acquisition of new knowledge in research

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• Published: 19 August 2021

Toward an understanding of when prior knowledge helps or hinders learning

• Garvin Brod   ORCID: orcid.org/0000-0002-7976-5609 1 , 2  

npj Science of Learning volume  6 , Article number:  24 ( 2021 ) Cite this article

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Knowledge begets knowledge—or so they say? David Ausubel 1 has famously described prior domain knowledge as the most important determinant of a student’s learning success, “ascertain this and teach him accordingly”. Indeed, prior knowledge—previously learned information organized in a learner’s memory 2 —has long been known to explain large portions of variance in learning outcomes 3 . A new meta-analysis by Simonsmeier et al. 4 suggests that this is not the end of the story, however. By distinguishing learning outcomes from learning gains, the new meta-analysis found that prior knowledge indeed explained large portions of variance in learning outcomes, but it did not—on average—explain variance in learning gains. The former result indicates that those students in a class who know the most at the beginning of a class will likely know the most at the end as well. The latter result is an eye-catching one, as it suggests that knowledge at the beginning of a class does not, in fact, determine how much a student will learn from a particular task or instruction. Does this finding contradict the famous statement by Ausubel? At the very least, as put by Simonsmeier et al., it “calls for systematic research on the conditions under which prior knowledge has positive, negative, or negligible effects on learning” 4 . I wholeheartedly agree that this is of outmost importance for both theory construction and educational practice, and I applaud the authors of the meta-analysis for bringing this topic to the fore. The goal of this brief commentary is to provide initial pointers for systematic research on the factors that determine whether and how prior knowledge affects learning.

Whether and how prior knowledge exerts an influence on learning certainly depends on the prior knowledge itself. The different dimensions of prior knowledge and their effect on learners’ text comprehension have been thoroughly described in a recent article 5 , which identified four important dimensions: amount, accuracy, specificity, and coherence (for earlier work on mapping and defining prior knowledge, see refs. 6 , 7 ). The existence of different dimensions of prior knowledge makes it clear that the effect that prior knowledge has on learning depends on more than just having more or less knowledge available. As will be further explained below, however, looking only at the knowledge itself is not sufficient, as even large amounts of correct, specific, and coherent prior knowledge can be unused.

In this short commentary, I will argue that there are several determinants for whether and how prior knowledge affects learning. Put differently, the identical prior knowledge can steer learning differently in different learning tasks and can thus both help and hinder learning. I will focus on three determinants: (1) whether prior knowledge is activated (i.e., information is retrieved from memory), (2) whether it is relevant for the learning task at hand, and (3) whether it is congruent or incongruent with the to-be-learned content. These three determinants can be put in a hierarchical relation (see Fig. 1 ), which illustrates that, for example, relevance of the prior knowledge only becomes important when this knowledge is activated. Note that this means that the determinants considered here are all acting on an intra-individual level. Inter-individual and environmental determinants such as participants’ age or the duration of the intervention, which are covered in the meta-analysis as well, are not considered. While the different determinants are presented as dichotomies, these dichotomies are meant as the extreme ends of one scale/dimension. There is substantial evidence that each of the dimensions has a strong impact on learning, which I will elaborate on in the following.

Note that, while the different determinants are presented as dichotomies, these dichotomies are meant as the extreme ends of one scale/dimension.

Determinants of the effects of prior knowledge on learning

To have any effect on learning whatsoever, prior knowledge needs to be activated first. That is not trivial, as has been illustrated in a seminal study by Bransford and Johnson 8 . Participants had to read a description of an activity of which all participants could be assumed to have substantial prior knowledge (i.e., washing clothes). Some participants received a hint (i.e., the topic of the passage) beforehand that enabled the activation of relevant prior knowledge, whereas other received the hint afterward or not at all. Comprehension ratings and recall performance were strongly enhanced in participants who were given the hint beforehand, which provides evidence that available prior knowledge needs to be activated in order to affect learning. The phenomenon that prior knowledge is not activated by learners has been particularly well researched in children, who display characteristic deficiencies in using their prior knowledge strategically, which hampers their learning performance 9 , 10 , 11 . In summary, it is not sufficient that prior knowledge is available but it also has to be activated and used to steer the learning process.

Even if some prior knowledge gets activated by learners, it has to be relevant for the learning task at hand to have a beneficial effect. Research on the so-called “Baker–baker paradox” 12 illustrates the importance of this dimension. The paradox describes the finding that remembering a face–name association (i.e., that a person’s surname is Baker) is disproportionately harder than remembering face–profession associations (i.e., that a person’s profession is baker). In the case of face–name associations, even if participants knew someone with the surname Baker and activated this prior knowledge, they had a hard time to leverage it to connect this surname to a new face in a meaningful way because of the arbitrariness of the association 13 . Their activated prior knowledge is, thus, largely irrelevant for the learning task at hand. In contrast, in the case of profession–name associations, prior knowledge is relevant because a common profession activates a large knowledge network that participants reported to use to associate the profession with the face (e.g., by imagining the face with a baker’s hat or by evaluating facial characteristics as to whether these fit with their idea of a baker) 13 . In summary, research on the “Baker–baker paradox” shows that knowledge can be more or less relevant in a particular learning context.

Activated irrelevant prior knowledge can even hamper learning. Research on memory intrusions suggests that large amounts of correct prior knowledge in a domain can have detrimental consequences for learning in this domain because it can induce perceptual biases and intrusions. For example, football experts who studied lists of animal names that were also names of football teams later falsely recalled many non-presented animal names that represented football teams. This resulted in a higher number of falsely recalled words in experts than in non-experts, a pattern that was absent in a control task in which participants had to study body parts 14 . Higher (activated) prior knowledge can thus lead astray when it is irrelevant for the learning task at hand.

Even if prior knowledge is activated and relevant, it has to be congruent (i.e., in agreement) with the to-be-learned information to have an unequivocal beneficial effect. Congruency describes the fit or agreement between prior knowledge and new information. A wealth of memory research has demonstrated that new information that is congruent with prior knowledge tends to be better remembered than information this is incongruent with it; a phenomenon that has been dubbed the memory congruency effect and that has been suggested to arise from a more elaborate memory trace that is laid down during encoding and facilitated memory search during retrieval 15 , 16 , 17 . While the memory congruency effect suggests that higher congruency between prior knowledge and new information typically goes along with better learning of the new information, the opposite can for once be true as well. Highly incongruent new information has been shown to be learned well, but only when it leads to strong surprise in learners 18 .

Research on conceptual change has dealt extensively with different types or degrees of (in)congruency that exist between prior knowledge and new information and the way that this affects whether prior knowledge is helping or hindering learning. These differences range between what has been called the enrichment kind and the radical conceptual change kind 19 , which point to differences in the kind of change in the learner’s knowledge structures that is necessary to incorporate the to-be-learned information. These differences can be illustrated by research on the acquisition of taxonomic knowledge 20 . Consider a taxonomy of the concept “whale”. Learning (a) that whales can be found in the Arctic Ocean, (b) that orcas (killer whales) belong to the family of dolphins, and (c) that whales are mammals are all likely to be new to most learners. However, these three arguably differ in the kind of change in the learner’s taxonomy that is necessary to incorporate the to-be-learned information 20 : (a) can be fairly easily integrated because it does not require larger reorganization (i.e., enrichment of the existing taxonomy); (b) requires differentiation, which goes along with a reappraisal of which attributes of a concept are primary and which are secondary; (c) requires large-scale reorganization of the hierarchy, that is, shifting of a concept from one branch to another in the taxonomy. To conclude, the greater the necessary reorganization of the prior knowledge, the more difficult it will be for learners to acquire new information.

The complex relation between prior knowledge and learning success

The goal of this commentary is to provide initial pointers for systematic research on the factors that determine whether and how prior knowledge affects learning. I have suggested that there are at least three important determinants that need to be taken into account besides the structure of the prior knowledge itself: whether prior knowledge is activated, whether it is relevant, and whether it is congruent with the to-be-learned information. While there is no simple one-to-one mapping between those determinants and learning success, they do provide a glimpse into the complexity of the relation between prior knowledge and learning success. Figure 1 illustrates that there are many contexts in which prior knowledge will likely not be beneficial for learning. Only when it is activated, relevant, and congruent will prior knowledge reliably help. When it is not activated at all, the prior knowledge a learner brings to the learning task will have negligible effects on learning outcomes. When the prior knowledge that is activated is irrelevant for the task at hand, its effects will either be negligible or they will even hinder learning because of intrusions or biases. Prior knowledge that is activated, relevant, and incongruent with the to-be-learned information will mostly hinder learning, too (see, e.g. 21 ).

Figure 1 also illustrates why the overall effects reported in the meta-analyses by Simonsmeier et al. 4 , which suggest a wide distribution of effect sizes that centers around zero, might not be that surprising after all. While the meta-analysis covered an impressive number of inter-individual and environmental moderators of the effect of prior knowledge on learning gains, it did not explicitly consider the determinants described in the current article except for the knowledge itself. While it can be assumed that most of the knowledge that was assessed during pretests was relevant for the learning task at hand, it is unclear whether this knowledge was activated by the learners during the task and how congruent it was with the to-be-learned content. Of note, the meta-analysis did include studies that have targeted incongruent prior knowledge by measuring the amount of incorrect knowledge regarding the to-be-learned concept (i.e., misconceptions). None of these studies reported the correlation with learning gains, however, which impeded further analyses.

Coming back to the initial question: does the famous statement by Ausubel 1 , according to which prior knowledge is the most important determinant of a student’s learning success, need to reconsidered given the findings of the meta-analysis? Not so fast. First of all, one has to say that he did not say that prior knowledge will always be helpful, but only that it will be a strong determinant of what a student will learn in a lesson. In a similar vein, Simonsmeier and colleagues 4 suggest that the different ways in which prior knowledge affects learning, while all important, might sometimes cancel each other out. I will thus conclude by saying that it remains of outmost importance to assess a learner’s prior knowledge before teaching her some new content. Unraveling the systematics of whether and how this prior knowledge then steers the learning process is food for future research.

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Acknowledgements

G.B. has been supported by a Jacobs Foundation Research Fellowship.

