the past year.

Conclusions

This study aimed to examine engineering students’ perceptions of the advantages and disadvantages of peer teaching after implementing the method in a specific setting, namely a Hydropneumatics Drives laboratory. Although the analysis is limited to a specific context, the results are promising and support the available literature on peer teaching methods in engineering education.

The results show that students respond positively to the social elements of the peer teaching process, as many highlighted positive aspects related to „better communication” or „increased attention and interaction.” These outcomes are confirmed by previous studies on this matter (Hammond et al. 2010 ; Tullis and Goldstone, 2020 ) and highlight the importance of feeling comfortable asking questions and receiving answers in relevant and applicable terms. Furthermore, the fact that over 70% of respondents declared that they understood the laboratory better when a classmate explained it reinforces the results of previous research highlighting the impact of peer teaching on academic performance (Tullis and Goldstone, 2020 ; Rusli et al. 2020 ). However, the fact that there is a strong positive relationship between preparation and knowledge (Q9) and explanation by a colleague of the material (Q4), means that the success of the instructional method is highly dependent on the preparation of all stages and the professor’s ability to guide and provide support for student-teachers in the preparation and delivering process. An additional benefit of this method lies in the enhancement of empathy between students and professors, as the practicality of the teaching experience offers students a different viewpoint and promotes a deeper understanding of the pedagogical process.

An effective learning process is characterized by its ability to foster student independence, enhance confidence, and elevate motivation. Our results show that peer teaching can be a valuable method for training students to develop independence, enhance their confidence, and increase their enthusiasm to learn, as these are directly related to students assuming responsibility for their own learning. Overall, the study reveals that taking on the teacher role comes with both academic benefits (gaining an in-depth understanding of the subject) and personal benefits (developing better communication and presentation skills). This can further lead to another benefit for the students and the institution: opening the possibility to follow an academic career. This is important as the industry represents a more appealing option, especially for computer science graduates, and fewer decide to continue with a Ph.D. and remain as professors.

The study also has some limitations as it was conducted in a specific setting with a restricted number of computer science students who enrolled in the Hydropneumatics Drives laboratory. Students were all assigned both roles as teachers and as learners, and the demographic data was not included in the analysis. Future studies should be conducted on other courses with larger sample sizes and random assignments. Another useful direction for future studies is investigating the long-term effects of peer teaching on students’ academic performance and retention rates. This can provide valuable information regarding the long-term sustainability of this instructional method, as more research is needed to fully understand its potential impact and optimal implementation strategies.

Data availability

All data generated or analyzed during this study are included in this published article.

Al-Badrawy A-N (2017) Role of Engineering Design in Enhancing ABET Outcomes of Engineering Programs at Taif University. INTERNATIONAL JOURNAL APPLIED SCIENCE Technology. Vol. 6. 9-15., 39 1:9–14

Google Scholar  

Altintas T, Gunes A, Sayan H (2016) A peer-assisted learning experience in computer programming language learning and developing computer programming skills. Innov. Educ. Teach. Int. 53:329–337

Article   Google Scholar  

Bene K, Bergus G (2014) When learners become teachers: a review of peer teaching in medical student education. Fam. Medicion. 46(10):783–787

Boud, D (2001). Making the move to peer learning. In DC Boud, Peer Learning in Higher Education: Learning from and with each other (pp. 1-20.). London: Kogan Page (now Routledge)

Boud D, Cohen R, Sampson J (1999) Peer learning and assessment. Assess. High. Educ. 24:413–426

Boud D, Cohen R, Sampson J (2001) Peer learning in higher education: Learning from and with each other. Kogan Page, London

Braun V, Clarke V (2006) Using a thematic analysis in psychology. Qualit. Res. Psychol. 3(2):77–101

Bulte C, Betts A, Garner K, Durning S (2007) Student teaching: views of student near-peer teachers and learners. Med Teach. 29(6):583–590

Article   PubMed   Google Scholar  

Capstick, S (2004). Benefits and shortcomings of peer assisted learning (PAL) in Higher Education: An appraisal by students,. Peer Assisted Learning Conference Proceedings . 2004: Bournemouth University

Daud, S, & Ali, S (2014). Perceptions of learners about peer assisted learning and lectures. Int. J. Sci. Res, 1449–1455

Deslauriers L, Schelew E, Wieman C (2011) Improved learning in a large-enrollment physics class. Science 332:862–864

Article   ADS   CAS   PubMed   Google Scholar  

Egbochuku, E, & Obiunu, J (2006). The effects of reciprocal peer counselling in the enhancement of self-concept among adolescents. Education Project Innovation Inc., 126 (3)

Elshami W, Abuzaid M, Abdalla M (2020) Radiography students’ perceptions of Peer assisted learning. Radiography 26:e109–e113

Article   CAS   PubMed   Google Scholar  

Erlich DR, Shaughnessy AF (2014) Student–teacher education programme (STEP) by step: Transforming medical students into competent, confident teachers. Med. Teach. 36(4):322–332

Felder RM, Silverman LK (1988) Learning and teaching styles in engineering education. Eng. Educ. 78(7):674–681

Freeman S, Eddy SL, McDonough M, Smith MK, Okoroafor N, Jordt H, Wenderoth MP (2014) Active learning increases student performance in science, engineering, and mathematics. Proc. Natl Acad. Sci. 111(23):8410–8415

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Gabor G, Lucache DD, Dosoftei CC (2022) Learning PLC-based Automation by Using an Educational Elevator. 2022 Int. Conf. Exposit. Electr. Power Eng. (EPE) 36(4):683–687

Glynn, L, MacFarlane, A, Kelly, M, Cantillon, & Murphy, A (2006). Helping each other to learn-a process evaluation of peer assisted learning. BMC Med. Educat. 6 (1)

Gong HJ, Park H, Hagood TC (2020) Peer learning in STEM: a qualitative study of a student-oriented active learning intervention program. Interact. Learn. Environ. 31(4):1922–1932

Hailikari, T, Virtanen, V, Vesalainen, M, & Postareff, L (2021). Student perspectives on how different elements of constructive alignment support active learning. Active Learning in Higher Education

Hammond JA, Bithell CP, Jones L, Bidgood P (2010) A first year experience of student-directed peer-assisted learning. Act. Learn. High. Educ. 11(3):201–212

Hartikainen S, Rintala H, Pylväs L, Nokelainen P (2019) The Concept of Active Learning and the Measurement of Learning Outcomes: A Review of Research in Engineering Higher Education. Educ. Sci. 9(4):276

Irvine S, Williams B, McKenna L (2018) Near-peer teaching in undergraduate nurse education: An integrative review. Nurse Educ. Today 70:60–68

Jamieson L, Lohman J (2012) Innovation with impact: Creating a culture for scholarly and systematic innovation in engineering education. American Society for Engineering Education, Washington, DC

Lelis C (2017) Participation ahead: perceptions of masters degree students on reciprocal peer learning activities. J. Learn. Des. 10(2):14–24

ADS   MathSciNet   Google Scholar  

Lima R, Andersson P, Saalman E (2017) Active learning in engineering education: A (re)introduction. Eur. J. Eng. Educ. 42:1–4

Lindblom-Ylänne S, Trigwell K, Nevgi A, Ashwin P (2006) How approaches to teaching are affected by discipline and teaching context. Stud. High. Educ. 31:285–298

Lucas A (2009) Using peer instruction and i-clickers to enhance student participation in calculus. Primus 19(3):219–231

Meeuwisse M, Severiens SE, Born MP (2010) Learning environment, interaction, sense of belonging and study success in ethnically diverse student groups. Res. High. Educ. 51(6):528–545

Menezes S, Premnath D (2016) Near-peer education: A novel teaching program. Int. J. Med. Educ. 7:160–167

Article   PubMed   PubMed Central   Google Scholar  

Orji, TC, & Ogbuanya, TC (2018). Assessing the effectiveness of problem-based and lecture-based learning environments on students’ achievements in electronic works. Int. J. Electrical Engineering Educat., 1–20

Porter, L, Bailey-Lee, C, & Simon, B (2013). Halving fail rates using peer instruction: A study of four computer science courses. SIGCSE ‘13: Proceedings of the 44th ACM technical symposium on computer science education (pp. 177–182). New York: ACM

Prince M (2004) Does Active Learning Work? A Review of the Research. J. Eng. Educ. 93(3):223–231

Ramaswamy S, Harris I, Tschirner U (2001) Student Peer Teaching: An Innovative Approach to Instruction in Science and Engineering Education. J. Sci. Educ. Technol. 10(No. 2):165–171

Ramm D, Thomson A, Jackson A (2015) Learning clinical skills in the simulation suite: the lived experiences of student nurses involved in peer teaching and peer assessment. Nurse Educ. Today 35(6):823–827

Rodgers TL, Cheema N, Vasanth S, Jamshed A, Alfutimie A, Scully PJ (2020) Developing pre-Laboratory Videos for Enhancing Student Preparedness. Eur. J. Eng. Educ. 45(2):292–304

Rohrbeck CA, Ginsburg-Block MD, Fantuzzo JW, Miller TR (2003) Peer-assisted learning interventions with elementary school students: A meta-analytic review. J. Educ. Psychol. 95:240–257

Roseth CJ, Garfield JB, Ben-Zvi D (2008) Collaboration in learning and teaching statistics. J. Stat. Educ. 16:1

Rusli, M, Degeng, NS, Setyosari, P, & Sulton, M (2020). Peer teaching: Students teaching students to increase academic performance. Teaching Theology & Religion

Secomb J (2008) A systematic review of peer teaching and learning in clinical education. J. Clin. Nurs. 17(6):703–716

Sim, L (2003). Student perceptions of peer learning in the English unit ‘Romanticism and Revolution’,. In AB O’Sullivan (Ed.), Partners in Learning: Proceedings of the 12th Annual Teaching and Learning Forum . Perth, Australia

Smith KA, Sheppard SD, Johnson DW, Johnson RT (2005) Pedagogies of Engagement: Classroom-Based Practices. J. Eng. Educ. 94(1):87–101

Smith MK, Wood WB, Adams WK, Wieman C, Knight JK, Guild N, Su T (2009) Why peer discussion improves student performance on in-class concept questions. Science 323(5910):122–124

Snětinová, M, & Kácovský, P (2019). Hands-on experiments in the interactive physics laboratory: A study of students’ intrinsic motivation. J. Phys. Conference Series, 1287(1)

Snyder JJ, Sloane JD, Dunk RD, Wiles JR (2016) Peer-led team learning helps minority students succeed. PLoS Biol. 14(3):1–7

Soldner M, Rowan-Kenyon H, Inkelas KK, Garvey J, Robbins C (2012) Supporting students’ intentions to persist in STEM disciplines: The role of living-learning programs among other social-cognitive factors. J. High. Educ. 83(3):311–336

Stigmar M 2016Peer-to-peer teaching in higher education: A critical literature review. Mentoring Tutoring Partnership in Learning 24(20):124–136

Tomkin, J, Beilstein, S, Morphew, J, & Herman, G (2019). Evidence that communities of practice are associated with active learning in large STEM lectures. Int. J. STEM Educat. 6(1)

Tullis, JG, & Goldstone, RL (2020). Why does peer instruction benefit student learning? Cognit. Res. Principles Implications, 5(1)

Webb N, Franke M, De T, Chan A, Freund D, Shein P, Melkonian D (2009) ‘Explain to your partner’: teachers’ instructional practices and students’ dialogue in small groups. Camb. J. Educ. 39(1):49–70

Williams B, Reddy P (2016) Does peer-assisted learning improve academic performance? A scoping review. Nurse Educ. Today 42:23–29

Zarifnejad, G, Mirhaghi, A, & Rajabpoor, M (2018). Does peer education increase academic achievement in first year students? A mixed-method study. J. Peer Learning, 11

Download references

Author information

Authors and affiliations.

“Gheorghe Asachi” Technical University of Iasi, Iași, Romania

Constantin Cătălin Dosoftei & Lidia Alexa

You can also search for this author in PubMed   Google Scholar

Contributions

The authors contributed equally to the article writing process, from formulating the research plan to writing and editing the manuscript.

Corresponding author

Correspondence to Lidia Alexa .

Ethics declarations

Competing interests.

The authors declare that they have no competing interests.

Ethical approval

All procedures performed in our study were in accordance with the ethical standards of the Gheorghe Asachi Technical University of Iasi. The research was conducted in accordance with the Code of ethics and professional deontology of the Gheorghe Asachi Technical University of Iasi - TUIASI.COD.01, approved on 21.01.2016, edition 3, rev. 0.