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Brod, G. Toward an understanding of when prior knowledge helps or hinders learning. npj Sci. Learn. 6 , 24 (2021). https://doi.org/10.1038/s41539-021-00103-w

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• Published: 08 November 2019

The effect on new knowledge and reviewed knowledge caused by the positioning task in closed concept maps

• Pedro Gabriel Fonteles Furtado   ORCID: orcid.org/0000-0003-2397-4716 1 ,
• Tsukasa Hirashima 1 &
• Yusuke Hayashi 1  

Research and Practice in Technology Enhanced Learning volume  14 , Article number:  15 ( 2019 ) Cite this article

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The advancement of technology has made it possible for automated feedback to be added to learning activities such as the construction of concept maps. The addition of feedback allows learners to acquire new knowledge instead of only focusing on reviewed knowledge. The cognitive processes for acquiring new knowledge and reviewing knowledge are different, so the benefits of concept maps in past research may not apply to the acquisition of new knowledge. However, how concept map construction varies across these two aspects has not been investigated. This research starts this investigation by researching how the positioning task affects new knowledge and reviewed knowledge. The position task is the act of deciding and managing the position of the elements of the concept map. In this paper, we study the differences in new knowledge and reviewed knowledge across two closed concept map interfaces by comparing test answers. One interface, Kit-build, includes the positioning task. The other interface, Airmap, does not include it. Results suggest that the interfaces only differ in retained reviewed knowledge, having similar performance in immediate new knowledge, immediate reviewed knowledge, and retained new knowledge. Results have potential implications for the general presence of the positioning task in learning interfaces.

Introduction

A concept map is a graphical tool used to represent knowledge. It is composed of concepts and of links between the concepts ( Cañas and Novak 2010 ). Concept maps are believed to improve reading comprehension ( Usman et al. 2017 ; Riahi and Pourdana 2017 ; Chang et al. 2002 ; Sánchez et al. 2010 ; Guastello et al. 2000 ). It is theorized that concept maps improve reading comprehension through the continuous processing of content ( Armbruster and Anderson 1984 ), by providing a template to help structure and understanding the information ( Cañas and Novak 2010 ), and by being close to the macrostructure of the text ( Van Dijk et al. 1983 ).

Computer-based tools for building concept maps are popular ( Liu et al. 2010 ; Reader and Hammond 1994 ; Anderson-Inman and Zeitz 1993 ). Among these tools, some of them provide the nodes and links so that the user only has to assemble the map ( Hirashima et al. 2011 ; Wu et al. 2012 ). They are called closed concept map tools because the set of possible maps is finite. This allows for automated scoring of the maps by comparing the built map to an expert map. This mechanism also allows for automated feedback ( Pailai et al. 2018 ; Furtado et al. 2018 ). Since closed concept maps provide information to the user, they can go beyond the reviewing activities that traditional concept maps provide. Closed concept maps with automated feedback can help students acquire new knowledge ( Pailai et al. 2018 ; Furtado et al. 2018 ). While there is research on the reviewing aspects of concept maps ( Cañas and Novak 2010 ; Armbruster and Anderson 1984 ; Van Dijk et al. 1983 ), no research so far has investigated if the benefits of concept maps also transfer to the acquisition of new knowledge through automated feedback.

One matter of concern in closed concept maps is the effort of organizing the pieces in the concept map. We call this the positioning task. This is one portion of the activity that can be automatized and thus lower the effort of building the map. Past research has shown that the effort related to building the map is lowered by automatizing the positioning task ( Furtado et al. 2018 ). Also, tests done after building the map show that immediate reading comprehension is not affected by automatizing the task. However, tests done after 2 weeks suggest that automatizing the task negatively affects the retention of the information.

It remains unknown how the positioning task affects reviewed knowledge and new knowledge. Reviewed knowledge is similar to how traditional concept maps work, with the user continuously processing knowledge he previously acquired. However, new knowledge is when the user attains new information while building the map. This can happen in closed concept maps because automated feedback is possible. It is a new aspect of learning by concept map building, and it is necessary to investigate the difference from the traditional concept map aspect of reviewing knowledge. It is important to know how positioning affects these different types of knowledge because of the following:

It can help to decide when the positioning task is desirable in activities using closed concept maps.

It can help explain the cognitive mechanisms between the positioning task, which can help understand better how concept maps help students learn.

It can inform designers of the potential benefits and drawbacks of designing activities which include similar tasks.

This study has four research questions :

How does the positioning task affect immediate new knowledge?

How does the positioning task affect immediate reviewed knowledge?

How does the positioning task affect new knowledge after a retention period?

How does the positioning task affect reviewed knowledge after a retention period?

To answer the research questions, we study how the Airmap interface and the Kit-build interface differ in knowledge retention as a reviewing tool and as an acquisition tool. To the best of our knowledge, this type of investigation has never been done before. To do so, we compare test scores at three points of time: before building a map, after building a map, and after a 2-week delay. As such, 2 weeks are used as the length of the retention period described in the research questions. Knowledge which the student had before building the map is identified as reviewed knowledge. Knowledge the student obtains during map construction is identified as new knowledge.

Computer-based concept map tools and closed concept maps

Computer-based concept mapping has been used to improve learning in general ( Hwang et al. 2013 ; Kim and Olaciregui 2008 ; Willerman and Mac Harg 1991 ) and in reading comprehension ( Morfidi et al. 2018 ; Omar 2015 ; Alkhateeb et al. 2015 ). It has been pointed out that the advantages of computer-based concept map mapping are the ease of correction and construction ( Liu et al. 2010 ), the capability to add behavior-guiding constraints ( Reader and Hammond 1994 ), the creation process personalization, and the frustration reduction ( Anderson-Inman and Zeitz 1993 ). Computer-based concept mapping tools also make automated feedback possible. Past work has used semantic web technologies to make this feedback possible ( Park and Calvo 2008 ). Another approach is using word proximity data to score the maps ( Taricani and Clariana 2006 ). It is trivial to compare the student-built maps to the expert map when using closed concept maps. Multiple tools used this approach, such as Cmapanalysis ( Cañas et al. 2013 ), Kit-build ( Hirashima et al. 2011 ; Hirashima et al. 2015 ), CRESST ( Herl et al. 1999 ), KAS ( Tao 2015 ), and ICMLS ( Wu et al. 2012 ). It is possible to display exactly in which ways the student map differs from the expert map because they are built from the same pieces. This type of automatic diagnosis was found to be reliable when compared to traditional map scoring approaches ( Wunnasri et al. 2018 ; Wunnasri et al. 2017 ) and was found to correlate with standard science tests ( Yoshida et al. 2013 ).

This type of automated measurement has been used to measure changes in interdisciplinary learning during high school ( Reiska et al. 2018 ). Teachers can revise their lessons and feedback by using the diagnosis information. This approach has shown good results in retention when compared to traditional teaching, especially when the teacher uses the map to give the feedback ( Sugihara et al. 2012 ). This type of automatic diagnosis also allows for different automated feedback schemes, which has been effective for improving reading comprehension ( Pailai et al. 2018 ; Wu et al. 2012 ).

Closed concept map interfaces

This section describes both interfaces. They were both coded using the Unity engine Footnote 1 . WebGL builds were then generated so that the interfaces could be used on web browsers.

A screenshot of Kit-build can be seen in Fig.  1 . Example of nodes in the screen are “Komodo dragon” and “large size.” Examples of links are “mates” and “can grow to.” At first, the nodes and links are displayed in columns. There is a column for the nodes and a column for the links.

The Kit-build interface

There are gizmos to the side of the links. Users can drag and drop those gizmos. By overlapping the gizmo with a node, the user can associate the link with the node. As links have two gizmos, it can be associated with two nodes. When this happens, a proposition composed of the two nodes and the link is formed.

By drag and dropping the gizmos away from the nodes, the association between node and link is undone. This allows the user to undo the created propositions.

The user can also drag and drop nodes and links. This allows them to manage the layout. The nodes and links never move by themselves.

Figure  2 shows the Airmap interface. Concepts 1 to 3 are the nodes. Users can click on nodes to select them. Concept 1 and concept 2 are selected in the figure. Link 2-3 is a link. It is connecting concept 2 and concept 3. Thin lines connect the link to the concepts. There is a button menu on the left. It shows which links are available alongside their available quantities.

The Airmap interface

To connect nodes, it is necessary to select two of them. Afterward, it is necessary to select the link that will connect the two nodes. It is not possible for a link to be associated with a single node in Airmap. This marks a difference from Kit-build.

Users can click on links to disconnect nodes. Clicking on a link destroys it. After the destruction, the link becomes available once again.

Links and nodes move automatically. The user is unable to directly control the positions of the nodes and links. As such, users are not burdened with managing the layout.

The feedback system

Since Kit-build and Airmap both use closed concept maps, it is possible to compare the user made maps to the expert map. After the user builds the map, the system compares the built map to the expert map. It then displays to the user which parts of the expert map are missing in the user map. It also displays which parts of the user map do not exist in the expert map. The user can then use this information to modify their own map. The user continues to receive this information until they can build the expert map.

The positioning task, reviewed knowledge, and new knowledge

Positioning task.

The positioning task is the task of moving nodes and links while building a closed concept map. This is unnecessary in Airmap because of the automatic layout function. As such, the positioning task is only present in Kit-build. Closed concept maps involve finding three elements to build propositions. The positioning task in Kit-build may make the student have to reorganize the map while searching, increasing the effort related to building the map. Furthermore, Airmap makes the search simpler by separating links and nodes into different portions of the screen. As such, the positioning task in Kit-build also includes the higher effort caused by the lack of separation between links and nodes. Also, the positioning task is believed to increase cognitive load ( Furtado et al. 2018 ).

One important consideration is that the map creation effort should decrease as the user makes the map. This is because the amount of free pieces becomes smaller, decreasing the number of possible propositions the user could make. This means that the activity should become more manageable as the user completes the map, lowering the burden caused by the positioning task.

Reviewing in Kit-build and Airmap

Reviewing involves recalling previously learned information. As an example, let us say that the student answers that Komodo dragons are known as “Komodo monitor” in the pre-test. Then, when building the map, the student builds a proposition “Komodo dragon - known as - Komodo monitor.” That means that the student reviewed the information about the Komodo dragon while building the map. The ability of the users to keep this information in memory is compared across the two interfaces used in the experiment. This is a simplification of the actual process that occurs, as building the proposition is quite difficult given the number of pieces present.

The way information is recalled varies between traditional and closed concept maps. Closed concept map creation involves cued recall because the user has access to the labels in the nodes and links from the start. In contrast, traditional concept map built without access to external material involves free recalling. Past research has shown that cued recalls have beneficial memory effects when compared to free recall ( Paivio et al. 1994 ; Begg 1972 ).