Informed consent

Informed consent was obtained from all individual participants involved in the study. Participants were involved in an information session about the study and had the opportunity to ask questions before fill-up the questionnaire. Participants were informed that they could refuse to complete the questionnaire without penalty or consequences. The questionnaire ensured participants’ anonymity, as no identifying details were required.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Dosoftei, C.C., Alexa, L. Students’ perception of peer teaching in engineering education: a mixed–method case study. Humanit Soc Sci Commun 11 , 793 (2024). https://doi.org/10.1057/s41599-024-03349-y

Download citation

Received : 10 April 2023

Accepted : 13 June 2024

Published : 20 June 2024

DOI : https://doi.org/10.1057/s41599-024-03349-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

A bibliometric analysis of artificial intelligence in language teaching and learning (1990–2023): evolution, trends and future directions

• Published: 22 June 2024

Cite this article

• Ma Huiling   ORCID: orcid.org/0000-0002-6834-3195 1 , 2 ,
• Lilliati Ismail 2 &
• Han Weijing 2   nAff3  

The advancement and application of Artificial Intelligence (AI) has introduced innovative changes in language learning and teaching. In particular, the widespread utilization of various chatbots as foreign language learning partners showcases their remarkable potential contribution to the field. Nevertheless, there are currently few studies that encompass extensive and holistic reviews and analyses of the relevant literature during this period. The study employs bibliometric analysis and a systematic review of representative research to present trends, the current status and future directions of AI research in language teaching and learning, providing language educators, policymakers, and research scholars with visually accessible and comprehensive insights. Results indicate that the field is in its early stages of development, growing rapidly with significant research potential. The study identified the most productive and influential sources, institutions, authors and countries and provided a summary for the most representative papers in the research field. Through keyword analysis, the study delineates the evolutionary progression of AI in the domain of language teaching and learning across different time periods, identifies prevailing research trends and proposes future research directions. Results indicate that influential research in this realm predominantly focuses on refining technological solutions and conducting empirical studies on AI applications in language teaching and learning. This highlights significant interest in the effectiveness of AI in language education and its implementation methods. However, research on the application of AI in language education is still in its infancy. Therefore, the study advocates for increased empirical research on AI’s specific applications in language listening, speaking, reading, and writing, as well as the development of more effective pedagogical approaches. Furthermore, the findings reveal a lack of attention given to various concerns and challenges associated with AI utilization in language teaching and learning, such as concerns regarding academic integrity, content authenticity, potential bias, privacy and security issues, and environmental concerns. At present, there is a lack of suitable solutions or regulatory frameworks proposed to address these concerns adequately.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Data availability

The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.

Abdullah Sharadgah, T., & Abdulatif Sa’di, R. (2022). A systematic review of research on the use of artificial intelligence in english language teaching and learning (2015–2021): What are the current effects? Journal of Information Technology Education: Research , 21 , 337–377. https://doi.org/10.28945/4999 .

Article   Google Scholar  

Agarwal, A., Durairajanayagam, D., Tatagari, S., Esteves, S., Harlev, A., Henkel, R., Roychoudhury, S., Homa, S., Puchalt, N., Ramasamy, R., Majzoub, A., Ly, K., Tvrda, E., Assidi, M., Kesari, K., Sharma, R., Banihani, S., Ko, E., Abu-Elmagd, M., & Gosalvez, J. (2016). Bibliometrics: Tracking research impact by selecting the appropriate metrics. Asian Journal of Andrology , 18 (2), 296. https://doi.org/10.4103/1008-682x.171582 .

Belda-Medina, J., & Calvo-Ferrer, J. R. (2022). Using chatbots as AI conversational partners in Language Learning. Applied Sciences , 12 (17), 8427. https://doi.org/10.3390/app12178427 .

Chen, H. L., Widarso, V., G., & Sutrisno, H. (2020). A ChatBot for Learning Chinese: Learning achievement and technology acceptance. Journal of Educational Computing Research , 58 (6), 1161–1189. https://doi.org/10.1177/0735633120929622 .

Chen, C. H., Koong, C. S., & Liao, C. (2022). Influences of integrating dynamic assessment into a speech recognition learning design to support students’ english speaking skills, learning anxiety and cognitive load. Educational Technology & Society , 25 (1), 1–14.

Google Scholar  

Chen, X., Zou, D., Cheng, G., & Xie, H. (2021, July 1). Artificial intelligence-assisted personalized language learning: systematic review and co-citation analysis . IEEE Xplore. https://doi.org/10.1109/ICALT52272.2021.00079 .

Chodorow, M., Gamon, M., & Tetreault, J. (2010). The utility of article and preposition error correction systems for English language learners: Feedback and assessment. Language Testing , 27 (3), 419–436. https://doi.org/10.1177/0265532210364391 .

Chowdhary, K. R. (2020). Natural language processing. Fundamentals of Artificial Intelligence , 603–649. https://doi.org/10.1007/978-81-322-3972-7_19 .

Coughlin, J. M. (1990). Artificial intelligence and computer-assisted language learning: Present developments and future prospects. The French Review , 63 (3), 560–565. http://www.jstor.org/stable/394523 .

Delaherche, E., Chetouani, M., Mahdhaoui, A., Saint-Georges, C., Viaux, S., & Cohen, D. (2012). Interpersonal synchrony: A survey of evaluation methods across disciplines. IEEE Transactions on Affective Computing , 3 (3), 349–365. https://doi.org/10.1109/t-affc.2012.12 .

Dergaa, I., Chamari, K., Zmijewski, P., & Ben Saad, H. (2023). From human writing to artificial intelligence generated text: Examining the prospects and potential threats of ChatGPT in academic writing. Biology of Sport , 40 (2), 615–622. https://doi.org/10.5114/biolsport.2023.125623 .

Dizon, G. (2020). Evaluating intelligent personal assistants for L2 listening and speaking development. Language Learning & Technology , 24 (1), 16–26.

MathSciNet   Google Scholar  

Fatih, Karataş, & Yasemin Kuzgun. (2024). Faramarz Yaşar Abedi, Filiz Ozek Gunyel, Derya Karadeniz, &. Incorporating AI in foreign language education: An investigation into ChatGPT’s effect on foreign language learners. Education and Information Technologies . https://doi.org/10.1007/s10639-024-12574-6 .

Fryer, L., & Carpenter, R. (2006). Bots as language learning tools. Language Learning & Technology , 10 (3).

Fryer, L. K., Ainley, M., Thompson, A., Gibson, A., & Sherlock, Z. (2017). Stimulating and sustaining interest in a language course: An experimental comparison of Chatbot and Human task partners. Computers in Human Behavior , 75 , 461–468. https://doi.org/10.1016/j.chb.2017.05.045 .

Fryer, L. K., Nakao, K., & Thompson, A. (2019). Chatbot learning partners: Connecting learning experiences, interest and competence. Computers in Human Behavior , 93 , 279–289. https://doi.org/10.1016/j.chb.2018.12.023 .

Fryer, L. K., Coniam, D., Carpenter, R., & Lăpușneanu, D. (2020). Bots for language learning now: Current and future directions. Language Learning & Technology , 24 (2), 8–22.

Gibney, E. (2022). How to shrink AI’s ballooning carbon footprint. Nature , 607 (7920), 648–648.

Huang, W., Hew, K. F., & Fryer, L. K. (2022). Chatbots for language learning—are they really useful? A systematic review of chatbot-supported language learning. Journal of Computer Assisted Learning , 38 (1), 237–257. https://doi.org/10.1111/jcal.12610 .

Huang, X., Zou, D., Cheng, G., Chen, X., & Xie, H. (2023). Trends, research issues and applications of artificial intelligence in language education. Educational Technology & Society , 26 (1), 112–131. https://www.jstor.org/stable/48707971 .

Jaakkola, H., Henno, J., Lahti, A., Järvinen, J. P., & Mäkelä, J. (2020). Artificial intelligence and education. 43rd International Convention on Information, Communication and Electronic Technology (MIPRO) , 548–555.

Ji, H., Han, I., & Ko, Y. (2022). A systematic review of conversational AI in language education: Focusing on the collaboration with human teachers. Journal of Research on Technology in Education , 1–16. https://doi.org/10.1080/15391523.2022.2142873 .

Jia, J. (2009). CSIEC: A computer assisted English learning chatbot based on textual knowledge and reasoning. Knowledge-Based Systems , 22 (4), 249–255. https://doi.org/10.1016/j.knosys.2008.09.001 .

Johnson, W. L. (2007). Serious use of a serious game for language learning. International Journal of Artificial Intelligence in Education , 20 . https://doi.org/10.3233/JAI-2010-0006 .

Johnson, W. L., Vilhjalmsson, H., & Marsella, S. (2005). Serious games for language learning: How much game, how much AI? Artificial Intelligence in Edcation , 306–313.

Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science , 349 (6245), 255–260. https://doi.org/10.1126/science.aaa8415 .

Article   MathSciNet   Google Scholar  

Kerly, A., Ellis, R., & Bull, S. (2008). CALMsystem: A conversational agent for learner modelling. Knowledge-Based Systems , 21 (3), 238–246. https://doi.org/10.1016/j.knosys.2007.11.015 .

Kessler, G. (2018). Technology and the future of language teaching. Foreign Language Annals , 51 (1), 205–218. https://doi.org/10.1111/flan.12318 .

Kim, J., Lee, H., & Cho, Y. H. (2022). Learning design to support student-AI collaboration: Perspectives of leading teachers for AI in education. Education and Information Technologies , 27 (5). https://doi.org/10.1007/s10639-021-10831-6 .

Kirkpatrick, K. (2023). The carbon footprint of artificial intelligence. Communications of the ACM , 66 (8), 17–19. https://doi.org/10.1145/3603746 .

Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning. RELC Journal , 003368822311628. https://doi.org/10.1177/00336882231162868 .

Hwang, G.-J., Xie, H., Wah, B., & Gašević, D. (2020). Vision, challenges, roles and research issues of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence , 1 (1), 100001. https://doi.org/10.1016/j.caeai.2020.100001 .

Liang, J. C., Hwang, G. J., Chen, M. R. A., & Darmawansah, D. (2021). Roles and research foci of artificial intelligence in language education: An integrated bibliographic analysis and systematic review approach. Interactive Learning Environments , 31 (7), 1–27. https://doi.org/10.1080/10494820.2021.1958348 .

Lu, X. (2019). An empirical study on the Artificial Intelligence writing evaluation system in China CET. Big Data . https://doi.org/10.1089/big.2018.0151 .

Moral-Muñoz, J. A., Herrera-Viedma, E., Santisteban-Espejo, A., & Cobo, M. J. (2020). Software tools for conducting bibliometric analysis in science: An up-to-date review. El Profesional De La Información , 29 (1). https://doi.org/10.3145/epi.2020.ene.03 .

Perkins, M. (2023). Academic integrity considerations of AI large Language models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching and Learning Practice , 20 (2). https://doi.org/10.53761/1.20.02.07 .

Pokrivcakova, S. (2019). Preparing teachers for the application of AI-powered technologies in foreign language education. Journal of Language and Cultural Education , 7 (3), 135–153. https://doi.org/10.2478/jolace-2019-0025 .

Ruan, S., Jiang, L., Xu, J., Tham, B. J. K., Qiu, Z., Zhu, Y., Murnane, E. L., Brunskill, E., & Landay, J. A. (2019). QuizBot. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems . https://doi.org/10.1145/3290605.3300587 .

Salmon, P. M., Baber, C., Burns, C., Carden, T., Cooke, N. J., Cummings, M., Hancock, P., McLean, S., Gemma, J. M., Read, & Stanton, N. A. (2023). Managing the risks of artificial general intelligence: A human factors and ergonomics perspective. The 5 Th MLSys Conference . https://doi.org/10.1002/hfm.20996 .

Settles, B., & Meeder, B. (2016). A trainable spaced repetition model for language learning. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 1 . https://doi.org/10.18653/v1/p16-1174 .

Sindermann, C., Sha, P., Zhou, M., Wernicke, J., SchmittH. S., Li, M., Sariyska, R., Stavrou, M., Becker, B., & Montag, C. (2020). Assessing the attitude towards Artificial Intelligence: Introduction of a short measure in German, Chinese, and English Language. KI - Künstliche Intelligenz , 35 . https://doi.org/10.1007/s13218-020-00689-0 .

Smutny, P., & Schreiberova, P. (2020). Chatbots for learning: A review of educational chatbots for the Facebook Messenger. Computers & Education , 151 (103862), 103862. https://doi.org/10.1016/j.compedu.2020.103862 .

Sun, Z., Anbarasan, M., & Praveen Kumar, D. (2020). Design of online intelligent English teaching platform based on artificial intelligence techniques. Computational Intelligence . https://doi.org/10.1111/coin.12351 .

Tai, T. Y., & Chen, H. H. J. (2020). The impact of Google Assistant on adolescent EFL learners’ willingness to communicate. Interactive Learning Environments , 1–18. https://doi.org/10.1080/10494820.2020.1841801 .

Troussas, C., Virvou, M., & Alepis, E. (2013). Comulang: Towards a collaborative e-learning system that supports student group modeling. SpringerPlus , 2 (1). https://doi.org/10.1186/2193-1801-2-387 .

Weizenbaum, J. (1966). ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM , 9 (1), 36–45. https://doi.org/10.1145/365153.365168 .

Yan, D. (2023). Impact of ChatGPT on learners in a L2 writing practicum: An exploratory investigation. Education and Information Technologies . https://doi.org/10.1007/s10639-023-11742-4 .

Yang, S. J. H., Ogata, H., Matsui, T., & Chen, N.-S. (2021). Human-centered artificial intelligence in education: Seeing the invisible through the visible. Computers and Education: Artificial Intelligence , 2 , 100008. https://doi.org/10.1016/j.caeai.2021.100008 .

Yang, H., & Kyun, S. (2022). The current research trend of artificial intelligence in language learning: A systematic empirical literature review from an activity theory perspective. Australasian Journal of Educational Technology , 38 (5), 180–210. https://doi.org/10.14742/ajet.7492 .

Yang, H., Kim, H., Lee, J. H., & Shin, D. (2022). Implementation of an AI chatbot as an English conversation partner in EFL speaking classes. ReCALL , 1–17. https://doi.org/10.1017/s0958344022000039 .

Yang, H., Gao, C., & Shen, H. (2023). Learner interaction with, and response to, AI-programmed automated writing evaluation feedback in EFL writing: An exploratory study. Education and Information Technologies . https://doi.org/10.1007/s10639-023-11991-3 .

Download references

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author information

Han Weijing

Present address: Yunnan Technology and Business University, Kunming, 650000, Yunnan, China

Authors and Affiliations

College of Arts and Sciences · Kunming, Kunming, 650000, Yunnan, China

Fakulti Pengajian Pendidikan, UPM, Universiti Putra Malaysia, Seri Kembangan, 43400, Selangor, Malaysia

Ma Huiling, Lilliati Ismail & Han Weijing

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Ma Huiling .

Ethics declarations

Competing interest.

The author declare that they have no conflict interest.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Ma, H., Ismail, L. & Han, W. A bibliometric analysis of artificial intelligence in language teaching and learning (1990–2023): evolution, trends and future directions. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12848-z

Download citation

Received : 02 February 2024

Accepted : 05 June 2024

Published : 22 June 2024

DOI : https://doi.org/10.1007/s10639-024-12848-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Artificial intelligence
• Language learning
• Language teaching
• Bibliometric analysis
• Find a journal
• Publish with us
• Track your research

• Advanced Search

Data analysis of digital teaching resources and interactive behaviour between teachers and students based on K-means algorithm

School of Management, Guizhou University of Commerce, Guiyang 550014, Guizhou, China

ShanXi Optical Storage Information Industry Development Ltd., Jinzhong 030600, Shanxi, China

New Citation Alert added!

This alert has been successfully added and will be sent to:

You will be notified whenever a record that you have chosen has been cited.

To manage your alert preferences, click on the button below.

New Citation Alert!

Please log in to your account

• Publisher Site

International Journal of Information and Communication Technology

The focus of traditional research on teaching behaviour of teachers and students is mainly on identifying the expressions and behaviours of teachers and students, neglecting the analysis of interactive behaviour data between students and teachers. Effective recognition of teacher-student interaction behaviour through K-means algorithm, automatic recognition of classroom teacher-student behaviour using trained teacher and student behaviour recognition models, and analysis and statistics of paired and correlated teacher-student behaviour patterns in traditional classrooms through artificial intelligence technology, thereby promoting effective processing of teacher-student interaction behaviour data. Through experimental research, it has been verified that the method proposed in this article can accurately identify the interactive behaviour between teachers and students in smart teaching, effectively improving the effectiveness of teaching strategy formulation. Through research, it is known that combining intelligent algorithms for training and identifying teacher-student interaction behaviours is beneficial for improving educational and teaching modes, promoting teacher reflection, and promoting the development of educational and teaching informatisation reform towards a better path. In the future, continuous improvement can be made to the K-means algorithm to further promote the effective implementation of smart teaching.

Recommendations

Efl students’ perception of emergency remote learning: associations with teachers’ behavior, teaching content, learning process and learning achievements.

The COVID-19 pandemic requires EFL teachers to take charge of emergency remote teaching shortly. Understanding students’ perception of the online teaching and learning could help instructors optimize the virtual EFL lecture planning. This study aimed to ...

Improving hypermedia teaching resources – new designs for e-learning environments

This study aims to offer a set of empirically-based guidelines for the design of hypermedia teaching resources used in computer-mediated educational environments. These criteria are: incorporating additional tools to guide and aid navigation; limiting ...

Digital Technologies, Teachers' Competences, Students' Engagement and Future Classroom: ITEC Project

iTEC -Innovative Technologies for Engaging Classrooms- is a four-year project in which European Schoolnet is working with education ministries, technology providers and research institutions to transform the way that technology is used in teaching and ...

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

• Information
• Contributors

Published in

Copyright © 2024 Inderscience Enterprises Ltd.

This is an Open Access Article distributed under the CC BY license. ()

In-Cooperation

Inderscience Publishers

Geneva 15, Switzerland

Publication History

• Published: 13 June 2024

Author Tags

• K-means algorithm
• digitalisation
• teaching resources
• interactive behaviour
• research-article

Funding Sources

Other metrics.

• Bibliometrics
• Citations 0

Article Metrics

• 0 Total Citations View Citations
• 0 Total Downloads
• Downloads (Last 12 months) 0
• Downloads (Last 6 weeks) 0

This publication has not been cited yet

Digital Edition

View this article in digital edition.

Share this Publication link

https://dl.acm.org/doi/10.1504/ijict.2024.139107

Share on Social Media

• 0 References

Export Citations

• Please download or close your previous search result export first before starting a new bulk export. Preview is not available. By clicking download, a status dialog will open to start the export process. The process may take a few minutes but once it finishes a file will be downloadable from your browser. You may continue to browse the DL while the export process is in progress. Download
• Download citation
• Copy citation

We are preparing your search results for download ...

We will inform you here when the file is ready.

Your file of search results citations is now ready.

Your search export query has expired. Please try again.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.6(11); 2020 Nov

Multimedia tools in the teaching and learning processes: A systematic review

M.d. abdulrahaman.

a Department of Information and Communication Science, University of Ilorin, Ilorin, Nigeria

b Department of Telecommunication Science, University of Ilorin, Ilorin, Nigeria

A.A. Oloyede

N.t. surajudeen-bakinde.

c Department of Electrical and Electronics Engineering, University of Ilorin, Ilorin, Nigeria

L.A. Olawoyin

O.v. mejabi, y.o. imam-fulani.

d Department of Religions, Faculty of Arts, University of Ilorin, Ilorin, Nigeria

e Department of Mass Communication, University of Ilorin, Ilorin, Nigeria

Access to quality education is still a major bottleneck in developing countries. Efforts at opening the access to a large majority of citizens in developing nations have explored different strategies including the use of multimedia technology. This paper provides a systematic review of different multimedia tools in the teaching and learning processes with a view to examining how multimedia technologies have proven to be a veritable strategy for bridging the gap in the provision of unrestricted access to quality education and improved learners' performance. The review process includes conducting an extensive search of relevant scientific literature, selection of relevant studies using a pre-determined inclusion criteria, literature analysis, and synthesis of the findings of the various studies that have investigated how multimedia have been used for learning and teaching processes. The review examines various case study reports of multimedia tools, their success and limiting factors, application areas, evaluation methodologies, technology components, and age groups targeted by the tools. Future research directions are also provided. Apart from text and images, existing tools were found to have multimedia components such as audio, video, animation and 3-D. The study concluded that the majority of the multimedia solutions deployed for teaching and learning target the solution to the pedagogical content of the subject of interest and the user audience of the solution while the success of the different multimedia tools that have been used on the various target groups and subjects can be attributed to the technologies and components embedded in their development.

Education, Media in education, Teaching/learning strategies, Pedagogical issues, Systematic review

1. Introduction

Multimedia is a combination of more than one media type such as text (alphabetic or numeric), symbols, images, pictures, audio, video, and animations usually with the aid of technology for the purpose of enhancing understanding or memorization ( Guan et al., 2018 ). It supports verbal instruction with the use of static and dynamic images in form of visualization technology for better expression and comprehension ( Alemdag and Cagiltay, 2018 ; Chen and Liu, 2008 ). The hardware and software used for creating and running of multimedia applications is known as multimedia technology ( Kapi et al., 2017 ). Multimedia technology has some characteristics like integration, diversity, and interaction that enable people to communicate information or ideas with digital and print elements. The digital and print elements in this context refer to multimedia-based applications or tools used for the purpose of delivering information to people for better understanding of concepts.

Indeed, various aspects of human endeavours, especially the educational sector, are being transformed by the advent of Information and Communication Technology (ICT). ICT involves the use of hardware and software for the purpose of collecting, processing, storing, presenting, and sharing of information mostly in digital forms. Multimedia technology is an important aspect of ICT that deals with how information can be represented and presented digitally, using different media such as text, audio, video, among others ( Guan et al., 2018 ). It involves the combination of several technologies provide information in the best possible formats, packages, and sizes.

However, when used in the classroom or for educational purposes, the design quality and sophistication of multimedia application must be high enough to combine the different elements of the cognitive processes so as to achieve the best mimicking of the teacher. There are different types of multimedia applications available in the market today. These applications have been deployed for different educational purposes such as the works deployed for Mathematics classes, Social Sciences, Sciences, Physiology, Physics and Physical Education Studies ( Al-Hariri and Al-Hattami 2017 ; Anderson, 1993 ; Chen and Liu, 2008 ; Chen and Xia, 2012 ; Ilhan and Oruc, 2016 ; Jian-hua & Hong, 2012 ; Milovanovi et al., 2013 ; Shah and Khan, 2015 ).

The central problem, however, remains the same. Which is, the problem of how to use the applications to provide students with stimulating experience by delivering information for better understanding of concepts. While it is important to develop various applications for effective teaching delivery, each of these applications has its own focus area, peculiarities, target age, merits and demerits. Thus, the taxonomy and component synthesis for the development of the multimedia application need to be extensively investigated as these would affect the teaching delivery, learning and wider applicability. Some of the multimedia solutions have been deployed, tested and recorded significant success, while some did not record marginal success.

The success stories also vary with location, target age and deployment purposes. Therefore, the aim of this paper is to provide a systematic review of the scientific published studies that examined different multimedia tools in the teaching and learning process with a view to identifying the existing multimedia-based tools, understanding their usage, application areas and impacts on education system. In order words, the study, through a systematic review of literature, aims at identifying the existing multimedia-based tools for teaching and learning; understanding their usage and limiting factors, application areas, evaluation methodologies, technology components synthesis and impacts on education system.

To this end, the study is guided by the following research questions:

• (1) What are the existing multimedia tools in teaching and learning?
• (2) What type of multimedia component fits an audience?
• (3) What types of multimedia components are adopted in the existing tools?
• (4) What evaluation methodologies are useful for successful outcome?
• (5) What factors aid success or failure in the use of multimedia tools for teaching and learning?

The outcome of this study is aimed at serving as a guide for teachers and education administrators while selecting multimedia tools and applications for teaching in schools. So, in this study, the taxonomy and component synthesis of some widely cited multimedia applications are provided. Various case studies and results are examined. Furthermore, barriers limiting the usage of ICT and multimedia in teaching and learning are identified; and some unresolved cases and future research decisions are outlined.

The subsequent parts of this paper include Section 2 , which is the literature review that examines multimedia technology and its place in teaching and learning; Section 3 , the research methodology; Section 4 , presentation of results; Section 5 , discussion of the findings; and Section 6 , the conclusion, recommendations and suggestions for future work.

2. Literature review

2.1. multimedia learning and teaching: concepts and resources.

Multimedia or digital learning resources assist learners to get on well with mental representations with the use of different media elements, which support information processing. Information, which is made up of content and sometimes learning activities, are presented with the use of the combination of text, image, video and audio by digital learning resources. It has been demonstrated, by research on using multimedia for learning, that there are more positive results observed in learners who combine picture and words than those who use words only ( Chen and Liu, 2008 ; Mayer, 2008 ). As stated in Eady and Lockyer (2013) , different pedagogy methods were implemented by the use of digital resources. Their paper presented how the authors were able to introduce topics to students, demonstrate to them, stimulate a group, make different text types available and engage students in an interactive manner.

Generally speaking, multimedia technology for educational purposes can be categorised according to whether they are used for teaching or for learning. Some of the different multimedia or digital learning resources are listed in Eady and Lockyer (2013) . Furthermore, according to Guan et al. (2018) , several studies have established the importance of multimedia technologies to education and the widespread adoption of multimedia tools. Multimedia generally involves the use of technology and the widespread adoption of multimedia applications in education is as a result of its many benefits ( Almara'beh et al., 2015 ). Some of the benefits of the multimedia application tools for teaching and learning are summarized as follows:

• (1) Ability to turn abstract concepts into concrete contents
• (2) Ability to presents large volumes of information within a limited time with less effort
• (3) Ability to stimulates students' interest in learning
• (4) Provides teacher with the ability to know students position in learning.

Multimedia designed for learning refers to the process of building mental representation from words and pictures in different contexts. They are designed to assist learning with tools which can be used in presentations, class room or laboratory learning, simulations, e-learning, computer games, and virtual reality, thereby allowing learners to process information both in verbal and pictorial forms ( Alemdag and Cagiltay, 2018 ). Multimedia designed for learning requires understanding of some theories such as cognitive theory of multimedia learning, which postulates three assumptions that describe how people learn from instructional multimedia materials. These assumptions can be phrased as dual-channel, limited capacity, and active processing ( Alemdag and Cagiltay, 2018 ). Dual-channel assumes that learners have many channels to separate visual and auditory information. The restricted/limited capacity assumes that there is a limit to the load of data that can be processed in each channel. Understanding these will allow teachers not overwhelming learners with much information. On the other hand, learners will be aware of their information processing limitations or capabilities. Active processing proposes that when it comes to information selection, organization, and integration, human beings are active agents and are capable of managing the forms of information they are interacting with.

The appropriate use of ICT in teaching transforms the learning environment from teacher-centred to learner-centred ( Coleman et al., 2016 ) just as it is transforming all aspects of human life ( Guan et al., 2018 ). Coleman et al., (2016) emphasised that the shifting from teaching to learning creates a student-centred learning where teachers are there as facilitators and not sages on the stages, thus changing the role of the teacher from knowledge transmitter to that of a facilitator, knowledge navigator and a co-learner. Keengwe et al., (2008a) concluded that the application of multi-media technologies ensures a very productive, interesting, motivating, interactive and quality delivery of classroom instruction while addressing diverse learners' needs.

2.2. Role of multimedia technology in teaching and learning

Technology is evolving and scholars in the areas of Information Technology (IT) and education technology are continuing to study how multimedia technologies can be harnessed for the enhancement of teaching and learning. A software tool can be used to expand teaching and learning in various fields. It is important to provide students with practical experience in most fields of learning.

The importance of multimedia technologies and applications in education as a teaching or learning tool cannot be over emphasized. This has been confirmed in several studies that have investigated the impact of multimedia technology to the education system. Milovanovi et al. (2013) demonstrated the importance of using multimedia tools in Mathematics classes and found that the multimedia tool greatly enhances students' learning. Several works exist that show that multimedia enhances students' learning ( Aloraini, 2012 ; Al-Hariri and Al-Hattami, 2017 ; Barzegar et al., 2012 ; Chen and Xia 2012 ; Dalacosta et al., 2009 ; Jian-hua & Hong, 2012 ; Janda, 1992 ; Keengwe et al., 2008b ; Kingsley and Boone, 2008 ; Shah and Khan, 2015 ; Taradi et al., 2005 ; Zin et al., 2013 ).