Both closed concept maps and traditional concept maps involve summarizing existing knowledge into propositions (a trio of concept, link, and concept). The difference is that in traditional concept maps, the user can freely create the labels in the concepts and link. In a closed concept map, the user must find equivalently labeled links and concepts. Furthermore, there is the possibility that the idea the user wants to express is not viable with the provided pieces.

Cued recalls and summarizing into proposition work similarly in Kit-build and Airmap. However, the burden caused by the positioning task in Kit-build might increase the frequency of recalls, thus increasing retention ( Furtado et al. 2018 ). Retention impairment is also plausible since the positioning task might be distracting the user from the reviewing process ( Furtado et al. 2018 ).

As discussed above, the burden of the positioning task is theorized to be higher before the user first receives feedback. Because of this, most cued recall is believed to happen during this period of high burden. Furthermore, the positioning task burden might be increasing the amount of processing done on the macrostructure of the content ( Schroeder et al. 2017 ). If this is true, there should be a gap between Kit-build and Airmap in reviewing performance.

Furthermore, in this study, users received feedback on which propositions are correct, incorrect, and missing. In this case, reviewing is also reinforced, since the user is given feedback that his constructed propositions are correct. Furthermore, the user has to break down the incorrect propositions while creating the missing propositions, a process which involves reviewing information, especially misunderstandings. This process of reconstructing the map is complicated by the positioning task of Kit-build since the user also has to reposition the concepts.

New knowledge in closed concept creation

In this study, new knowledge is related to a question which the user answers correctly after building the map but not before. If the user states that apples are blue before building the map and then states apples are red after building it, then the user has acquired new information while building the map.

In closed concept maps, the user can acquire new knowledge through the use of automated feedback. The way information is acquired would depend on how the feedback is designed. A simple approach would be to inform the users of which propositions are correct, which are incorrect, and which are missing from his map. This is the approach used for the dataset analyzed in this study. Other types of feedback have been used in past studies, such as asking the user to justify his incorrect propositions by using phrases of a text ( Pailai et al. 2018 ).

As discussed above, the user has to reconstruct the map after receiving feedback. The complication caused by the positioning task of Kit-build also affects the acquisition process, as the user corrects his misunderstandings and builds new propositions. This could result in an increase in the retention of new knowledge when using Kit-build.

In contrast, the burden caused by the positioning task is believed to be lower during acquisition, since the search space gets smaller after the user receives feedback. The effect of the macrostructure of the map and positioning decisions could be diminished by the reduced burden. If this is true, the positioning task may not influence the retention of new knowledge.

Data analysis methods

Each research question needs a quantifiable metric. The metrics can be modeled after answer transitions between a test done before the concept map is built (pre-test), after the concept map is built (post-test), and after a delayed period (delayed post-test). Table  1 shows how each question can be classified. Each classification is related to one of the research questions.

“Review” is related to reviewed knowledge. “New” is related to new knowledge. “OnDelay” metrics are related to the 2-week retention period after building the map. Metrics which do not have the word “OnDelay” on them are related to the immediate measurements. Those immediate measurements are the pre-test and post-test performed minutes before and after building the map.

The questions are classified and then counted for each metric. This gives raw metrics for each user. Based on the raw metrics, normalized metrics are then calculated. The normalized metrics take into account individual ceilings into the calculation of each metric and are more representative of each research question. The normalized metrics and their formulas can be seen in Table  2 .

Data collection

The collection of data had a main phase and an optional delayed phase. In the main phase, participants were required to do the following:

Read tool instructions

Build the correct training map using the tool

Read a text

Take the comprehension pre-test

Build the correct text map using the tool

Take the comprehension post-test

The only difference between the conditions was the interface used. Air used the Airmap interface without hideable links, and Kit used the Kit-build interface. The interfaces had feedback enabled and required participants to redo their maps until they built the correct map. Participants who completed the main phase were invited to participate in the delayed phase. The delayed phase consisted of the same comprehension post-test used in the main phase, but with a delay of 2 weeks.

The data was collected from users recruited through Amazon Mechanical Turk. Participants were required to be residents of the USA and were also required to have completed more than 5000 tasks on AMT with an approval rate above 97%. They were paid $3.10 upon completion of the activities. Participants who agreed to take the delayed post-test received an additional $0.80.

The test answers are administrated through the system, digitally. As such, test answers are saved as log data. Table  3 shows an example of such data. The log data can then be used with the data analyses methods described above.

The text used described various characteristics of the Komodo dragon. It is a modified, shorter version of a text found in Wikipedia Footnote 2 . The comprehension pre-test and post-tests contained the same questions. The questions consisted of ten multiple choice questions created to test the content of the text. An English native speaker who is a university teacher of English as a second language verified the test and found no problems with it. The map participants were requested to build was based on the text and on the reading comprehension exercises. The expert map used in this experiment had 17 concepts and 17 links. Since each link corresponds to a proposition, it contained 17 propositions. The expert map was built based on the text.

The data collection process was delivered through a website. Participants completed informed consent and then proceeded to read instructions on the map building tool they would use. Afterward, they would build the training map to get used to the tool. The training map consisted of three concepts and three links. The content of this training map had no relation to the rest of the experiment. The tool instructions and the tool used to build the map were specific to each condition. After building the training map, participants read the narrative and answered the pre-test. After the pre-test, participants had to build the map using the tool respective to their condition. Participants then answered the post-test, ending the main phase of the experiment.

All activities in the main phase had a 5-min limit, with the exception of building the map, which had a 20-min limit.

During map constructions, users were given automated feedback by the system and could only proceed to the next task after submitting the correct map.

Two weeks later, participants were contacted by email to take part in the optional delayed phase. The delayed phase consisted of the same comprehension test taken in the pre-test and post-test. Participants did nothing else other than answer the comprehension test.

Normalized values for review, new, retained review, and retained new were calculated for each participant using their answers for the pre-test, post-test, and delayed post-test from the dataset. Table  4 shows the number of participants of each condition, alongside the average and standard deviation of the relevant normalized metrics. Figure  3 shows box plot comparisons of the two conditions.

Boxplots of the normalized values for Air and Kit conditions. Retained review, which is related to retained reviewed knowledge, represents the biggest difference between the two conditions

To address how the positioning task affects immediate retained knowledge, we compare the values of normalized review shown in the boxplots of Fig.  3 and the average values seen in Table  4 . There is very little difference in normalized review between the two interfaces, with users remembering around 90% of their pre-test answers in the post-test.

To address how the positioning task affects immediate new knowledge, we compare the values of normalized new shown in the boxplots of Fig.  3 and the average values seen in Table  4 . There is very little difference in normalized new between the two interfaces, with users from both interfaces correctly answering around 70% of the questions in the post-test that they could not answer correctly in the pre-test.

To address how the positioning task affects delayed reviewed knowledge, we performed a Mann-Whitney test with retained review as the dependent variable and condition as the predictor. The test revealed that retained review for the Kit condition (Mdn = 1) was significantly higher than retained review for the Air condition (Mdn = 0.62, U = 58, p = 0.002). Looking at Table  4 , Airmap users remember around 60% of revised information. In contrast, Kit-build users remember 87% of the revised information. Not only that, but the standard deviation is lower for Kit-build, suggesting results are more stable. Both Air and Kit maintain similar transitions during map building, but Air drops steeply after the 2-week delay, as far as reviewing is concerned. In contrast, Kit shows little loss in reviewed knowledge after the 2-week delay.

Looking at the scatter plot in Fig.  4 , multiple Kit-build users forgot none of the test answers related to reviewed information after 2 weeks. In the worst case scenario, Kit-build users would forget two answers, while Airmap users could forget up to four answers.

A scatter plot of review and retained review. Both metrics are related to reviewed knowledge. The farther away from the diagonal line, the more the user forgets. The Kit condition, represented by triangles, is able to retain more after the 2-week period when compared to the Air condition

To address how the positioning task affects delayed new knowledge, we compare the values of retained new in the boxplots of Fig.  3 and the average values seen in Table  4 . There is very little difference in normalized retained new between the two interfaces. This suggests that the two interfaces do not differ in retention as an acquisition tool. Users of both interfaces remember around 60% of the acquired information. This value is similar to the normalized retained review users have during Airmap use. This suggests that users process reviewed information and new information at around the same level while using Airmap.

Results show that there was little difference in immediate new knowledge, in the retention of new knowledge, and in immediate reviewed knowledge. As such, we can assume that the differences between the interfaces are not associated with processing new knowledge. Thus, Airmap outperforms Kit-build when new knowledge is of concern since users can make maps using less effort without decreasing immediate and delayed understanding of new knowledge. The reduction in effort, believed to also cause a reduction in cognitive load, is desirable because it has been associated with various benefits, such as reduced stress and higher satisfaction ( Zhang et al. 2015 ; Ward and Mann 2000 ). Results are in line with past research that stated the positioning task does not affect immediate learning gains ( Furtado et al. 2018 ), but it goes further to also state that it does not affect the retention of new information.

This, however, did not hold true for delayed reviewed knowledge. Kit-build outperforms Airmap in retention of reviewed content after a 2-week period. As such, we have a trade-off between effort and reviewing retention. Cognitive load reduction leading to a reduction in general retention has been shown in other research as well ( Kirschner et al. 2009 ; Schnotz and Rasch 2005 ), which is in line with the theory that Airmap has lower cognitive load than Kit-build ( Furtado et al. 2018 ). Unlike past results, results suggest that this influence on retention is limited to reviewing activities. The reduction is believed to be mostly caused by the layout management burden, but there is also the visual load reduction factor. Isolating these two factors to see how they affect reviewing retention is a matter for future studies.

Results also inform further educators who use closed concept map building tools. Previously, it was stated that Kit-build should be used whenever retention is of concern ( Furtado et al. 2018 ). However, current results suggest that Kit-build should be used as a tool for reviewing. If the user does not have a good grasp of the content, Airmap is better suited since a good portion of information will be new. Furthermore, developers of other closed concept map building tools now have more information when deciding whether or not to add automatic layout management and spatial separation to their tools.

Past research has also pointed out that learning applications, in general, could be made easier to use by applying automatic layout management and spatial separation when retention is not of concern ( Furtado et al. 2018 ). The same work also pointed out that using a reversed approach could benefit retention gains in learning activities. Adding the positioning task to the activity would be the reversed approach. Current results go further, stating that retention is only prejudiced during reviewing activities, so the amount of activities that could benefit from removing the positioning task is higher than previously thought. However, the reversed approach that was thought to benefit overall retention is suggested to only influence retention during reviewing. As such, only learning activities which focus on reviewing knowledge should consider this reversed approach. Fields in which retention plays a strong role, such as vocabulary learning ( Folse 2006 ) and science classes ( O’day 2007 ), could benefit from review activities focused on these reversed approaches.

This study showed the effect of the positioning task has on reviewed knowledge and new knowledge when using closed concept maps. The positioning task did not affect new knowledge in any way while affecting reviewed knowledge after a 2-week retention period. Thus, having to manage the layout during concept map creation and having to search around for pieces in a complex space help students commit reviewed information deeper into memory, without affecting new information attained during the process.

The obtained results inform educators and researchers of when the positioning task is desirable in the use of closed concept maps. It also helps inform learning activity designers of the potential benefits of redesigning an activity to include or exclude the positioning task.

A limitation of this study is that the two factors related to cognitive load were not separated. Because of this, it is unknown how much the spatial separation of elements and the automatic layout management separately influence learning and cognitive load. Another limitation of the study is that it does not include how involved the students are in the positioning task when using Kit-build. Different students may be more or less involved in positioning. They might also give positioning different degrees of importance. This study does not take these elements into consideration. The fact that only one learning material was used in the experiment is also a limitation. The comprehension test used was verified by a university teacher of English who is also a native speaker, but it is a non-standardized test. Using standardized materials in a future experiment would improve the robustness of the results.

One matter for future studies is analyzing differences in knowledge acquisition without the use of feedback mechanisms. Finally, an investigation of whether or not time and energy saved by using Airmap can be used in other activities might strengthen the retention of reviewed knowledge when using the tool. Finally, a mixed approach of using first Airmap to introduce the content and then Kit-build to review could extend the retention-enhancing properties of Kit-build to a larger portion of the learning content.

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Abbreviations

Amazon Mechanical Turk

Web Graphics Library

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This work was partially supported by JSPS KAKENHI grant numbers 17H01839 and 15H02931.

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Fonteles Furtado, P.G., Hirashima, T. & Hayashi, Y. The effect on new knowledge and reviewed knowledge caused by the positioning task in closed concept maps. RPTEL 14 , 15 (2019). https://doi.org/10.1186/s41039-019-0108-1

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A Conceptual Framework Toward Understanding of Knowledge Acquisition Sources and Student Well-Being

1 Business College, Yango University, Fuzhou, China

2 College of Management, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan

Michael Yao-Ping Peng

3 School of Economics & Management, Foshan University, Foshan, China

4 School of Digital Economics, Guilin University of Electronic Technology, Guilin, China

Yangyan Shi

5 Business School, Guilin University of Technology, Guilin, China

Shwu-Huey Wong

6 Department of Education, New Era University College, Kajang, Malaysia

Wei-Loong Chong

Ching-chang lee.

7 Department of Information Management, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan

Associated Data

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.

There are a multitude of factors influencing student employability, with all previous studies basing their conclusions upon predetermined variables according to different theories and exploring the relevance between them. In this study, teachers’ knowledge transfer and market orientation—are put forward on the basis of the marketing concepts in order to explore the conspicuousness between various factors within the structural model. This study uses students from colleges in Taiwan and mainland China, and purposive sampling is adopted to acquire samples required for statistics. A total of 1,222 valid questionnaires were collected from Taiwanese and Mainland China students. The results indicate that knowledge transfer, market orientation and absorptive capacity have significant impacts on student employability, that the absorptive capacity has a positive moderating effect on the influence of knowledge transfer and market orientation on student employability. Based on results and findings, this study will provide suggestions for theoretical and practical implications.

Introduction

In this era of the knowledge economy, knowledge and capability-based views assert that knowledge is the main source of enterprise innovation, new value creation, differentiation and access to competitive advantage ( Zhou and Li, 2012 ). A focus on higher education is likewise relevant; the importance of knowledge to college students is at the core of personal differentiation and personal value creation. How to use knowledge to enhance their competitiveness to enter the job market is an important issue to be discussed ( Chung et al., 2016 ). In the field of higher education, most studies explore factors that affect student’s learning effectiveness ( Pike et al., 2011 , 2012 ) or the application effects of learning patterns ( Pascarella et al., 2013 ). However, few studies have explored the knowledge-processing of students from the “input-process-output” stance of knowledge management with regards to the future entry of students into the workplace ( Baek and Cho, 2018 ). The ability to utilize and apply internalized knowledge is to cultivate a student’s employability ( Blázquez et al., 2018 ). Some scholars argue that “employability” is an individual’s need to continuously acquire or create jobs through appropriate use of competence, to accomplish tasks and adapt to changes in the internal and external labor market ( Fugate et al., 2004 ; Cuyper et al., 2008 ; Vermeulen et al., 2018 ; Shahzad et al., 2020 ). According to the cognitive learning theory, the development of employability can be regarded as the process of forming knowledge cognition, and the acquisition and input of knowledge is the source of learning stimulation.

A majority of studies on higher education have discussed factors influencing learning outcomes of students ( Pike et al., 2012 ; Bailey and Phillips, 2016 ) or the application effect of learning models ( Pascarella et al., 2013 ; Campbell and Cabrera, 2014 ). Some studies in recent years began to discuss the shape of student well-being from the view of educational psychology. The emergence of positive psychology leads the psychology ( Seligman and Csikszentmihalyi, 2000 ) into a new direction. Under the influence of the positive psychology, counseling and psychotherapy begin to turn their attention to positive affect subject ( Stallman et al., 2018 ). Many scholars advocate to emphasize the well-being of adolescent, and believe that well-being is the core of adolescent’s mentally healthy development ( Miller et al., 2008 ; Graham et al., 2016 ). This study replaces student learning outcome with student well-being as the core view: (1) Well-being, as the major concerns of student personality and social psychology, is used to examine social change and improvement of educational policies and solve student learning problems ( Bailey and Phillips, 2016 ; Hanson et al., 2016 ; Stallman et al., 2018 ). (2) The discussion of student well-being will put emphasis on finding symptoms such as possible depression, anxiety, and psychological disorder ( Bailey and Phillips, 2016 ) the positive and negative psychology lies between two extremes of continuous psychological states, and the more well-being of students will help students face challenges with a positive psychological state, and increase the value of learning course ( Stallman et al., 2018 ). Considering the above reasons, this study aims to further understand and discuss the development course of student well-being through enhancing students’ employability in the learning process.

From a system perspective, the source of knowledge acquisition can be divided into internal and external. Webster and Hommond (2008) point out that the development of university education standards should be conjoined with the market orientation concept of marketing theory ( Narver and Slater, 1990 ; Webster and Hommond, 2008 ; Webster et al., 2014 ). Universities need to continuously perceive the needs of students—both at present and in the future—to help them understand the advantages and conditions of off-campus competitors, and to create, communicate and balance the values of students and related stakeholders ( Webster et al., 2014 ). According to the above phenomena, the purpose of this study is to explore the impact of teacher knowledge transfer (TKT) and market orientation (MO) on student employability (SE).

Although students can attain valuable knowledge and information from teachers and external markets, they are not sure to develop high-quality employability. While market-oriented research suggests that systematic acquisition and analysis of information and knowledge contributes to capacity and performance ( Slater and Narver, 1995 ) this argument seems to ignore exactly how students use this valuable knowledge. Fabrizio (2009) emphasizes connectedness—students should have some level of method and mechanism to learn, digest, transfer and apply knowledge, and enhance the benefits of that knowledge; namely, absorptive capacity (AC) ( Cohen and Levinthal, 1990 ). Despite the abundance of rich and valuable knowledge and information, the AC of students is of crucial importance. Therefore, this study uses AC as the moderating variable between knowledge acquisition and employability, and understands its impact on employability.

Scholars believe that the results of a cross-country comparison can provide insights with profound implications; as such, scholars often use cross-cultural methods to compare research differences in different contexts ( Chang et al., 2011 ). There are differences in the development of student well-being in the context of higher education of different countries, even if in Asian regions ( Jang et al., 2016 ; Shogren et al., 2018 ; Al-Jubari et al., 2019 ). Although mainland China and Taiwan are difference in higher education policies, cultural distance and language teaching also show similarities. That’ why students of mainland China and Taiwan are used as samples in this study for comparative analysis. According to the above explanations, this study intends to propose relevant research contributions based on the following theoretical gaps: (1) applying learning cognition theory to explore students’ knowledge acquisition in higher education; (2) exploring the capability/skill development from the perspective of students to cultivate and establish students’ employability, and verify the relevance among sources of knowledge acquisition, absorptive capacity and employability; (3) adding the cross-country comparisons to research framework to explore the difference in subjective well-being of students in mainland China and Taiwan.

Literature Review and Hypotheses Development

Student employability (se).

The substantial technological, social, and economic changes that have occurred in recent decades have modified the concepts and operations of industrial organizations ( Abbas and Saǧsan, 2019 ) and HEIs across the world ( Vermeulen et al., 2018 ). Hence, dynamic HEIs maintain the highest standards of human capital development, so as to contribute to economic growth ( Ahmed et al., 2015 ; Baek and Cho, 2018 ). Student employability drives the reform of higher education policies in various countries and has become the core project of university administration ( Cranmer, 2006 ). The research of employability has been paid more and more attention by scholars, through research context and research method design in conjunction with cross-theory and practice analysis to understand the meaning of employability and its causal relationship with other factors ( Hennemann and Liefner, 2010 ; Avramenko, 2012 ; Baek and Cho, 2018 ). There are a number of important factors that cannot be learned from higher education courses, such as personal conditions, interpersonal relationships, and external reasons ( McQuaid and Lindsay, 2005 ).

It can be seen that employability is a social and psychological construct, including subjective and objective aspects ( De Vos et al., 2011 ; Blázquez et al., 2018 ). In the objective aspect, The Department of Education, Science and Training (DEST) (2006) established an “employability skills framework” with eight categories: communication skills, teamwork ability, problem-solving ability, original and entrepreneurial ability, planning and organizational ability, self-management ability, autonomous learning, and scientific and technological ability. In the subjective aspect, some scholars have developed measurement scales to examine individual cognition on employability in several way ( Andrews and Higson, 2008 ; Pan and Lee, 2011 ). The factors of employability need to consider elements such as national culture, industrial development and population structure ( Lurie and Garrett, 2017 ; Likisa, 2018 ) and subjective aspect should be taken into account in this study. Pan and Lee (2011) based on the employability measurement scale developed by Andrews and Higson (2008) surveyed the flow of Taiwanese graduates, and found that employability can cover general ability for work, professional ability for work, attitude at work, career planning and confidence.