Multimedia communication has close similarities to face-to-face communications. It is less restricted than text and ensures better understanding ( Pea, 1991 ). Multimedia technology helps simplify abstract content, allows for differences from individuals and allows for coordination of diverse representation with a different perspective. The use of the computer-based technique as an interface between students and what they are learning with suitable fonts and design can be very valuable.

Certainly, multimedia technology brings about improvement in teaching and learning, however, there are a number of limitations in this technology for educational purposes. Some of these limitations include unfriendly programming or user interface, limited resources, lack of required knowledge and skill, limited time and high cost of maintenance among others ( Al-Ajmi and Aljazzaf, 2020 ; Putra, 2018 ).

2.3. Multimedia evaluation techniques

Evaluation entails assessing whether a multimedia programme fulfils the purposes set including being useful for its target audience. Kennedy and Judd (2007) make the point that developers of multimedia tools have expectations about the way they will be used which could be functional (focused on the interface) or educational (involving the learning designs, processes and outcomes). It is important to note that there are different methods used in the evaluation of multimedia and most evaluations entail experiments, comparisons and surveys. The primary goal is to balance assessment validity with efficiency of the evaluation process ( Mayer, 2005 ).

Survey research has two common key features – questionnaires (or interviews) and sampling, and is ideally suited for collecting data from a population that is too large to observe directly and is economical in terms of researcher time, cost and effort when compared to experimental research. However, survey research is subject to biases from the questionnaire design and sampling including non-response, social desirability and recall and may not allow researchers to have an in-depth understanding of the underlying reasons for respondent behaviour ( West, 2019 ; Kelley et al., 2003 ).

Generally, comparison studies follow the format of comparing outcome from an experimental group using the multimedia being evaluated against a control group. This method has been criticised for having inadequate treatment definition, not specifying all treatment dimensions and failure to measure treatment implementation, among others ( Yildiz and Atkins, 1992 ).

Faced with the subjective nature of surveys and the limitations from comparison studies, eye tracking and other student behaviour such as emotional response, provides information not consciously controlled by the student or researcher and is used as an objective data gathering technique. Eye tracking research is a multi-disciplinary field that tracks eye movements in response to visual stimuli ( Horsley et al., 2014 ). Data from eye-tracking allows researchers to validate empirically and objectively, how learners comprehend the multimedia content, the attention of the learner while analysing the multimedia content, and the cognitive demand of the content ( Molina et al., 2018 ). Eye tracking is quite interesting as it provides a useful source of information in the case of children. This is because gathering information using the traditional techniques is more difficult especially when it involves children's interests and preferences ( Molina et al., 2018 ).

Earlier attempts at analysing student behaviour while engaging with online material included analysing student access computer logs, and the frequency of participation and duration of participation ( Morris et al., 2005 ). Nie and Zhe (2020) demonstrated that the conventional method of manually analysing student behaviour is gradually becoming less effective compared to online classroom visual tracking. They found that the online classroom visual tracking behaviour can be divided into several components: selection, presentation, mapping, analysis and collection, as well as the analysis from students' facial expression.

Several works exist that use student behaviour tracking to examine how students interact with multimedia learning tools. For instance, Agulla et al. (2009) , incorporated in a learning management system (LMS), student behaviour tracking that provided information on how much time the student spent in front of the computer examining the contents. They did so through the use of face tracking, fingerprint and speaker verification. Alemdag and Cagiltay (2018) conducted a systematic review of eye-tracking research on multimedia learning and found that while this research method was on the rise it was mainly used to understand the effects of multimedia use among higher education students. They also identified that although eye movements were linked to how students select, organise and integrate information presented through multimedia technologies, metacognition and emotions were rarely investigated with eye movements.

Molina et al. (2018) used eye-tracking in evaluating multimedia use by primary school children. Some studies have used a combination of eye tracking data and verbal data in order to gain insight into the learners' cognitions during learning and how they perceived the learning material ( Stark et al., 2018 ).

As much as eye-tracking and other behavioural research present opportunity for objective evaluation, difficulty of interpretation is one of the limitations of eye-movement data ( Miller, 2015 ), and it is not surprising that the traditional methods of evaluation through questionnaire administration and surveys are still commonly used.

3. Research methodology

This study adopted a research design that involves a searching method for identifying the articles to be reviewed for solving a specific research problem. It includes a systematic review of the article contents for analysis and synthesis. The systematic review follows the procedure outlined in the Preferred Reporting Items for Systematic Reviews and Meta-analysis for Protocol (PRISMA-P) 2015 guideline as provided in the work of Moher et al. (2015) , an extension of Liberati et al. (2009) . The guideline is to facilitate a carefully planned and documented systematic review in a manner that promotes consistency, transparency, accountability and integrity of review articles. Although it was originally developed for the analysis of health related studies, it is now widely adopted in other fields of study. Furthermore, the study involves protocol that includes identifying the data sources for the search, the keywords for the search and the inclusion criteria. To aid in synthesis of the identified articles, key points from the articles are summarised in tables and quantifiable components are analysed.

3.1. Data sources

The quality of a systematic review starts with the data sources used for identifying the articles to be selected for the review. This requires a thorough search and scrutiny of existing literatures from variety of academic databases and journals. The academic databases and journals considered for this review include Science Direct, IEEE Explore, ACM Digital library, Google Scholar, Springer, Wiley Online Library, Taylor & Francis, EBSCOHOST, Web of Science, and Scopus. These databases are reputable bibliographic sources and journals or conference papers indexed in them are deemed reputable and of good quality.

3.2. Search keywords

In order to ensure appropriate primary search terms are used and relevant papers are carefully selected for the review purpose, the literature search method of Kitchenham et al. (2009) was adopted. While it is expected that searching on a main string should be sufficient for the query output to collect all related papers, this is not the case always; hence the inclusion of substrings. Some problems associated with the databases used for the study are:

• • Some do not have automatic root recognition
• • Some have limitation of how many words to use e.g. IEEE, 15 words
• • Some databases offer advanced or expert search
• • ACM, IEEE and others do not have anything, not even a precedence rule.

The search terms for relevant literatures in the academic databases and journals specified in section 3.1 , are: “multimedia”, “multimedia technology”, “multimedia technology + Education”, “ICT impact + Education”, “multimedia tools + Education”, “multimedia + Teaching”, “multimedia + Learning”, “Application Software + Education”, and “Digital + Education”.

3.3. Inclusion and exclusion criteria

For the purpose given, each of the articles from the consulted academic databases and libraries had an equal chance of being selected. In order to avoid bias in the selection, a clear principle was set and adopted to form the criteria for inclusion of papers. These criteria are presented in Table 1 .

Table 1

Inclusion criteria of articles.

Thus, the queries using the stated search strings led to a pool of 10,972 articles in the subjects of interest that were online and written in English. All publications found as at the time of the search, which was in May 2019, were included. Publication date constraint for including a paper in the study was not applied. The process of screening this pool of 10,972 articles to meet the purpose of the study is outlined in the next section.

3.4. Exclusion from pooled articles

The number of articles from the database keywords search were reduced in line with the elimination procedure outlined as follows:

• i. elimination of paper based on unrelated title and elimination of duplications from various sources, leading to a reduction from 10,972 to 1,403;
• ii. examination of the abstracts of the 1,403 articles and reduction from 1,403 to 505;
• iii. elimination based on the direction of the article after reading through, leading to reduction from 505 to 78.

The elimination procedure is represented in Figure 1 which shows the flow of the procedure for screening the articles for the study.

Literature elimination process.

Table 2 provides a summary of the databases visited and the respective number of articles (from the final 78) that were obtained from that source.

Table 2

Search databases and number for articles.

Table 2 shows the percentage of the articles sourced from each academic database and reveals that Science Direct accounts for the highest number of the related articles with 25 (32%) papers, closely followed by Google Scholar 20 (26%) and IEEE Explore with 12 (15%) articles. Springer accounts for 8 articles, which represents 10% of the entire reviewed papers, while ACM Digital Library, Taylor and Francis, Web of science and EBSCOHOST contribute 4 (5%), 2 (3%), 4 (5%) and 2 (3%) respectively. The least paper is contributed by Wiley Online Library with one paper, which represents 1% of the entire papers reviewed for this study.

3.5. Data collection and synthesis of results

Based on the selection mechanism, 78 articles were shortlisted for analysis. Each article was reviewed and information extracted from it for tabulation. The information sought included the following: the type of multimedia tool used, the focus area of the tool, the technology that was deployed, the multimedia components used within the tool, how the tool was applied – whether for teaching or learning or both, the location where the tool was tested, and the target age on which the tool was tested. The researchers also tabulated impressions gleaned from the review in a “comments” column. If the tool was evaluated, then the evaluation methodology, target group, sample size, outcome, limitations of the methodology and whether or not the outcome could be generalized, were also presented.

In the next section, the insights from the articles reviewed are presented and some of the findings presented in tables for ease of analyses and synthesis.

After careful application of the procedures for selection as outlined in section 3 , each of the 78 shortlisted articles were subjected to a systematic review which involved extracting information as itemised in section 3.5 . Such information were tabulated for further analysis. Not all the articles were empirical based or contained the desired data items. Nineteen articles which were based on experimental work reported the details of the multimedia tool developed or deployed. Furthermore, 13 articles with details of the evaluation of the use of multimedia tools in teaching and learning were identified. Also revealed, were barriers to the use of multimedia. The findings from the systematic review are presented in this section.

The set of articles reviewed clearly emphasized the importance of multimedia technology to the improvement of teaching and learning environment. Several studies that have investigated the impact of ICT to education stated that multimedia technology has positive impact on the way teachers impart knowledge and the manner in which learners comprehend subject matters. The review also revealed that several multimedia-based tools exist, most of which are usually based on subject, field, age or level at various institutions of learning. In addition, some of the reviewed papers investigated the impact of teaching and/or learning with multimedia based instructional materials using descriptive, qualitative and quantitative research methods with different focus groups for both the pre-test and post-test conditions.

Nevertheless, despite the impact of multimedia tools on the improvement of teaching and learning activities, it could be counterproductive if the computer-based tools are not properly designed or the instructional materials are not well composed. The reviews showed that multimedia adoption in education requires adequate understanding of technology and multimedia types or components required to properly represent concepts or ideas. This implies that a teacher must understand the learners and know what technology or tool needs to be adopted at a given time for a set of targets. According to the reviews, the target groups determine the type of multimedia components employed while preparing instructional materials and the ways they are to be delivered. To provide context, a review of some of the analysed case studies are presented next.

Huang et al. (2017) explored the use of multimedia-based teaching materials that include three view diagrams (3D) and tangible 3D materials to teach 3D modelling course. This was aimed at determining the influence of multimedia technology in meta-cognitive behaviour of students. The authors employed lag sequential analysis as well as interview methods to examine the pattern transformation of students' meta-cognitive behaviour while solving problematic tasks. The evaluation results show that different teaching method and materials produce different meta-cognitive behaviours in student. The result further revealed that compare to traditional instructional instruments, using 3D tangible object in cognitive apprenticeship instruction stimulates more meta-cognitive behaviour. To teach an introductory course to control theory and programming in MATLAB, a video based multimedia guide was created by Karel and Tomas (2015) for distance learning students using Camtasia Studio 7 program. The software can record screen, edit video and create DVD menu. The impact of the multimedia aid tool was evaluated to be positive on the students based on the feedback.

Zhang (2012) created an online teaching and learning resource platform with interactive and integrated features. The platform was created with Macromedia Flash version 8.0, a form of Computer – Aided Drawing (CAD) software that is very easy to use. In an attempt to test student's professional cognition and operational skill cognition as well as learning satisfaction during learning phase, an experimentation technique that utilizes a non-equivalent pre-test and post-test control group was adopted. The evaluation revealed no significant difference between the groups in terms of professional cognition and operation skill cognition. However, it was noted that a significant difference exists in learning satisfaction, which shows a greater satisfaction in the coursework with multimedia Flash compare to that of the traditional learning method.

A web-based multimedia software is another popular educational tool designed to enhance teaching and learning. The major constraints of web-based learning are in its ability to provide personalised learning materials. Hwang et al., (2007) presented a web-based tool for creating and sharing annotations in their study. They then investigated the effect of the tool on learning using college students as a case study after four months of using the tool. The study concluded that there is value in understanding the use of collaborative learning through shared annotation. The paper also carried out a GEFT test on the students and concluded that there was no significant divergence between field – dependent and cognitive style students on the quantity of annotation. The paper also concluded that in the final examination, the tool provided a high motivation for students to study for their final exams.

Similarly, Bánsági and Rodgers (2018) developed a graphic web-based application in the educational sector for liquid – liquid extraction with the help of ternary phase diagram. The application allows chemical engineering students of the University of Manchester to draw liquid – liquid two – phase equilibrium curves and calculate mixture of phase separation among others. The application was put into use for testing purpose during which student usage figure as well as their opinions was sampled for both full – time taught and distance learning courses. The HTML 5, JavaScript, and Cascading Style Sheet (CSS) based application is interactive and easy to be used. In order to further analyse the web application developed, an iTeach questionnaire for the assessment of the efficiency of individual pedagogical approach was administered to students. The study revealed that students find the application useful as it has increased their level of understanding the course.

In order to teach students how to compose and continue delivering text based information in various media forms for current and emerging technologies, Blevins (2018) made students to search and analyse various multimedia technologies used in new media and capable of reflecting on their current and future works by adopting a scaffold project – based activities. The students were taught Augmented Reality (AR) software in a specific way with an assumption that such method will change next time students embark on AR project. After student's evaluation, the assumption was achieved even more than expected.