Student Employability and Self-Efficacy

Social cognition scholars argue that individuals’ behavioral outcomes will be influenced by both environmental and cognitive factors in a given situation, especially those beliefs that lead to success and behavior ( Lent et al., 2014 ; Wang et al., 2016 ). They call these beliefs “self-efficacy,” an important cognitive variable in personal factors during the process of interpreting individual formative behaviors, and interaction with the environment ( Lent et al., 2014 ; Sheu et al., 2014 ). It can also be seen as the basis for human behavioral motivation, mental health and personal achievement ( Dacre and Qualter, 2013 ). Self-efficacy is widely used in the field of education to explore the psychological cognitive factors of students of different ages and their positive impact on academic achievement and student career development ( Wang et al., 2016 ).

According to the above discussion, students who have confidence in their abilities will have more efficient behavior and better interpersonal relationships than those who do not. According to Dacre and Qualter (2013) highly self-motivated students look for resources and opportunities to accomplish tasks that exist in social networks. Only by establishing and maintaining network relationships can they achieve their goals. Knowledge and resources are needed ( Lent et al., 2014 ; Sheu et al., 2014 ). Furthermore, teamwork can also be seen as a strong network relationship, and the process of students solving problems and achieving tasks through teamwork will positively affect their employability. It is pointed out that, according to the above, this study proposes the following assumptions:

H1: Self-efficacy has a positive and significant impact on SE.

SE and Subjective Well-Being (SWB)

People will eventually begin to reflect on the self-seeking of material satisfaction, further seeking psychological satisfaction and beginning to emphasize the importance of quality of life; thus the proposal of the concept of SWB. SWB is a result of satisfaction of life coupled with perceived positive and negative emotional intensity ( Pearce, 2017 ). Keyes and Waterman (2003) and Keyes (2005) expanded the definition to incorporate the concept of “social well-being” by merging the two (psychological well-being and emotional well-being) to delineate SWB as a sum of three aspects: in the sense of psychological well-being, it serves to explore self-psychological adjustment and the macro-consciousness of the individual’s inner self; a sense of evaluating the function of the self in life through public and social norms; and lastly, emotional well-being as the individual’s awareness and assessment of the emotional state of self-life ( Pearce, 2017 ).

Cuyper et al. (2008) considers employability as having its importance in the post-industrial knowledge society by continuously updating knowledge to maintain competitiveness in a global market ( Griffeth et al., 2005 ) and making them feel capable of dealing with temporary and future developments—new psychological contracts created by individuals will likely increase their well-being. In addition, individuals can process the same things and tasks more efficiently and in less time with relevant experience, updated skills and knowledge—as well as a well-developed social network—so as to improve employability ( Griffeth et al., 2005 ). The abundance of time saved will be used for life needs and personal future planning, thereby enhancing happiness. Similarly, students with higher employability can face the challenges of the future with a broader perspective. In addition to mastering the content of school work, they also have a more precise direction for planning and preparation for entering the workplace, reducing their insecurity and enhancing SWB. Based on the above phenomena, the hypothesis of this study is as follows:

H2: SE has a positive and significant impact on SWB.

Self-Efficacy and Subjective Well-Being

Some scholars have focused their investigations on mental health concerns, social support, and coping styles in low SES college students ( Tong and Song, 2004 ). However, few studies thus far have tapped this population’s general self-efficacy and SWB ( Pearce, 2017 ). Tong and Song (2004) research findings indicated that low-SES college students reported a lower level of social support, limited sources of support, and low perceived support. It implies that low-SES college students’ general self-efficacy and SWB decrease because they are unable to receive timely and necessary psychological support when confronting stress. In addition, it might contribute to unique stressors. Conversely, students with higher self-efficacy have higher SWB ( Pearce, 2017 ). In summary, the study infers the following:

H3: Self-efficacy has a positive and significant impact on SWB.

Market Orientation (MO) and Student Employability

In particular, Narver and Slater (1990) define “market-oriented” as a composition culture that creates value for customers efficiently and effectively, thereby establishing superior performance for the company. They propose three cultural measurement aspects: (1) “customer orientation,” which means that students can understand the requirements and expectations of future employers from the perspective of employment; (2) “competitor orientation,” an analysis of the existing and potential graduates of other universities, to understand their short-term advantages and disadvantages as well as long-term possible development capabilities and strategies; and (3) “inter-functional coordination,” meaning that the university can provide the target employer with the value of future superior employees (i.e., graduates) through the integration and application of on-campus curriculum and administrative resources. Even the Education Criteria for Performance Excellence of the Malcolm Baldrige National Quality Award (MBNQA) raises the importance of MO for the development of higher education ( Webster and Hommond, 2008 ; Webster et al., 2014 ).

Regarding employability, Fugate et al. (2004) found that it is a multidimensional construct covering career identity, personal adaptability, social and human capital etc., which helps to create individual employability; that is, from a learning perspective, it emphasizes individual initiative learning and engaging in activities or participating behaviors to promote self-adjustment and enhance the possibility of strategic change and success ( Crossman and Clarke, 2010 ). The study pointed out that in the process of constructing individual employability, collecting employment information is a necessary skill; students should be aware of the employment situation in the labor market, including familiarity with industrial structure and workplace information so as to understand the ways to effectively obtain work and create social capital to obtain job opportunities and important resource conditions ( Bridgstock, 2009 ). The establishment and development of relevant beliefs, values and cultures in this kind of market-sensing orientation will encourage students to actively collect employment information, explore careers, select suitable industries, and propose an energy-increasing plan to build individual employment competitiveness.

H4: Market-oriented culture has a positive and significant impact on SE.

Teacher Knowledge Transfer (TKT) and Student Employability

According to the cognitive learning perspective, students can use their abilities and resources to build their own core competencies and shape their employability through the use of their abilities. Whether it is the acquisition, accumulation or creation of knowledge, each will certainly be affected by the process of knowledge transfer; only knowledge transfer can effectively create new knowledge to be applied in the management process to create value for individuals ( Walter et al., 2007 ). Therefore, in the learning processes of knowledge recipients, the information must be internalized. Knowledge exists in the human mind through learning or experience, and then gradually grows with experience, involving personal beliefs, judgments, and value perceptions, in addition to explicit textual behavior, including the implicit mental journey. Polanyi (1962) distinguishes between tacit knowledge and explicit knowledge. There are some differences between the two types of knowledge. Explicit knowledge is objective and rational, and it can be encoded and stored in various physical and electronic formats. Tacit knowledge is the individual’s own experience, reflection, cognition, or talents, which are difficult to be presented ( Astorga-Vargas et al., 2017 ). Scholars suggested four steps of process of knowledge transfer: (1) socialization: the beginning of knowledge transfer with the process of tacit knowledge, facilitation of life experience, and the capacity among students where they reside and are needed. It happens regularly through meeting records, that modeling the way of work by repeating a task that leads others to learn by example; (2) externalization: this propitiates all activities that are grouped and aimed at capturing, organizing, structuring, representing, coding knowledge, in order to facilitate the management by changing the knowledge from tacit one to explicit one ( Inkpen and Dinur, 1998 ; Nonaka and Von Krogh, 2009 ; Zhou et al., 2010 ) (3) combination: there is a process in which different pieces of existing explicit knowledge are merged to create a new explicit knowledge; (4) internalization: process is carried out by putting into practice what has been learned from explicit knowledge ( Astorga-Vargas et al., 2017 ). According to the above description, this study defines TKT into a learning process. Teachers will pass implicit knowledge and explicit knowledge to students through knowledge externalization, enabling students to integrate it with their own currently held knowledge.

Teacher knowledge transfer helps students to learn richer knowledge. In addition to the teaching experience of teachers, it also needs the cooperation of the learning environment. As mentioned above, TKT is a learning process which includes changes in the learning environment, assignment of course tasks, or conversion of the teacher’s instruction style. According to the characteristics of TKT, although implicit knowledge is more difficult to transfer than explicit knowledge, teachers use learning patterns to assist students in acquiring the value of knowledge. In short, the use of explicit knowledge helps to enhance general work ability and professional work ability, while promoting the improvement of learning efficiency and effectiveness, thereby enhancing students’ work attitude and self-confidence. Explicit knowledge plays an indispensable role in general and professional competence, but the most important aspect of this its combination with implicit knowledge to enhance the energy of innovation. In addition, Teigland and Wasko (2009) believe that explicit TKT will help students to reuse knowledge, besides solving common problems, interaction with teachers, and creating new knowledge; the transfer and integration of implicit knowledge can also generate new ideas and novel solutions. Therefore, this study infers the following assumptions:

H5: TKT has a positive and significant impact on SE.

Student Absorptive Capacity (SAC) as a Moderator

Nieto and Quevedo (2005) defined AC as the ability of companies to identify new values, acquire external knowledge, and absorb and apply this knowledge to commercial purposes. Based on existing knowledge, this AC serves to develop and promote new knowledge. In other words, in the context of higher education, the maintenance of students’ own abilities will determine how to apply, integrate, and even fundamentally develop their core competencies. Cadiz et al. (2009) pointed out that personal AC is the process of applying new knowledge through assessment (identification and filtering of valuable information), digestion (translating new knowledge into usable knowledge), and application (using knowledge and converting it into usable knowledge). When students have strong AC, this capacity will enable students to generate new ideas in the process of learning, and even enhance the efficiency of TKT in the process of teamwork to complete those tasks explained by teachers ( Wang et al., 2016 ).

The composition of employability includes knowledge, skills, and abilities ( Hennemann and Liefner, 2010 ). In the context of higher education, if there is no ability to absorb knowledge on the receiving end of the knowledge, even if the knowledge transmitted by the teacher or school avenue is of value, students may not be able to use this knowledge effectively. Students with sufficient AC will openly communicate and exchange knowledge content through common interests and language, and retain valuable knowledge ( Cadiz et al., 2009 ). Its employability has a positive impact. Nor et al. (2012) pointed out that classifying students as having AC means that they have superior advanced knowledge and academic achievement, can effectively transfer knowledge and apply it, and improve their future academic achievement, thus enhancing their employability. Based on the above description, this study infers the following assumptions:

H6: Students’ AC has a positive and significant impact on SE.