Ertugrul (2000) provided an overview of some lab view application software for teaching. The focus of the software was to seek for software use friendliness and compatibility faced by users. The paper provided recommendations for selection criterion. Even though the software applications have been found very useful and could compliment for conventional practical teaching particularly where there is shortage of laboratory facilities, the application is not suitable for engineering kind courses that requires hands on and intensive practical. Davies and Cormican (2013) identified the fundamental principles needed when designing a multimedia training tool or material for effective teaching and learning. The principles considered both students and an instructor's perspectives. Experiments were conducted in Ireland using a computer aided design (CAD) training environment. During data collection, mixed methods (i.e. interviews, surveys and a group discussion) were employed and findings showed that computer-based material is the most effective and popular way to learn. However, the costs, perceived lack of skill and insufficient support could be hindering factors.

The department of Computer Science in UiTMNegriSembian, developed three applications, namely, the Greenfoot, Visualization makes Array Easy (VAE) and e-TajweedYassin. The Greenfoot as a Teaching Tool in Object Oriented Programming is a tool that creates scenarios in order to ease visualization of 2D objects interaction in teaching object-oriented programming. The term “scenario” is used in Greenfoot to mean a project, and it has been used as a teaching aid for object-oriented programming (OOP) language introduction course. To ensure that a standard and quality application is built, the teaching aid was developed using System Development Life Cycle (SDLC). The Greenfoot-based scenario shows a great improvement in visualization and object element interaction and an impressive engagement of students during learning process. The application also provides clear illustration of object-oriented concepts to students and enabled them develop a game-like application from the scenario provided.

The Visualization makes Array Easy (VAE) on the other hand was created using the ADDIE model which is made up of Analysis, Design, Develop, Implement and Evaluate for instructional design. The analysis stage recognizes visualization technique as a key factor for enhancing students' understanding of programming concepts. The design stage of VAE took about a week to create a storyboard, while MS PowerPoint with i-Spring and Video Scribe formed the principal software for developing the application using storyboard as a guide. The VAE was instrumental in teaching students some hard programming concepts like Array. The results of the simple test with 60 students showed simulation technique of VAE to be effective in helping students to learn the concepts. To determine the effectiveness of VAE prototype, learnability, efficiency, memorability, accuracy and satisfaction of students were examined.

While the e-TajweedYaasin software was also developed using the ADDIE model (Analysis, Design, Develop, Implement and Evaluate) as an e-learning application, the tool was intended to aid students mastering tajweed and avoid common mistakes that were usually made by previous students who had undergone the course. During the analysis stage, visualization and interactive technique were recognised to be helpful in ensuring that students understand tajweed properly and are able to study with ease. The design stage involved the designing of the application layout with the focus on its easy accessibility to users. In addition, its user interface imitates the traditional teaching method called syafawiah. The development stage involved the use of MS PowerPoint with i-Spring features. The combination of audio, video and animation was more effective in comparison to text only in the promotion of learning. A sample of 51 students were selected to use the system and later, they were evaluated based on their ability to read the surah of Yaasin. A great improvement was observed as the number of mistakes had reduced to all the rules as students were enabled to better recognise and practice the tajweed for the surah of Yaasin ( Kapi et al., 2017 ).

Kapi et al. (2017) compared the effectiveness of three multimedia applications for effective teaching and learning. The applications considered were: Greenfoot Tool for programming; Visualisation Makes Array Easy (VAE) and e-TajweedYasin applications. The comparison looked into the design models used in meeting the desired instructional needs. Findings from the paper showed much more improved students' performance, learning and better understanding of subjects taught.

The advantages of using multimedia tools to teach Physics, which most students think is difficult, are enumerated in Jian-hua & Hong's (2012) work. They established that effective application of multimedia technology in university physics teaching can change the form of information, integrating graph, text, sound and image on PC, improving the expressive force of the teaching content so that the students can actively participate in multi-media activities via multi senses. High-quality university physics multimedia courseware is the best means to provide a variety of audio-visual images, which can show a lot of physical processes and phenomena vividly that is difficult by common means. The tool, especially, combines the advantages of multimedia courseware for university physics and that of traditional teaching of physics, and it greatly helped in improving teaching results of physics ( Jian-hua & Hong, 2012 ).

Two researchers developed a culturally responsive Visual Art Education module at the secondary level so as to assist the teachers to integrate and to implement a multicultural education in the teaching and learning practices at schools with the aim of enhancing students' knowledge and awareness regarding the elements of art and culture inherited by each race that makes up the multiracial society in Malaysia. Microsoft power point authoring tool was the technology with visual art materials including images and texts in a multimedia interactive teaching material for teaching 60 secondary school students, which resulted in accelerated teaching and learning processes with the IT skills of the teachers greatly improved ( Maaruf and Siraj, 2013 ).

Two control groups, pre-test and post-test, were selected for the implementation of a developed multimedia tool for 20 weeks. The tool, multimedia aided teaching (MAT) with text, audio, video and animation, was applied on 60 science students with age less than 15 years. The valid and reliable questionnaires were used as data collection tools. The Attitude Towards Science Scale (ATSS) was used to measure the attitude of both groups before and after treatments. The independent sample t-test was used to analyze the data. The results indicated that MAT is more effective than the traditional one. Students' attitude towards science improved with the use of MAT when compared to the traditional method of teaching ( Shah and Khan, 2015 ).

The effect of multimedia tools on the performance of 67 grade 4 students of social studies in Kayseri, Turkey was presented. Teaching tool with Computer representation with text, audio, video and animation as its components applied on a control group and an experimental group. The study concluded that academic performance of students in social studies was greatly improved when multimedia technique was applied as compared to traditional classroom ( Ilhan and Oruc, 2016 ).

Two samples of 60 senior secondary school II students in two different schools in Lagos State, Nigeria, were selected for the pre-test, post-test control group quasi experimental design in the research by Akinoso. Mathematics Achievement Test (MAT) with twenty-five questions from four topics namely: logarithm, percentage error, range, variance and standard deviation and circle theorems was the tool used. It was concluded that the students in the experimental group where multimedia tool was used performed better than those in the control group. It was equally inferred from the work that students' interest, motivation and participation increased according to the researcher and experimental group's teacher observations ( Akinoso, 2018 ).

Specifically, in the field of engineering, laboratory software applications can be used to provide an interface to providing practical alternatives to students depending on their requirement. Ertugrul (2000) provided a review of LabView software applications. The paper provided some knowledge about laboratory software tools used in the field of engineering and concluded that computer-based technology has advanced up to the stage where it can aid Engineering education at a significantly low price. The paper also highlighted some challenges faced by institutions in selecting and in the use of these software such as the need to upgrade software as the curriculum changes while also providing some future trends.

Zulkifli et al. (2008) examined a self-calibrating automated system for depressed cladding applications as they demonstrated utilizing the Laboratory Virtual Instrument Engineering Workbench (LabVIEW) software and General-Purpose Interface Bus (GPIB) interface. The presented model confirmed that the overall experiment time was reduced by 80% and data obtained is more accurate than caring out the experiment physically. Similarly, Teng et al. (2000) presented a Lab view as a teaching aid for use as power analyzer. The paper showed the tool allows for developmental speed to be accelerated as it is a connection between different workbench instruments.

The structured information extracted from the relevant reviewed articles are presented in the next sections. The systematic review enabled us to extract information from the reviewed articles on the type of multimedia tool the article described, what type of technology the tool deployed, what were the multimedia components utilized, and whether the tool applied to a teaching or learning scenario or both. Furthermore, results from articles reviewed for their evaluation studies are also presented including barriers to multimedia use.

4.1. Multimedia tools, technology, components and applications

The systematic review enabled us to extract information from the reviewed articles on the type of multimedia tool the article described, what type of technology the tool deployed, what were the multimedia components utilized, and whether the tool applied to a teaching or learning scenario or both. The results are presented in Table 3 .

Table 3

Summary of multimedia tools, technology, components and applications for education.

Various multimedia tools were identified in the research papers reviewed. Perhaps, owing to the advancement in multimedia technology, several applications have been developed and deployed to enhance teaching skills and learning environment in many fields of study. These include subject specific tools such as that for teaching and learning Mathematics ( Akinoso, 2018 ), the Chinese language ( Wu and Chen, 2018 ), Physics ( Jian-hua & Hong, 2012 ) and for teaching Social Studies ( Ilhan and Oruc (2016) . All the multimedia tools were developed for teaching except the CENTRA tool ( Eady and Lockyer, 2013 ) and the e-Tajweed Yaasin tool ( Kapi et al., 2017 ). Likewise, all the tools handled learning except the web-based application reported by Bánsági and Rodgers (2018) and the multimedia interactive teaching material ( Maaruf and Siraj, 2013 ).

The tools fell into two categories: standalone or web-based. One-third were web-based (36%) while 65% were standalone.

Technologies identified varied widely. Multimedia tools used included advanced technologies such as computer representation ( Akinoso, 2018 ; Aloraini, 2012 ; Ilhan and Oruc, 2016 ; Milovanovic et al., 2013 ) and augmented reality ( Blevins, 2018 ). High-level web design and programming software were also utilized. For instance, Bánsági and Rodgers (2018) and Hwang et al. (2007) utilized HTML 5, JavaScript and Cascading Style Sheet (CSS), which are software commonly used for web site programming. Camtasia Studio 7 software was used in the development of a video based multimedia guide for teaching and learning ( Karel and Tomas, 2015 ).

A commonly used web design and animation software, Macromedia Flash, was also identified ( Zhang, 2012 ). Object-oriented programming software was reported by Kapi et al. (2017) in the Greenfoot multimedia tool reported by them. Some low end technologies such as word-processing ( Eady and Lockyer, 2013 ) and presentation software ( Kapi et al., 2017 ) were also utilised. Other technologies reported include the use of e-book ( Wu and Chen, 2018 ), computer aided design (CAD) ( Davies and Cormican, 2013 ) and YouTube ( Shoufan, 2019 ).

As shown in Table 3 , several multimedia components were identified. These included text, audio, video, image, animation, annotation and 3D, with several of the multimedia tools combining two or more components. However, the incorporation of 3D was reported only by Huang et al. (2017) . All the analysed papers incorporated text in the multimedia tool reported, except in the tool, CENTRA ( Eady and Lockyer, 2013 ). Animation was also embedded as part of the multimedia tool developed for visualisation ( Kapi et al., 2017 ), for teaching Social Studies ( Ilhan and Oruc, 2016 ), engineering virtual learning tool ( Ertugrul, 2000 ), CAD ( Davies and Cormican, 2013 ), augmented reality ( Blevins, 2018 ) and in tool for teaching Mathematics ( Akinoso, 2018 ). Figure 2 shows the trend in educational technology based on year of publication of the reviewed articles. The figure reveals that while incorporation of audio and video became common as from 2012, 3-D makes its first appearance in 2017. This suggests that as new ICTs emerge educators are likely to try them in the quest for the best learning experience possible.

Educational technology trend based on year of publication.

4.2. Multimedia tools test location and target age

In this section, information on the location where the multimedia tool was tested and the target age of the study group are presented as summarised in Table 4 . The table also includes comments about the articles that could not be captured under any of the tabulation headings.

Table 4

Summary of multimedia tools for education study locations.

The multimedia tools tested were reported in studies from various countries, including Nigeria ( Akinoso, 2018 ), Saudi Arabia ( Aloraini, 2012 ), England ( Bánsági and Rodgers, 2018 ), Ireland ( Davies and Cormican, 2013 ), Australia and Canada ( Eady and Lockyer, 2013 ), Taiwan ( Huang et al., 2017 ), Turkey ( Ilhan and Oruc, 2016 ) Czech republic ( Karel and Tomas, 2015 ), Malaysia ( Maaruf and Siraj, 2013 ), Serbia ( Milovanovic et al., 2013 ), Pakistan ( Shah and Khan, 2015 ) and China ( Wu and Chen, 2018 ).

Various age groups were targeted by the multimedia tool tests. A considerable proportion involved university students with ages starting from 16 or 18 years as specified in the articles ( Bánsági and Rodgers, 2018 ; Huang et al., 2017 ); Hwang et al., 2007 ; Jian-hua & Hong, 2012 ; Kapi et al., 2017 ; Karel and Tomas, 2015 ). Another group targeted were secondary school students ( Akinoso, 2018 ; Maaruf and Siraj, 2013 ) including vocational school students ( Wu and Chen, 2018 ). Shah and Khan (2015) reported testing their multimedia tool on children below the age of 15 years.

4.3. Evaluation methods of multimedia technology tools in education

The articles involving evaluation were examined to identify the methodologies used for the evaluation, the target groups and sample of the evaluation and the evaluation outcome. The limitations of the evaluation were also identified and whether or not the study outcome could be generalized. Thirteen articles were found and the results are presented in Table 5 .

Table 5

Summary of Evaluation methods of multimedia technology Tools in education.

Evaluation of multimedia technology used for teaching and learning is important in establishing the efficacy of the tool. For determination of the impact of a developed tool, an experimental evaluation is more meaningful over a survey. However, the results from the analysis showed that the survey method for evaluation was used nearly as equally as the experimental design.

Experimental based evaluation was conducted by Akinoso (2018) , Aloraini (2012) , Ilhan and Oruc (2016) , and Shah and Khan (2015) in order to determine the effectiveness of the multimedia tool they developed. Another group of experimental evaluations involved designing the research for teaching with or without multimedia aids not necessarily developed by the research team which involved exposing 10–11 year olds ( Dalacosta et al., 2009 ) and elementary school students ( Kaptan and İzgi, 2014 ) to animated cartoons. Another of such evaluation was done by Milovanovi et al. (2013) , who used an experimental and control group to evaluate the impact of teaching a group of university students with multimedia.