Employability is composed of knowledge, technology, and diversity ( Hennemann and Liefner, 2010 ; Baek and Cho, 2018 ). In the context of higher education, students may not have the knowledge and ability to absorb knowledge ( Blázquez et al., 2018 ) even if the information provided by teachers is enriched. The market-oriented culture department emphasizes that students obtain relevant information from competitors and employers from the external employment market and respond to the information through the integration process. Therefore, the market-oriented culture is the mechanism for students to deal with external information. However, even if students are willing to collect external knowledge and information, SE is difficult to be improved if students have no sufficient basic knowledge and capabilities to absorb these knowledge and information, whereas AC plays an important role in the process of knowledge processing. Zahra and George (2002) argue that AC includes interpretation, comprehension, and learning. From the point of view of external knowledge, Szulanski (1996) argues that individuals’ external knowledge is affected by the environment in which they live ( Shahzad et al., 2020 ). Accordingly, external knowledge will differ in its meaning and value when used in different environments and thus enhance one’s understanding, digestion, and replication of the knowledge ( Blázquez et al., 2018 ; Abbas and Saǧsan, 2019 ). In other words, students with sufficient AC will be able to communicate and share knowledge about the connotation of knowledge through mutual interest and language, and then acquire valuable knowledge ( Diener et al., 1999 ; Cadiz et al., 2009 ; Abbas and Saǧsan, 2019 ). Furthermore, in addition to facilitating students to receive and respond to future job market related information, competitors’ message processing and institutional administration can also help students to improve their learning planning and effectiveness ( Webster and Hommond, 2008 ; Webster et al., 2014 ). Consequently, if students have adequate AC, a higher degree market-oriented culture will more effectively facilitate students to take into account the important changes in the job market, and their employability that they have cultivated can meet the needs of employers. Thus, a hypothesis is made as follows:

H7: Students’ AC has a positive effect on the relationship between market-oriented culture and SE.

Nieto and Quevedo (2005) argued that AC means the individual’s ability to acquire, digest, and apply knowledge because of its path-dependent nature; therefore, based on the knowledge stock accumulated by students in the past, the key traits are students’ ability to communicate with teachers or peers in a common language, share their unique knowledge, and apply new knowledge with each other. In the process of transforming external new knowledge, Yeoh (2009) believed that students will become embedded in the relationship of mutual cooperation and trust through the process of socialization. When the interaction between students and teachers increases, it will be helpful to improve the efficiency of communication and knowledge exchange between teachers and students. Jiang et al. (2008) believed that if students have better AC, they can improve their ability to use it, digest new knowledge more efficiently, and have a deeper understanding of new knowledge for enhancing their professional and general abilities. In other words, if students do not have such AC, they cannot completely digest and apply implicit or explicit knowledge transferred from the teacher’s side ( Chen, 2003 ). Superior AC can promote students’ spontaneous learning behavior, identify and utilize their own abilities and resources, and integrate new external knowledge in order to promote their employability. Therefore, this study infers the following assumptions:

H8: Students’ AC has a positive effect on the relationship between TKT and SE

Based on the above hypothesis, this study proposes the following research framework as Figure 1 shown:

Research framework.

Methodology

Participants and procedure.

This study proposed a framework to explore the correlations and development mode of MO, TKT, AC, SE, self-efficacy and SWB. It sampled from Taiwanese and mainland China HEIs, including both public and private. This study also incorporates students’ year of study as a sampling condition, as freshmen were left out of sample. This study selected 12 Taiwanese HEIs and 6 mainland China HEIs, and then sent 2,000 questionnaires to each of them. After sampling, a total of 657 Taiwanese questionnaires and 565 mainland China questionnaires were returned, for an effective response rate of 65.7 and 54.8%. Since freshmen were not familiar with the learning environment, all participants in this study were sophomores, junior, and senior students. In this study, according to analysis method of Lin et al. (2014) we used two method tools to analyze research framework. First, we adopted SEM to test the direct path among variables. Second, we used hierarchical regression analysis to examine the moderating effect of AC with interaction items and several control variables.

All constructs were measured by multiple-item scales based on previous studies. Similar to the employability scale reported by Pan and Lee (2011) eighteen items were used to capture general ability for work (GAW) (8 items), professional ability for work (PAW) (4 items), attitude at work (AW) (3 items) and career planning and confidence (CPC) (3 items). MO was adapted from Narver and Slater (1990) and Hammond et al. (2006) which was measured in terms of customer orientation (6 items), competitor orientation (4 items), and inter-functional coordination (6 items). TKT uses the explicit knowledge (5 items) and tacit knowledge (4 items) scales developed by Zhou et al. (2010) . Following Cadiz et al. (2009) absorptive capacity was measured in terms of assessment (3 items), assimilation (3 items) and application (3 items). In self-efficacy, six items were selected on the basis of prior scale and item analyses of Asian applications ( Little et al., 2002 ; Rigotti et al., 2008 ). Subjective Well-being was measured using Keyes (2005) Subjective well-being instrument (adolescent version), which comprehensively assesses well-being in terms of emotional (3 items), psychological (4 items), and social (4 items) dimensions. All scales are shown in Table 1 .

Scale description.

Reliability and Validity

All scales used in this study were found to be reliable, with Cronbach’s α ranging from 0.65 to 0.92. Table 2 shows the reliability of each scale, and the factor loadings for each item therein. In order to gauge validity, this study employed confirmatory factor analysis (CFA) using AMOS 23.0 to verify the construct validity (both convergent and discriminant) of the scales. According to Hair et al. (2006) recommended validity criteria, CFA results show standardized factor loading of higher than 0.7; average variance extracted (AVE) ranges between 0.501 ∼ 0.806; and composite reliability (CR) ranges between 0.744 ∼ 0.918. All three criteria for convergent validity were met, and correlation coefficients were all less than the square root of the AVE within one dimension, suggesting that each dimension in this study had good discriminant validity.

Measurement.

Main Effect Analysis of the Structural Model

It is confirmed that the measurement pattern is stable. However, in order to avoid overgeneralizing the data-driven patterns and theories, the study follows the suggestion of Hair et al. (2006) to divide the sample data into two groups based on regions (657 Taiwanese and 565 mainland China students, respectively). Besides, multiple group testing is combined with bootstrapping to gradually control the pattern parameters of the groups. The nested models develop from the different limitations χ 2 difference quantity to make significance analysis, in order to determine the reasonability of those parameters in controlling the two groups. The results are shown in Table 3 .

Multi-group testing.

The analysis results show that the value of each pattern mode of χ 2 / df ranges from 2.533 to 6.065, the RMSEA ranges between 0.036 and 0.065 and the ECVI is within 90% of the confidence interval. It can be learned from Table 3 that the χ 2 values of the weighted measurement model, weighted structure model, covariance structure model and residual structure model reach significant levels, which shows that the models have good between-groups invariance. In addition, the NFI added value of each model was less than 0.05, which is consistent with the standard recommended by Little (1997) . Therefore, the framework and conclusion of this research will present a good generalized validity.

The multi-group analysis method recommended by Chin (2004) was utilized to examine the hypothesis on the moderating effects of each country in the research model. The path coefficients and t -values of the hypothesized relationships were calculated to evaluate the significance of the relationships in each subgroup. The standardized structural weights for Taiwan and mainland China are shown in Table 4 , Figures 2 , ​ ,3. 3 . These standardized structural weights were estimated with the item-factor loadings held equal across countries. Thus, they are the best estimates of the true structural weights. Table 4 shows that seven hypotheses were supported for the Taiwanese subgroup, whereas for the Chinese subgroup only five hypotheses were supported. As shown above, H4 and H5 were partially supported, while H1, H2, H3, and H6 were fully supported. The results in Table 3 also show the addition of the interaction terms to the main effects model of the Taiwan and mainland China samples. In H7, we argue that interaction term of MO and AC has a positive effect on SE. Although interaction terms of both samples were significant, but coefficients of the Taiwanese sample were negative (−0.129 and 0.091). Consequently, H7 is partially supported. With regard to H8, Table 4 shows that the interaction term between TKT and AC moderates positively to SE (β = 0.098, p < 0.05) in the Taiwanese sample. Therefore, our findings partially support H8.

Comparison analysis between Taiwan and Mainland China.

Structural model on Taiwanese students. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001.

The statistical analysis shows that there were differences between the Taiwanese and Chinese subgroups. The objective was then to determine whether the differences were significant or not. First, the data were tested using the Kolmogorov–Smirnov test of normality, and the results indicated that it was not distributed normally. Therefore, the Smith–Satterthwaite t -test, which is utilized when the data violate normal distribution or exhibit unequal variances, was selected ( Chin, 2004 ). Thereafter, the results of the t -tests for each subgroup are detailed in Table 3 . There were significant differences in the all eight path coefficients between the two subgroups. Except for the path coefficient of AC → SE and AC ∗ MO → SE between the two subgroups were negatively significant differences, the remaining six path coefficients produced positively significant differences. The results of hypothesis testing are discussed further in the following sections for their possible implications for teaching.

Research Findings and Discussion

This study takes university students as research samples to test the correlations among MO, TKT, AC, SE, self-efficacy, and SWB by using the competition enhancement model, thus to bridge the theoretical gap of applying western theory in the eastern situation, and to increase the generalization of learning cognition theory. This study also finds the crux of the real-world phenomena based on the suggestions of scholars and verifies the differences between the variables of Taiwanese and mainland Chinese samples within the research structure ( Lu et al., 2003 ; Lu, 2009 ). Based on the research results, this study proposes the following research findings.

First, the study found that regarding the correlation between the research frameworks in the mainland Chinese sample, only the employability has a positive impact on self-efficacy and happiness. It means that even in the context of the East to verify the theoretical framework proposed by the Institute, different countries may also produce different outcomes, namely the potential interference effects of differences in social systems. The result have brought deeper enlightenment to the research in the field of education. When we use Western theories to verify their applicability to students in Eastern contexts, different social systems will also produce different results. Therefore, in addition to cultural differences, comparisons between countries are also necessary ( Chang et al., 2011 ).

Second, under the Taiwan sample, the correlations among the variables in the research framework are positive and statistically significant, meaning that Taiwanese students’ efforts in collecting market information and TKT are helpful to improve SE. When students are highly employable, they are more confident in psychological cognition to face all challenges, thereby enhancing their self-efficacy and SWB. This result is consistent with the research results of Cuyper et al. (2008) and Dacre and Qualter (2013) . However, for the mainland Chinese samples, MO and AC have no statistically significant impact on SE; in fact, TKT even has a significantly negative impact, which is inconsistent with this study’s inference. The possible reason is students in mainland China have a strong spontaneous learning attitude. They believe that the acquisition of skills and knowledge come from the process of self-study. Therefore, even though the ability to collect and absorb employment market information has become a tool for students to learn by themselves, the effect is not significant.

Thirdly, from the difference analysis of path coefficients, it is known that except for absorption capacity, the difference of the SE path is not significant; the other paths are statistically significant, representing the correlation between all paths, and the degree of Taiwanese students’ feelings will be larger than mainland Chinese students. Taiwanese students are aware of a high degree of personal obligation in the face of TKT and employment market information collection. Therefore, it is easy to detect the role that they should play as having a positive impact on the participation of various learning activities.

Fourth, this study infers that the individual’s AC will positively strengthen the influence of MO and TKT on SE. The study found that for the student aspect of Taiwan, the interaction effect between TKT and AC has a positive moderating effect on employability; it represents the assertion that AC can help to enhance the influence of TKT activities on SE. However, it is interesting to note that the interaction between MO and AC has a significant negative impact on Taiwanese SE, while conversely having a positive impact on students in mainland China. It implies that if students have higher AC, it will weaken the positive effect of MO on SE. This differs from the claims of Webster and Hommond (2008) and Webster et al. (2014) . Furthermore, although the employability of students in Taiwan can benefit from MO and TKT, students still have to apply knowledge to their internal capacity through the teacher’s introduction and explanation of knowledge. Even if students can obtain a large amount of external information, if there is no appropriate knowledge base and ability foundation, students will face learning obstacles which will reduce the positive impact on SE.

Educational Implications

This study explores the enhancement of employability development between Taiwanese and mainland Chinese students from the perspective of capacity enhancement. It is found that SE is the key to students’ self-efficacy and SWB, and the shaping of SE has considerable influence on the establishment of external information and TKT. In the process of education, schools and their teachers should identify the students’ thinking patterns and behavioral rules, and integrate the employment market perception into the students’ learning connotation, in order to successfully shape the market-oriented culture and strengthen their keen awareness of external information to meet the high expectations and employment conditions of future employers. On the other hand, it is known from the research findings that although Taiwanese students are better than mainland students in shaping their employability, the active self-learning attitude still needs to be strengthened. Therefore, it is more important for teachers to guide students to develop their employability through TKT activities.

In addition, the individual’s AC has no significant difference in the path of employment between the Taiwan sample and the mainland Chinese sample, indicating that AC plays an important role in shaping and developing students’ abilities regardless of the situation. In order to realize the benefits of AC, students must have a solid foundation of knowledge in order to clearly translate new knowledge and external information into their own understanding of knowledge and subsequently apply their employment skills ( Nor et al., 2012 ). Therefore, this study suggests that schools should assist students in establishing their own knowledge bases and effective knowledge-processing processes with knowledge learning platforms or electronic databases in order to simplify complex implicit and explicit knowledge and improve their employment skills.

The study found that Taiwanese college students are superior to mainland students in terms of the impact of the acquisition of their employability or the improvement of their self-efficacy on SWB. In other words, although students need more employment skills to face the uncertainty of the future employment workplace, students can gain more self-recognition in terms of self-growth, independence, etc., or can perceive that they contribute more socially. They can resonate with society and be accepted by society, and the feelings of self-emotion are happy and satisfied, making challenge of attaining knowledge have a positive influence on students. Therefore, this study suggests that Taiwanese universities should provide students with opportunities to develop superior employability in teaching activities and curriculum design, and strengthen self-belief, practical ability, professional ethics learning and application opportunities, so that students have higher psychological, social and emotional satisfaction ( Keyes, 2005 ) growing from learning in an appealing environment.

In short, Taiwanese and mainland students have their own merits at differing levels under this research structure. For example, Taiwanese students have diverse ideas and practical application experience, but lack clear learning goals and positive learning attitudes. Mainland students have strong motivation for learning and effective methods of knowledge exploration, but the abilities of problem-solving and creativity are slightly deficient. Therefore, if we can strengthen cross-strait teacher-student exchanges, through extensive short-term training, observation and academic competitions, and in-depth discussions on teacher curriculum and student learning outcomes, it will help each other in mutual learning and growth, thus improving the learning efficiency and employment competitiveness of cross-strait college students.

Research Limitations and Future Research Directions

The MO, TKT, SE, self-efficacy, and SWB scale of this research structure are based on the same set of samples for the reliability and validity test of the question scale, although Hair et al. (2006) agree that a same set of samples can be used for EFA and CFA to develop a measure of constructs. If, however, we can use another new set of samples for cross-validation, the reliability and validity of the study scale should be more valid. In addition, it is recommended that future researchers can expand the number of samples or compare and analyze them in different regions in order to better understand the differences. Moreover, higher education is not a single market. In addition to students, it also includes stakeholders such as teachers, staff, and employers. However, this study only explores students’ self-awareness of employability. It does not mean that their knowledge and skills are in line with the expectations of society and employers. Therefore, it is suggested that the measurement of SE in the future can be compared with each other, and the influence of different groups of cross-level factors will be explored. Additionally, it will be helpful to understand the cognitive differences between the students and the employment market.

Data Availability Statement

Ethics statement.

The studies involving human participants were reviewed and approved by Institutional Review Board, University of Taipei. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

YX and MP contributed to the ideas of educational research, collection of data, and empirical analysis. MP, YS, S-HW, and W-LC contributed to the data analysis, design of research methods, and tables. MP and C-CL participated in developing a research design, writing, and interpreting the analysis. All authors contributed to the literature review and conclusions.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding. This paper was supported by Fujian Province Social Sciences Plan Project in 2019 (Grant No. FJ2019B106).

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Article Contents

Practice and automatization in second language research: perspectives from skill acquisition theory and cognitive psychology.

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Majid Nikouee, Practice and Automatization in Second Language Research: Perspectives From Skill Acquisition Theory and Cognitive Psychology, Applied Linguistics , 2024;, amae035, https://doi.org/10.1093/applin/amae035

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As a second-language (L2) researcher and practitioner, I have long been fascinated by the intersection of practice and skill acquisition theory, a fascination rooted in several years of dedicated research in this domain. My personal journey of learning English as a foreign language, alongside acquiring other skills (recently ice skating), has mirrored the principles of skill learning theories: from factual information to developing procedural routines and subsequently (partially) automatized skills for real-world performance. It is this keen interest that led me to review ‘Practice and Automatization in Second Language Research: Perspectives from Skill Acquisition Theory and Cognitive Psychology’, edited by Yuichi Suzuki in 2024.

Before I proceed, it is crucial to emphasize (although you may be already aware of it!) that ‘practice’ as used in this piece refers to any L2 practice activity aimed at developing the skill of using the target language, distinct from the dichotomy of practice versus theory or the notion of communities of practice.

The concept of practice was sent into exile in the 1970s due to the stigma associated with mechanical drills, notably used in the Audiolingual method. However, it resurfaced in the 1990s (see DeKeyser 1997 , 1998 ) and gained significant traction in the late 2000s with the publication of Robert DeKeyser’s edited volume in 2007 : ‘Practice in Second Language: Perspectives from Applied Linguistics and Cognitive Psychology’. In preparation for writing this book review, I scoured through every issue of two journals, namely SSLA and Language Learning, published after 2008 (one year subsequent to DeKeyser’s edited book), seeking out L2 practice articles bearing the word ‘practice’ in their titles. In SSLA , I found 14 such articles while I found seven in Language Learning , not to mention a few articles in both journals addressing L2 practice without the word in their titles. Moreover, there was a special issue of Modern Language Journal in 2019 dedicated to the theme of L2 practice. These endeavours collectively suggest that the ‘second coming’ (I have borrowed this from W. B. Yeats’s poem ‘Second Coming’) of practice has opened a new avenue for L2 practice research. What sets this wave of research apart from the studies of the 1980s and earlier, prompting Patsy Lightbown to assert in 1985 that ‘practice does not make perfect’, lies in the conceptualization of practice in contemporary research.

The edited volume by Suzuki, who was a former PhD student of DeKeyser, serves as a sequel to DeKeyser’s edited work (henceforth referred to as the Suzuki book and DeKeyser book, respectively). At the core of the Suzuki book is the definition of practice laid out in the DeKeyser book: ‘Specific activities in the second language, engaged in systematically, deliberately, with the goal of developing knowledge of and skills in the second language’ (1). Drawing from this understanding of practice, Suzuki outlines five fundamental principles that underpin contemporary L2 practice, diverging from how practice was conceived during the audiolingual era: deliberateness, systematicity (including timing and variability), transfer-appropriate processing, feedback, and desirable difficulty. These five principles serve as the backbone of the entire book, which is comprised of nine chapters divided into three sections, along with an introduction and a conclusion paper.

Section 1, Foundations, comprises two articles. The first one, authored by Suzuki et al., explores how input-based practice can optimize input processing and refine L2 knowledge. Masatoshi Sato, in the second article, delves into skill acquisition theory with respect to learners’ L2 learning background and context. He opens the chapter by presenting two scenarios: one depicting an immersion context and the other illustrating a foreign language learning setting. I found Sato’s article particularly accessible due to his utilization of the scenarios to elucidate the components and mechanisms of the theory as they relate to L2 learning.

Section 2, Teaching Approaches and Contexts, comprises four chapters exploring L2 practice within varied educational settings. Marsden and Hawkes demonstrate how the five practice features identified by Suzuki can be integrated into foreign language curricula. Additionally, Ruiz et al. illustrate how artificial intelligence can provide affordances for skill development from declarative to automatized knowledge. Lambert’s contribution introduces a framework for seeding the five practice principles into task-based language teaching, contrasting it with task-supported language teaching. Drawing on an interactionist stance on practice, Lambert advocates for form-focussed instruction during task performance, through corrective feedback, arguing that focus on preselected forms in the pre-task phase jeopardizes the integrity of a task-based syllabus. Finally, Kevin McManus explores how the five principles can make inroads into the process of learning during study abroad programmes.