In contrast, the survey method was used to elicit the opinion of respondents on the impact of the use of multimedia in teaching and learning and the target group were university students ( Al-Hariri and Al-Hattami, 2017 ; Barzegar et al., 2012 ), secondary school students ( Akinoso, 2018 ; Maaruf and Siraj, 2013 ); one involved interviewing the Professors ( Chen and Xia, 2012 ), another involved 4–10 year olds ( Manca and Ranieri, 2016 ) and a sample of 272 students whose ages were not specified ( Ocepek et al., 2013 ).

The focus areas in which the evaluations were conducted ranged from the sciences including mathematics ( Akinoso, 2018 ; Al-Hariri and Al-Hattami, 2017 ; Dalacosta et al., 2009 ; Kaptan and İzgi, 2014 ; Milovanovi et al., 2013 ) to the social sciences ( Ilhan and Oruc, 2016 ) and the arts ( Maaruf and Siraj, 2013 ). There were evaluations focused on education as a subject as well ( Aloraini, 2012 ; Chen and Xia, 2012 ; Maaruf and Siraj, 2013 ; Manca and Ranieri, 2016 ). While positive outcomes were generally reported, Ocepek et al. (2013) , specified that students in their evaluation study preferred structured texts with colour discrimination.

Sample sizes used in the studies varied widely, from Maaruf and Siraj (2013) that based their conclusions on an in-depth interview of teachers, to Manca and Ranieri (2016) that carried out a survey with a sample of 6,139 academic staff. However, the latter study reported a low response rate of 10.5%. One notable weakness identified was that the findings from all but one of the studies could not be generalized. Reasons for this ranged from inadequate sample size, the exposure being limited to a single lesson, or the sampling method and duration of the experiment not explicitly stated.

4.4. Identified barriers to multimedia use in teaching and learning

The review revealed some challenges that could be barriers to the use of multimedia tools in teaching and learning. Some of these barriers, as found in the reviewed articles, are highlighted as follows:

• • Attitudes and beliefs towards the use of technology in education. Findings from literatures and surveys have shown high resistant to change and negative attitude towards adoption and use of ICT in education ( Cuban et al., 2001 ; Said et al., 2009 ; Snoeyink and Ertmer, 2001 ). In some findings, some respondents perceived no benefits ( Mumtaz, 2000 ; Snoeyink and Ertmer, 2001 ; Yuen and Ma, 2002 ).
• • Lack of teachers' confidence in the use of technology and resistance to change ( Bosley and Moon, 2003 ; Fabry& Higgs, 1997 ; Said et al., 2009 ).
• • Lack of basic knowledge and ICT skills for adoption and use of multimedia tools ( Akbaba-Altun, 2006 ; Bingimlas, 2009 ; Cagiltay et al., 2001 )
• • Lack of access to computing resources such as hardware and software ( Akbaba-Altun, 2006 ; Bosley and Moon, 2003 ; Cinar, 2002 ; Mumtaz, 2000 ; Taylor and Todd, 1995 )
• • Lack of technical, administrative and financial supports ( Akbaba-Altun, 2006 ; Cinar, 2002 ; Said et al., 2009 ; Goktas et al., 2013 )
• • Others include lack of instructional content, basic knowledge and skills, physical environment and lack of time to learn new technologies ( Akbaba-Altun, 2006 ; Cinar, 2002 ; Said et al., 2009 ).

5. Discussion

The findings from the systematic review are discussed in this section with a view to answering the research questions posed. The questions bordered on identifying the existing multimedia tools for teaching and learning and the multimedia components adopted in the tools, the type of audience best suited to a certain multimedia component, the methods used when multimedia in teaching and learning are being evaluated and the success or failure factors to consider.

5.1. Multimedia tools in teaching and learning

The review revealed that multimedia tools have been developed to enhance teaching and learning for various fields of study. The review also shows that multimedia tools are delivered using different technologies and multimedia components, and can be broadly categorized as web-based or standalone.

From the review, it was found that standalone multimedia tools were more than twice (64%) the number of tools that were web-based (36%). Standalone tools are a category of teaching and learning aids which are not delivered or used over the internet, but authored to be installed, copied, loaded and used on teachers or students' personal computers (PCs) or workstations. Standalone tools are especially useful for teaching and practicing new concepts such as 3D technology for modelling and printing ( Huang et al., 2017 ) or understanding augmented reality (AR) software ( Blevins, 2018 ). Microsoft Powerpoint is a presentation tool used in some of the reviewed articles and is usually done with standalone systems.

Standalone tools were favoured over web-based tools probably because the internet is not a requirement which makes the tool possible to deploy in all settings. This means that teachers and students in suburban and rural areas that are digitally excluded, can benefit from such a multimedia tool. This system is considered most useful because a majority of the populace in most developing countries are socially and educationally excluded due to a lack of the necessary resources for teaching and learning. The need to sustainably run an online learning environment may be difficult, and therefore, the standalone, provides a better fit for such settings. However, the problem with a standalone application or system is the platform dependency. For instance, a Windows based application can only run on a windows platform. Also, there will be slow convergence time when there is modification in the curricular or modules, since, each system will run offline and has to be updated manually or completely replaced from each location where the tool is deployed.

The other category, web-based multimedia tools, are authored using web authoring tools and delivered online for teaching and learning purposes. About one-third of the tools identified from the review were web-based although they were used largely in university teaching and learning. Examples of these tools are: online teaching and learning resource platform ( Zhang, 2012 ), graphic web-based application ( Bánsági and Rodgers, 2018 ), multimedia tool for teaching optimization ( Jian-hua & Hong, 2012 ), and educational videos on YouTube ( Shoufan, 2019 ).

One of the benefits of the web based multimedia solution is that it is online and centralized over the internet. Part of its advantages is easy update and deployment in contrast to the standalone multimedia system. The major requirements on the teachers and learners' side are that a web browser is installed and that they have an internet connection. Also, the multimedia web application is platform independent; it does not require any special operating system to operate. The same multimedia application can be accessed through a web browser regardless of the learners' operations system. However, when many people access the resource at the same time, this could lead to congestion, packet loss and retransmission. This scenario happens often when large classes take online examinations at the same time. Also, the data requirements for graphics or applications developed with the combination of video, audio and text may differs with system developed with only pictures and text. Hence, the web based system can only be sustainably run with stable high speed internet access.

A major weakness of web-based multimedia tools is the challenge posed for low internet penetration communities and the cost of bandwidth for low-income groups. As access to the internet becomes more easily accessible, it is expected that the advantages of deploying a web-based multimedia solution will far outweigh the disadvantages and more of such tools would be web-based.

5.2. Components, technology and applications of multimedia tools in education

The results from the review revealed that most of the existing multimedia tools in education consist of various multimedia components such as text, symbol, image, audio, video and animation, that are converged in technologies such as 3D ( Huang et al., 2017 ), Camtasia Studio 7 software ( Karel and Tomas, 2015 ), Macromedia Flash ( Zhang, 2012 ), HTML5, JavaScript, CSS ( Bánsági and Rodgers, 2018 ; Eady and Lockyer, 2013 ; Chen and Liu, 2008 ; Shah and Khan, 2015 ; Shoufan, 2019 ). As shown in Figure 3 , the analysis confirms that text (26.8%) is the predominant multimedia component being used in most of the educational materials while other components such as videos (19.5%), audios (18.3%), images (18.3%) and animation (11.0%) are fairly used in teaching and learning multimedia materials. However, annotation and 3D technologies are least incorporated.

Proportion of multimedia components in reviewed articles.

How these components are combined is shown in Figure 4 . Perhaps, the combination of these four major components (text, video, audio, image) provides the best outcome for the learner and points to the place of text as a most desired multimedia component. The components used also reflect the type of subject matter being addressed. For instance, the audio component is important for language classes while video and image components are stimulating in Biology classes, for example, due to the need for visual perception for the learners. It is, therefore, imperative to note that the choice of the combination of these components could yield variable impacts to learners. Hence, it can be deduced from the studies that most of the tools are applied either as teaching or/and learning aids depending on the nature of the audience and teacher.

Use of various multimedia combinations.

In Figure 4 , we provided the analysis of the component combination of the data set reviewed. The multimedia components combinations range from two to six. This was grouped based on the multimedia components combination employed in each of the data set. Group 1 (G1) represents the number of multimedia application with the combination of Text, Image, audio, Video, and 3D. G2 consists of video and audio, while G13 combines all the multimedia components except the 3D.

Furthermore, a majority of the multimedia applications considered four (4) and two (2) combinations of components in their design as shown in Figure 5 . Tools with five and six components were very few and as the figure reveals, all the tools used at least two components.

Multimedia tools and the number of components utilized.

These findings stress the fact that application of multimedia tools in education and the multimedia component incorporated, are audience, subject, curricula and teacher-specific and the tool needs to be well articulated and structured to achieve its goals.

5.3. Targeted multimedia solutions

Our systematic review also revealed that most multimedia solutions deployed for teaching and learning target the solution to the pedagogical content of the subject of interest (see Table 4 ) and the user audience of the solution ( Table 5 ). Several studies highlighted in Tables  4 and ​ and5 5 showcase multimedia tools used for mathematics classes ( Akinoso, 2018 ; Milovanovi et al., 2013 ), Social science ( Ilhan and Oruc, 2016 ), Physiology ( Al-Hariri and Al-Hattami, 2017 ), Physics ( Jian-hua and Hong, 2012 ), in Chemical engineering ( Bánsági and Rodgers, 2018 ) and teaching of Chinese language ( Wu and Chen, 2018 ). In addition, multimedia tools were utilized for teaching specific principles such as in control theory ( Karel and Tomas, 2015 ) and teaching of arrays ( Kapi et al., 2017 ). That multimedia solutions are subject-based is not surprising given that multimedia involves relaying information using different forms of communication. It follows that multimedia solution developers need to incorporate some text, video, audio, still photographs, sound, animation, image and interactive contents in a manner that best conveys the desired content for teaching or to aid learning.

As stated earlier, the review revealed a variety of user types for the multimedia solutions reported. It is noteworthy that a large proportion of the studies where the target audience were university students, a mixture of graphics, text, audio, video and sometimes animation was utilized ( Aloraini 2012 ; Blevins, 2018 ; Huang et al., 2017 ; Shah and Khan, 2015 ). While a sizeable number of solutions were targeted at secondary school students (such as Maaruf and Siraj, 2013 , Kapi et al., 2017 , and Ilhan and Oruc, 2016 ), very few studies were identified that targeted students less than 15 years in age. Shah and Khan (2015) targeted a multimedia teaching aid that incorporated text, audio, video and animation. Perhaps the absence of multimedia tools targeted at very young children may be as a result of the inclusion criteria used for identifying articles for the review.

5.4. Success factors

The success of the different multimedia tools that have been used on the various target groups and subjects can be attributed to the technologies and components embedded as shown in Tables  4 and ​ and5. 5 . In most cases where text, audio, video, graphics and animations were the components of choice, significant improvements in teaching and learning are used, as reported in the studies reviewed ( Blevins, 2018 ; Huang et al., 2017 ; Zhang, 2012 ).

These studies also implemented technologies such as 3D modelling and printing; Macromedia flash version 8.0 and augmented reality (AR) software respectively. It is worthy of note that all the above-mentioned multimedia tools were applicable in both the teaching and learning processes. Another set of tools with components being text, audio, video and animation, excluding graphics, and equally applied in both the teaching and learning processes, adopted computer representation as their technologies ( Aloraini, 2012 ; Ilhan and Oruc, 2016 ; Milovanovic et al., 2013 ). Teaching and learning were equally greatly improved in these cases.

5.5. Evaluation methodologies

Our systematic review included a synthesis of the methodologies described by the reviewed articles for evaluating the multimedia tools that they present as shown in the summary in Table 5 . The evaluation methodologies appeared to be different depending on the type of multimedia tool, technology components, deployment strategies, and application area and target groups. However, two main evaluation methods were identified - experimental investigations and the survey methodology.

The experimental approach involved the use of an experimental group and a control group, where the assessment of the impact of the multimedia tool on the students' performance on the experimental group was compared with the performance of the control group who were taught the same content without the use of the multimedia tool. This experimental approach is a widely practiced evaluation method and has proven to be effective. It was deployed by Aloraini (2012) , Milovanovi et al. (2013) , Kaptan and İzgi (2014) , Shah and Khan (2015) , Ilhan and Oruc (2016) and Akinoso (2018) in their studies in the subject area of education, social sciences, general science, science, education and mathematics classes respectively.

Survey, as an evaluation approach which was used in 46% of the studies reviewed, involved the use of questionnaires that were administered to gather opinion on the perceived impact of the multimedia tool from a targeted group of respondents. From the systematic review, it was found that the questionnaire administration approach also varied. The data collection could be face-to-face interview ( Al-Hariri and Al-Hattami, 2017 ; Barzegar et al., 2012 ; Chen and Xia, 2012 ), or online survey ( Armenteros et al., 2013 ; Wang et al., 2020 ).

The difficulty of determining impact from a survey is related to the weaknesses associated with instrument design and sampling biases. It is our opinion that the perceived impact of the technology components used in the development of the multimedia tools may not be accurately ascertained using survey when compared with the actual deployment and experimentation with the multimedia tool that takes place in experimentation approach. Besides, in the survey approach, judgment is merely based on perceptions. Interestingly, the simplicity and ease of the survey method makes it a good option for evaluating larger target groups, and its findings can be generalised when the statistical condition is satisfied ( Krejcie and Morgan, 1970 ).

Although the evaluation studies analysed had publication dates as recently as 2015 to 2018, none reported any objective data collection such as from eye-tracking or other behavioural data. Perhaps, this may be due to our search keyword terms not being wide enough to identify multimedia evaluation studies that used objective data gathering. It could also be that the cost, time and effort needed to collect objective data means that many studies incorporating evaluation are avoiding this route.

5.6. Barriers to multimedia use in teaching and learning

Several barriers to multimedia use in teaching and learning were revealed as a result of the review. Such barriers include resistance to the adoption of ICT, lack of teachers' confidence in the use of technology, resistance to change on the part of teachers, a lack of ICT skills and lack of access to ICT resources. Other barriers identified were the lack of support, lack of time to learn new technologies, lack of instructional content, and the physical environment in which multimedia delivery took place. Some studies reported respondents that perceived no benefits from the use of multimedia. These barriers certainly affect both the integration of multimedia in teaching and learning and the uptake of the multimedia tool.

Most of the barriers identified could be classified into three groups with a major one being the fear or resistance to change. This means that change management must be an integral part of multimedia tools development and deployment in order to achieve the desired goal. Also, barriers such as lack of time and lack of resources should not be underestimated. Some of the studies reported providing the hardware for the multimedia application and such an approach should be considered. Most multimedia tools are ICT driven and as such the identified barrier of lack of ICT skills is an important aspect that must be addressed. This can be done as part of the change process and would also boost the confidence of teachers to incorporate multimedia for teaching.

It is important that the multimedia tool is designed and developed with the end-goal in mind. As indicated, some recipients of multimedia applications did not see any benefit in its use. This means that the multimedia tool should be designed to provide an experience that is worth the teachers and students' time, attention and effort.

6. Conclusions and future research direction

A lot of work has been done to highlight the effectiveness of multimedia as a teaching and learning aid. This paper provides a systematic review of studies on the use of multimedia in education in order to identify the multimedia tools being commonly used to aid teaching and learning. The paper did a systematic review of extant literature that reported studies that have been carried out to determine the extent to which multimedia has been successful in improving both teaching and learning, and challenges of using multimedia for leaning and teaching.

We note, however, that our review, especially of the studies on evaluation of multimedia, leaned more to the outcome from the studies rather than the process. Some of the information that was not captured include how the classroom teacher's mastery of the technology influences the attractiveness of the tool to the learner, both visually and through the content and if the multimedia tool allowed for learners' participation. Also, while studies on multimedia evaluation was of interest to us, this search phrase was not part of the search phrases used. A future review could incorporate these for a richer perspective.

It is obvious from the review that researchers have explored several multimedia in order to develop teaching and learning tools either based on the web or standalone using different technologies. It is observed that there exist several multimedia tools in education, but the proliferation of the tools is attributed to the evolvement of technologies over the years and the continuous teachers' efforts to improving knowledge delivery with respect to the subject areas and target audience. It is also revealed that most multimedia solutions deployed for teaching and learning target the solution to the pedagogical content of the subject of interest and the user audience of the solution. The success of the different multimedia tools that have been used on the various target groups and subjects is also attributed to the technologies and components embedded.

Furthermore, the evaluation methodologies and learning outcomes of the deployment of multimedia tools appeared to be different depending on the type of multimedia tool, technology components, deployment strategies, and application area and target groups. The two main evaluation methodologies identified from the various studies reported in the articles we reviewed were the experimental investigations and the survey methodology.

Attitudes and beliefs towards the use of technology in education, lack of teachers' confidence and resistance to change, lack of basic knowledge and ICT skills, lack of technical, administrative and financial supports, lack of physical environment are some of the barriers identified in the various articles reviewed. These barriers affect the integration of multimedia in education.

For future work, efforts should be made to explore mobile technology with several multimedia components in order to enhance teaching and learning processes across a diverse group of learners in the primary, secondary, vocational, and higher institutions of learning. Such research efforts would be significant in increasing inclusiveness and narrowing the educational divide. Also, research into the change management process for overcoming the barriers to multimedia adoption would be of interest.

Declarations

Author contribution statement.

All authors listed have significantly contributed to the development and the writing of this article.

Funding statement

This work was supported by Tertiary Education Trust Fund (TetFund), Ministry of Education, Federal Government of Nigeria 2016–2017 Institutional Based Research Grant.

Competing interest statement

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

• Agulla E.G., Rúa E.A., Castro J.L.A., Jiménez D.G., Rifón L.A. 2009 11th IEEE International Symposium on Multimedia. 2009. Multimodal biometrics-based student attendance measurement in learning management systems; pp. 699–704. [ Google Scholar ]
• Akbaba-Altun S. Complexity of integrating computer technologies into education in Turkey. Educ. Technol. Soc. 2006; 9 (1):176–187. [ Google Scholar ]
• Akinoso O. Effect of the use of multimedia on students' performance in secondary school mathematics. Global Media J. 2018; 16 (30):1–8. [ Google Scholar ]
• Al-Ajmi N.A.H., Aljazzaf Z.M. Factors influencing the use of multimedia technologies in teaching English language in Kuwait. Int. J. Emerg. Technol. Learn. 2020; 15 (5):212–234. [ Google Scholar ]
• Alemdag E., Cagiltay K. A systematic review of eye tracking research on multimedia learning. Comput. Educ. 2018; 125 :413–428. 2018. [ Google Scholar ]
• Al-Hariri M.T., Al-Hattami A.A. Impact of students' use of technology on their learning achievements in physiology courses at the University of Dammam. J. Taibah Univ. Med. Sci. 2017; 12 (1):82–85. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Almara'beh H., Amer E.F., Sulieman A. The effectiveness of multimedia learning tools in education. Int. J. Adv. Res. Comput. Sci. Software Eng. 2015; 5 (12):761–764. [ Google Scholar ]
• Aloraini S. The impact of using multimedia on students’ academic achievement in the College of Education at King Saud University. Kind Saud Univ. J. King Saud Univ. Lang. Transl. 2012; 24 :75–82. 2012. [ Google Scholar ]
• Anderson R.E. IEA Computers in Education Study, Department of Sociology, University of Minnesota. 1993. Computers in American schools 1992: an overview: a national report from the international IEA computers in education study. [ Google Scholar ]
• Armenteros M., Liaw S.S., Fernández M., Díaz R.F., Sánchez R.A. Surveying FIFA instructors' behavioral intention toward the multimedia teaching materials. Comput. Educ. 2013; 61 :91–104. [ Google Scholar ]
• Bánsági T., Jr., Rodgers T.L. Graphic web–apps for teaching ternary diagrams and liquid–liquid extraction. Educ. Chem. Eng. 2018; 22 :27–34. [ Google Scholar ]
• Barzegar N., Farjad S., Hosseini N. The effect of teaching model based on multimedia and network on the student learning (case study: guidance schools in Iran) Procedia Soc. Behav. Sci. 2012; 47 :1263–1267. 2012. [ Google Scholar ]
• Bingimlas K. Barriers to the successful integration of ICT in teaching and learning environments: a review of the literature. Eurasia J. Math. Sci. Technol. Educ. 2009; 5 (3):235–245. [ Google Scholar ]
• Blevins B. Teaching digital literacy composing concepts: focusing on the layers of augmented reality in an era of changing technology. Comput. Compos. 2018; 50 :21–38. [ Google Scholar ]
• Bosley C., Moon S. Centre for Guidance Studies, University of Derby; 2003. Review of Existing Literature on the Use of Information and Communication Technology within an Educational Context. [ Google Scholar ]
• Cagiltay K., Cakiroglu J., Cagiltay N., Cakiroglu E. Teachers’ perspectives about the use of computer in education. H. U. J. Educ. 2001; 21 (1):19–28. [ Google Scholar ]
• Chen H.Y., Liu K.Y. Web-based synchronized multimedia lecture system design for teaching/learning Chinese as second language. Comput. Educ. 2008; 50 (3):693–702. [ Google Scholar ]
• Chen S., Xia Y. Research on application of multimedia technology in college physical education. Procedia Eng. 2012; 29 (2012):4213–4217. [ Google Scholar ]
• Cinar A. METU; Ankara, Turkey: 2002. Teachers’ Computer Use at Basic Education Schools: Identifying Contributing Factors. Unpublished master’s thesis. [ Google Scholar ]
• Coleman L.O., Gibson P., Cotten S.R., Howell-Moroney M., Stringer K. Integrating computing across the curriculum: the impact of internal barriers and training intensity on computer integration in the elementary school classroom. J. Educ. Comput. Res. 2016; 54 (2):275–294. [ Google Scholar ]
• Cuban L., Kirkpatrick H., Peck C. High access and low use of technology in high school classrooms: explaining an apparent paradox. Am. Educ. Res. J. 2001; 38 (4):813–834. [ Google Scholar ]
• Dalacosta K., Kamariotaki-Paparrigopoulou M., Palyvos J.A., Spyrellis N. Multimedia application with animated cartoons for teaching science in elementary education. Comput. Educ. 2009; 52 (4):741–748. [ Google Scholar ]
• Davies W., Cormican K. An analysis of the use of multimedia technology in computer aided design training: towards effective design goals. Procedia Technol. 2013; 9 :200–208. 2013. [ Google Scholar ]
• Eady M.J., Lockyer L. Queensland University of Technology; Australia: 2013. “Tools for Learning: Technology and Teaching Strategies,” Learning to Teach in the Primary School; p. 71. [ Google Scholar ]
• Ertugrul N. Towards virtual laboratories: a survey of LabVIEW-based teaching/learning tools and future trends. Int. J. Eng. Educ. 2000; 16 (3):171–180. [ Google Scholar ]
• Fabry D., Higgs J. Barriers to the effective use of technology in education. J. Educ. Comput. 1997; 17 (4):385–395. [ Google Scholar ]
• Goktas Y., Gedik N., Baydas O. Enablers and barriers to the use of ICT in primary schools in Turkey: a comparative study of 2005–2011. Comput. Educ. 2013; 68 :211–222. [ Google Scholar ]
• Guan N., Song J., Li D. On the advantages of computer multimedia-aided English teaching. Procedia Comput. Sci. 2018; 131 :727–732. 2018. [ Google Scholar ]
• Horsley M., Eliot M., Knight B.A., Reilly R. Springer; Cham, Switzerland: 2014. Current Trends in Eye Tracking Research. [ Google Scholar ]
• Huang T.C., Chen M.Y., Lin C.Y. Exploring the behavioral patterns transformation of learners in different 3D modeling teaching strategies. Comput. Hum. Behav. 2017; 92 :670–678. 2017. [ Google Scholar ]
• Hwang W.Y., Wang C.Y., Sharples M. A study of multimedia annotation of Web-based materials. Comput. Educ. 2007; 48 (4):680–699. [ Google Scholar ]
• Ilhan G.O., Oruc S. Effect of the use of multimedia on students' performance: a case study of social studies class. Educ. Res. Rev. 2016; 11 (8):877–882. [ Google Scholar ]
• Janda K. Multimedia in political science: sobering lessons from a teaching experiment. J. Educ. Multimedia Hypermedia. 1992; 1 (3):341–354. [ Google Scholar ]
• Jian-hua S., Hong L. Explore the effective use of multimedia technology in college physics teaching. 2012 International Conference on Future Electr. Power Energy Syst. Explore. 2012; 17 :1897–1900. [ Google Scholar ]
• Kapi A.Y., Osman N., Ramli R.Z., Taib J.M. Multimedia education tools for effective teaching and learning. J. Telecommun. Electron. Comput. Eng. 2017; 9 (2-8):143–146. [ Google Scholar ]
• Kaptan F., İzgi Ü. The effect of use concept cartoons attitudes of first grade elementary students towards science and technology course. Procedia Soc. Behav. Sci. 2014; 116 :2307–2311. 2014. [ Google Scholar ]
• Karel P., Tomas Z. Multimedia teaching aid for students of basics of control theory in Matlab and Simulink. Procedia Eng. 2015; 100 :150–158. 2015. [ Google Scholar ]
• Keengwe S., Onchwari G., Wachira P. The use of computer toolsto support meaningful learning. Educ. Technol. Rev. 2008; 16 (1):77–92. [ Google Scholar ]
• Keengwe J., Onchwari G., Wachira P. Computer technology integration and student learning: barriers and promise. J. Sci. Educ. Technol. 2008; 17 :560–565. 2008. [ Google Scholar ]
• Kelley K., Clark B., Brown V., Sitzia J. Good practice in the conduct and reporting of survey research. Int. J. Qual. Health Care. 2003; 15 (3):261–266. [ PubMed ] [ Google Scholar ]
• Kennedy G.E., Judd T.S. Expectations and reality: evaluating patterns of learning behaviour using audit trails. Comput. Educ. 2007; 49 (3):840–855. [ Google Scholar ]
• Kingsley K.V., Boone R. Effects of multimedia software on achievement of middle school students in an American history class. J. Res. Technol. Educ. 2008; 41 (2):203–221. [ Google Scholar ]
• Kitchenham B., Brereton O.P., Budgen D., Turner M., Bailey J., Linkman S. Systematic literature reviews in software engineering–a systematic literature review. Inf. Software Technol. 2009; 51 (1):7–15. [ Google Scholar ]
• Krejcie R.V., Morgan D.W. Determining sample size for research activities. Educ. Psychol. Meas. 1970; 30 (3):607–610. [ Google Scholar ]
• Liberati A., Altman D.G., Tetzlaff J., Mulrow C., Gøtzsche P.C., Ioannidis J.P., Clarke M., Devereaux P.J., Kleijnen J., Moher D. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Ann. Intern. Med. 2009; 151 (4):65. [ PubMed ] [ Google Scholar ]
• Maaruf S.Z., Siraj S. The state of technology and the arts-interactive multimedia in enhancing culturally responsive pedagogy. Procedia Soc. Behav. Sci. 2013; 103 :1171–1180. [ Google Scholar ]
• Manca S., Ranieri M. Facebook and the others.Potentials and obstacles of social media for teaching in higher education. Comput. Educ. 2016; 95 :216–230. [ Google Scholar ]
• Mayer R.E. Cognitive theory of multimedia learning. Camb. handb. Multimed Learn. 2005; 41 :31–48. [ Google Scholar ]
• Mayer R.E. Applying the science of learning: evidence-based principles for the design of multimedia instruction. Am. Psychol. 2008; 63 (8):760–769. [ PubMed ] [ Google Scholar ]
• Miller B.W. Using reading times and eye-movements to measure cognitive engagement. Educ. Psychol. 2015; 50 (1):31–42. [ Google Scholar ]
• Milovanovic M., Obradovic J., Milajic A. Application of interactive multimedia tools in teaching mathematics--examples of lessons from geometry. Turk. Online J. of Educ. Technol.-TOJET. 2013; 12 (1):19–31. [ Google Scholar ]
• Moher D., Shamseer L., Clarke M., Ghersi D., Liberati A., Petticrew M., Shekelle P., Stewart L.A. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst. Rev. 2015; 4 (1):1. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Molina A.I., Navarro O., Ortega M., Lacruz M. Evaluating multimedia learning materials in primary education using eye tracking. Comput. Stand. Interfac. 2018; 59 :45–60. [ Google Scholar ]
• Morris L.V., Finnegan C., Wu S.S. Tracking student behavior, persistence, and achievement in online courses. Internet High Educ. 2005; 8 (3):221–231. [ Google Scholar ]
• Mumtaz S. Factors affecting teachers’ use of information and communications technology: a review of the literature. J. Inf. Technol. Teach. Educ. 2000; 9 (3):319–341. [ Google Scholar ]
• Nie Y., Zhe Y. On-line classroom visual tracking and quality evaluation by an advanced feature mining technique. Signal Process. Image Commun. 2020; 84 (May):115817. [ Google Scholar ]
• Ocepek U., Bosnić Z., Šerbec I.N., Rugelj J. Exploring the relation between learning style models and preferred multimedia types. Comput. Educ. 2013; 69 :343–355. 2013. [ Google Scholar ]
• Pea R.D. Learning through multimedia. IEEE Comput. Grap. Appl. 1991; 11 (4):58–66. [ Google Scholar ]
• Putra C.A. Utilization of multimedia technology for instructional media. J. ICT Educ. 2018; 5 :1–8. 2018. [ Google Scholar ]
• Said A., Lin L., Jim P. Barriers to adopting technology for teaching and learning in Oman. Comput. Educ. 2009; 53 :575–590. [ Google Scholar ]
• Shah I., Khan M. Impact of multimedia-aided teaching on students’ academic achievement and attitude at elementary level. US China Educ. Rev. 2015; 5 (5):349–360. [ Google Scholar ]
• Shoufan A. Estimating the cognitive value of YouTube's educational videos: a learning analytics approach. Comput. Hum. Behav. 2019; 92 :450–458. [ Google Scholar ]
• Snoeyink R., Ertmer P. Thrust into technology: how veteran teachers respond. J. Educ. Technol. Syst. 2001; 30 (1):85–111. [ Google Scholar ]
• Stark L., Brünken R., Park B. Emotional text design in multimedia learning: a mixed-methods study using eye tracking. Comput. Educ. 2018; 120 :185–196. [ Google Scholar ]
• Taradi S.K., Taradi M., Radic K., Pokrajac N. Blending problem-based learning with Web technology positively impacts student learning outcomes in acid-base physiology. Adv. Physiol. Educ. 2005; 29 (1):35–39. [ PubMed ] [ Google Scholar ]
• Taylor S., Todd P.A. Understanding information technology usage: a test of competing models. Inf. Syst. Res. 1995; 6 (2):144–176. [ Google Scholar ]
• Teng J.H., Chan S.Y., Lee J.C., Lee R. Vol. 1. 2000. A LabVIEW based virtual instrument for power analyzers; pp. 179–184. (2000 International Conference on Power System Technology. Proceeding s (Cat.No. 00EX409) ). [ Google Scholar ]
• Wang C., Fang T., Gu Y. Learning performance and behavioral patterns of online collaborative learning: impact of cognitive load and affordances of different multimedia. Comput. Educ. 2020; 143 :103683. [ Google Scholar ]
• West J. 2019. Data Collection. https://www.researchconnections.org/childcare/datamethods/survey.jsp Retrieved on 3 Sept 2020 from: [ Google Scholar ]
• Wu T.T., Chen A.C. Combining e-books with mind mapping in a reciprocal teaching strategy for a classical Chinese course. Comput. Educ. 2018; 116 (2020):64–80. [ Google Scholar ]
• Yildiz R., Atkins M.J. ERIC No. ED 350 978; 1992. How to Evaluate Multimedia Simulations: Learning from the Past. [ Google Scholar ]
• Yuen A.H., Ma W.W. Gender differences in teacher computer acceptance. J. Technol. Teach Educ. 2002; 10 (3):365–382. [ Google Scholar ]
• Zhang F. Significances of multimedia technologies training. Phys. Procedia. 2012; 33 :2005–2010. 2012. [ Google Scholar ]
• Zin M.Z.M., Sakat A.A., Ahmad N.A., Bhari A. Relationship between the multimedia technology and education in improving learning quality. Procedia Soc. Behav. Sci. 2013; 90 :351–355. 2013. [ Google Scholar ]
• Zulkifli M.Z., Harun S.W., Thambiratnam K., Ahmad H. Self-calibrating automated characterization system for depressed cladding EDFA applications using LabVIEW software with GPIB. IEEE Trans. Instrument. Meas. 2008; 57 (11):2677–2681. [ Google Scholar ]

By continuing to browse the site you are agreeing to our use of cookies and similar tracking technologies described in our privacy policy .

Supporting Educators & Students

Teaching & learning.

As part of its broad-based teaching mission, the AHA develops and shares resources for educators and students. From regional teaching conferences and online programs to pathbreaking research projects, AHA initiatives foster a community grounded in our shared commitment to understanding the past. We support and convene people who share a love of history and historical thinking.

Resources for Educators & Students

K–12 Education

The AHA strives to ensure that every K–12 student has access to high quality history instruction. We create resources for the classroom, advise on state and federal policy, and advocate for the vital importance of history in public education.

Undergraduate Education

Teaching and learning are at the foundation of the AHA’s mission to promote historical thinking in public life. What do students learn in undergraduate history courses? How and why are history majors so successful in a variety of careers?

Graduate Education

Many historians will pursue graduate training at some stage in their career. To meet the needs of both students and graduate programs, the AHA creates resources, provides platforms, and convenes conversations about student success from application to completion.

For Academic Departments

History department chairs are on the front lines of the discipline, defending historians’ work and supporting their professional lives at all stages of their academic careers. The AHA strives to strengthen this work and provide resources and opportunities that make chairs’ work easier and valued. The AHA provides resources and hosts a variety of events and opportunities to benefit department chairs and build community, including webinars, sessions at the annual meeting, and an in-person workshop.

Current Events in Historical Context

Essential, carefully researched resources by historians providing context for conversations about current events.

Regional Conferences on Introductory History Courses

What do students learn in introductory history courses? How can historical thinking support student learning and success across the curriculum? Our regional conferences endeavor to strengthen the community of practice focused on introductory history courses, both in secondary and higher education.

Standards & Guidelines

June 10, 2024

Guidelines for Academic Tenure-Track Job Offers in History

June 9, 2024

Statement on Age Discrimination

Aha historical collections.

The AHA has made primary sources available for research purposes, along with AHA archival reports and documents.

Vetted Resources

Vetted Resources compiles in a central location materials and tools that have been professionally vetted by historians, offering instructors access to high-quality materials that meet professional standards

AHA Resource Library

June 20, 2024

16 Months to Sumter: Newspaper Editorials on the Path to Secession

June 16, 2024

The History of Racism and Racist Violence: International Contexts and Comparisons

The history of racism and racist violence: monuments and museums, join the aha.

The AHA brings together historians from all specializations and all work contexts, embracing the breadth and variety of activity in history today.

Alternatives to Traditional Testing

For many courses of varying format and size, across many disciplines, reasonable alternatives to traditional tests (i.e., paper-based T/F or Multiple Choice) exist. In fact, oftentimes the alternatives may even be advantageous to promote student learning and be more authentic means of students demonstrating what they have learned at the higher levels of Bloom's Taxonomy (synthesis, analysis, evaluation). All such courses should, however, include appropriate summative evaluation activities per COCI policy on (alternatives to) final exams:

• The University of California's Academic Senate maintains regulations on final exams .
• the requirement to have a final exam (or alternative method of final assessment )
• the process to request a change to your assigned final exam group (determining the day and time of the exam)
• More details can be found in the COCI Handbook regarding the approval of new (and changes to existing) courses .

Alternative Assessment Types

Paper instead of test.

A standard alternative to a test, the paper can take many forms. Make sure that the paper is integral to the course and not simply an add-on. One way to accomplish this, to help students write better, and to encourage academic integrity is to give the assignment early and ask for portions of the paper to be turned in at intervals: preliminary topic, outline, bibliography, draft, and so on. And ask students to include all drafts and notes along with the paper.

A series of quizzes or chapter tests instead of comprehensive, high-stakes tests

Unless there is a solid pedagogical reason for a comprehensive, high-stakes test (i.e., midterm), you might consider a series of shorter tests throughout the semester. You can always add one or two questions relating to previous units in the course. Remember, though, a comprehensive final assessment is still required in most courses per COCI policy .

Memorandum or briefing

Students prepare a one or two page memorandum or briefing, with, for example, the following headings: background, problem, possible solutions with pros and cons, final recommendation (and you can add as you like, for instance, final recommendation with implications, possible impact, and so on). Besides being a good exercise in synthesizing material, it’s an excellent way for students to practice being concise and direct.

Professional presentation

Many courses lend themselves to presentations of the kind that a professional consultant would provide to a community group or some kind. For example, in Architecture and City and Regional Planning, students often present their projects to a simulated “community board.” The presentation could be applicable to many fields, in the form of an expert witness presenting material. One variant: Local library board. Make a presentation arguing for the inclusion of certain books in the library, based on the reading for the semester. Applicable to many different disciplines.

Annotated Anthology or course reader

Students prepare a selection of works they have read during the term as a thematic anthology—they create the theme, choose the works, write a paragraph introduction to each, and an introduction to the anthology. (If the works themselves are short, e.g., poems, they should be included). For longer pieces, just a table of contents, the introduction, and the introduction to each piece. Of course students will also have to think about order. Katherine Snyder of English has used such an assignment as part of a final exam, but it could be easily adapted for use as an in-course assignment.

The course reader exercise works essentially the same way, but in this case, students have to organize the readings chronologically to develop the theme they have created for the course. This assignment can be made as complex as you wish, by asking for such things as assignments to go with the readings, suggestions for further reading, and so on.

Poster Sessions (with peer critique)

This is applicable to many different kinds of classes. Chemistry 1A has used it quite successfully in large classes for several years. Here is a description of the assignment developed by Michelle Douskey:

“The goal of the project is to help each student link the material covered in class to everyday products and processes by asking and answering key chemistry questions that get at the heart of the topic.  Students must pick a topic from a given list, develop a hypothesis, and perform library research to support or refute their hypothesis. The students present their research during a poster session during the last lab period. The scaffolding focuses on two main aspects of the project; support for the students and support for the GSIs. The GSIs are trained to assist the students in the refinement of their hypothesis and in the search for appropriate sources of information. Students are given a topic list, an example poster, the grading rubric and a proscribed feedback mechanism with the GSI. The clear timeline and implementation strategies help the students to be successful in pushing their understanding of chemistry. When polled in the Spring 2005 semester, 84% of the students stated that the project increased their ability to apply chemistry to things beyond the textbook.”

Annotated portfolio of work throughout the term

Portfolios in place of a test have been used for a number of years in the College Writing Programs. Students compile their best or representative work from the term, write a critical introduction to the portfolio and a brief introduction to each piece.

Annotated research bibliography with introduction

Rather than ask students to write a research paper, ask them instead to compile a bibliography on a problem or question. In essence they do everything but write the paper. They must read the works, evaluate their accuracy and helpfulness, and provide an explanatory introduction to the bibliography (from Anna Livia Braun, French). Each entry contains an explanatory and/or evaluative paragraph. Students can also compare the relative usefulness of sources, authors’ points of view, biases, and so on.

Developed by Barbara Abrams of Public Health, a Fact Sheet is a more demanding assignment than it first appears to be, and would be relevant to other courses. Such a fact sheet would be intended to be distributed to the public in relevant places. While Abrams’ fact sheets deal with health issues (smoking, HIV, etc.), other applications might be in economics or sociology (school board budgets or trends in enrollment), history or political science (fact sheet on the 1960 Presidential Election), engineering (fact sheet on the new Bay Bridge). Students must learn to search the relevant databases for the discipline, evaluate material, and present it in a concise, readable way. 

Reflective paper

If the class is experiential in nature (e.g., student teaching, performance), ask the students to write a reflective paper/critique of their experience. The key here is to make them tie this to theory or themes in the course so that it doesn’t become an effusion of personal feeling.

Even in non-experiential/performance courses, a reflective paper can be very useful. Some classes ask students to add a reflection to a term paper. 

Op-Ed piece to be sent to local newspaper

The Op-Ed piece is a “real world” exercise that requires not only a thorough understanding of both sides of an issue, but an ability to understand the audience.

Student-Proposed Project

Students, at a predetermined point in the class and with specific conditions tied to it to ensure it will represent their learning as related to the course goals, may have the option of suggesting a course project that they would like to undertake.