While both the Suzuki book and DeKeyser book include a Foundations and a Context section (albeit with minor variations in content), they differ in the final section. The latter addresses individual differences, specifically learner age and aptitude, whereas the former covers methodological issues in L2 practice research. Mai and Godfroid analyze 115 primary research studies in terms of methodological features and highlight areas within L2 practice research that warrant further exploration. The last two chapters by Y. Suzuki and Elgort and S. Suzuki and Revesz are dedicated to an important yet under-researched aspect of practice research: the measurement of practice outcomes, known as automaticity within skill acquisition theory and as fluency and accuracy development from a pedagogical standpoint (see Sato in the same volume). As a researcher intrigued by the concept of transfer of training, I am convinced that developments in this area can deepen our understanding of how transfer occurs from practice to performance. Specifically, by incorporating and validating a diverse array of assessment tasks, we can gain deeper insights into the mechanisms underlying the transfer process.

I would like to conclude this book review by offering some recommendations. Firstly, when Lightbown (2019) asserts that ‘practice is the only way to make perfect’, she emphasizes the importance of the renewed conceptualization of practice driving this volume. Given the substantial theoretical and empirical evidence for this understanding of L2 practice, it is time to set aside any lingering negative biases inherited from the audiolingual era within both researcher and practitioner circles. Researchers are encouraged to investigate the interplay between the core features of L2 practice while teachers can employ these insights to create more effective practice activities. Secondly, upon initially encountering the title of this volume, I presumed it was primarily intended for researchers. However, having read the book from cover to cover, I now believe that L2 instructors with a foundational understanding of SLA theory can benefit from its chapters, depending on their specific educational contexts. For instance, instructors implementing a task-based syllabus can glean valuable insights from Lambert’s chapter on incorporating repetition into practice activities. Lastly, this book is a rich source of research ideas. It holds promise for researchers, particularly graduate students, aspiring to advance this field of enquiry by addressing existing gaps in knowledge. Its comprehensive coverage and nuanced insights make it an invaluable resource for those seeking to push the boundaries of L2 practice research.

DeKeyser , R.   1997 . ‘Beyond explicit rule learning: Automatizing second language morphosyntax,’   Studies in Second Language Acquisition   19 / 2 : 195 – 221 .

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DeKeyser , R.   1998 . ‘Beyond focus on form: Cognitive perspectives on learning and practicing second language grammar’ in C.   Doughty and J.   Williams (eds.): Focus on Form in Classroom Second Language Acquisition . Cambridge University Press , pp. 42 – 63 .

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DeKeyser , R.   2007 . Practice in Second Language: Perspectives from Applied Linguistics and Cognitive Psychology . Cambridge University Press .

Lightbown , P.   1985 . ‘Great expectations: Second-language acquisition research and classroom teaching,’   Applied Linguistics   6 / 2 : 173 – 89 .

Lightbown , P.   2019 . ‘Perfecting practice,’   Modern Language Journal   103 / 3 : 703 – 12 .

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Learning by… Knowledge and skills acquisition through work-based learning and research

Journal of Work-Applied Management

ISSN : 2205-2062

Article publication date: 25 January 2022

Issue publication date: 5 October 2022

Issues around informal, non-formal and formal learning, intended and unintended learning and competencies and capabilities have been considered in work-based learning (WBL). However, demarcated modes of learning, or what can be called strategies or pedagogies of learning, associated with experience of work environments have yet to be examined. One mode of learning which has been highlighted in relation to work is reflective practice, and its centrality to learning at work has been established. But reflective practice as a core skill, and its relation to other approaches to learning and research in WBL, remains uncovered. The purpose of the present study therefore is to identify different modes of learning as they appear in the literature and to present a proto-theoretical “learning by …” model for WBL and research founded on learning by reflection.

Design/methodology/approach

Proto-theoretical modelling and qualitative descriptions of each mode of learning.

Work environments, and the higher degree WBL programmes which support them, should provide learning via every available mode of learning, thereby allowing students to find their own best orientation to learning and encourage it by any means.

Originality/value

The proto-theoretical model and 12 modes of learning applied to WBL are unique to this study. WBL provides participants of work with multiple opportunities and approaches to learn and similarly provides multiple modes through which learning can occur on the basis of knowledge and skills in reflective practice.

• Reflective practice
• Work-based learning
• Work-based research

Fergusson, L. (2022), "Learning by… Knowledge and skills acquisition through work-based learning and research", Journal of Work-Applied Management , Vol. 14 No. 2, pp. 184-199. https://doi.org/10.1108/JWAM-12-2021-0065

Emerald Publishing Limited

Copyright © 2022, Lee Fergusson

Published in Journal of Work-Applied Management . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyonemay reproduce, distribute, translate and create derivative works of this article (for both commercial andnon-commercial purposes), subject to full attribution to the original publication and authors. The fullterms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

This paper considers the notion of different modes of learning and how they apply to work-based learning (WBL) and research. It has been established that human beings learn via a significant number of separate, but often overlapping and interconnected, channels of exchange. These channels have been variously called learning strategies, learning methodologies, learning conditions, pedagogical practices and approaches to learning. In this research, we apply to WBL (as differentiated from workplace learning and work-integrated learning, for example) the meaning ascribed to it by Fergusson and van der Laan (2021a) .

the teaching-learning process from a model of knowledge transfer by teachers to a learning model based on student-centred competencies. Therefore, it has been necessary to include active learning methodologies that entail a greater degree of involvement on the part of the student, a greater dynamism in learning and a greater interaction with the contents. ( Oliván Blázquez et al. , 2019 , p. 2)

Such a view has been wholeheartedly embraced by pedagogues of WBL, a transdisciplinary “field of study” ( Garnett, 2016 , p. 306) which incorporates a variety of learning approaches in work environments. WBL's impact has recently been investigated ( Boud et al. , 2020 ), and its relation to research is a topic of growing pedagogical interest (e.g. Fergusson et al. , 2019a ; Scott, 2020 ).

Modes of learning are not learning styles ( Coffield et al. , 2004 ). Modes of learning are value-free and can equally apply to all work-based learners, while learning styles have historically denoted dichotomous learner types and scales which have been rightly contested. Arguments against mechanistic and reductionistic classifications of learning styles have grown in the literature ( Glazzard, 2015 ), and the so-called “false dichotomies” associated with stereotyping learners have been increasingly seen as problematic ( MacNeill et al ., 2018 ). Indeed, Dewey rejected the notion of the sharp binaries associated with learner pairs, and recent research matching learning style with teaching methods designed to accommodate different types of learner in higher education found no relation to academic achievement ( Cimermanová, 2018 ).

According to Attenborough et al. (2019 , p. 132), WBL appears “everywhere and nowhere”, meaning it is ubiquitous but often goes unrecognised by learners, teachers and organisations. Learning in this context apparently sits on an informal–formal scale: “at the informal end of the continuum WBL comprises implicit, unintended, opportunistic and unstructured learning, with the absence of a teacher. . . . Practice that is supervised by a mentor or supervisor represents WBL towards the formal end” (ibid.). WBL therefore incorporates both primary learning, where learning is the intended outcome of a work-related activity, and secondary learning, where it is an incidental and spontaneous by-product of work. In such environments, learning occurs when the working professional is both a student and a teacher, learning at and from work through a variety of modes, including what Wofford et al . (2013) called “learning-on-the-fly”.

Irrespective of whether learning at work is informal or formal, or is intended or incidental, at its heart is reflective practice (e.g. Fergusson et al. , 2019b ; Helyer, 2015 ). Therefore, according to Eden (2014 , p. 267), “the experience of work and [its] subsequent analysis to properly comprehend that experience and to apply it to future work” sits at the core of the WBL mission. In this paper, I propose that reflective practice also permeates every mode of learning in WBL.

Reflective practice in work-based learning and research

Learning by reflection is well documented and involves creating a learning situation where the outcome is a combination of previous experience at work, specific work contexts and the theory that guided practice. Sometimes associated with higher-order thinking ( Cañas et al ., 2017 ), reflective practice is foundational to all modes of learning and has been adopted as a key feature of WBL (e.g. Costley and Abukari, 2015 ). Indeed, Helyer (2015 , p. 16) called it a “critical skill” in WBL, claiming it develops “self-identity, self-awareness and personal agency” and thus leads to learning how to learn. Carrol (2010 , p. 24) went further and said: “Reflection is the medium through which we learn. Not only is it the bridge between information and wisdom, it is the process that turns information and knowledge into wisdom”.

This is why Kim et al. (2018) discuss reflective practice in the context of work-based nursing, Gibbons (2018) does so in the context of work-based law education, and Gerhardt (2019) considers it in the context of human resource management. This is also why Lester and Costley (2010 , p. 563) maintain that “one of the distinctive features [of WBL] is its emphasis on reflecting on and enquiring into work activity and on developing people as reflective, self-managing practitioners who are committed to their own development”. In education, it has been argued that “reflective practice aims to progress teachers' knowledge, understanding, and actions throughout various stages of their career, so that they positively impact student outcomes. . . . At the heart of reflective practice research is a teacher's ability to know, understand, and reflect upon professional practice. …” ( Kern and Wehmeyer, 2021 , p. 170).

can be questioned, particularly personal reflection which tends to focus on feelings. Introspection is the dominant approach to personal reflective practice, with prime focus being on individual and personal thoughts, feelings and behaviours. This often is seen by students as adequate and appropriate reflective practice, but a practice that is “fluffy” and irrelevant. Perhaps it is purely naval gazing and needs to be challenged in students so that critical reflection occurs that can lead to change, development and growth.

Nevertheless, Greenberger (2020) has attempted to create a “guide for reflective practice”. In so doing, he has approached the thorny issue of defining the practice in a coherent way while calling it a “skill (of reflecting on past experience) and method (to inquire about problems in professional practice) that is contextualized but also theory-guided” (p. 459).

Reflective practice has also been analysed from the perspective of work-based research. This author, for example, has considered micro- and macro-reflective cycles in work-based research associated with learning how to develop objectives and a research proposal and learning how to successfully conduct and report results from a work-based project ( Fergusson et al. , 2019b ). The significance of that model was to show how reflective practice can be applied at all stages of work-based research: operationalising research; working with secondary data; gathering primary data; and analysing data. Activation of that reflective model in various real-world, international work-based settings has also been presented ( Fergusson et al. , 2020a ).

Such an approach is said to result in a triple dividend; that is: a benefit to oneself, to one's organisation and to original knowledge creation about work ( Fergusson et al. , 2018 ). In a more recent conceptualisation of WBL's approach to transdisciplinarity, I have posited a fourth so-called “futures” dividend, that is, a dividend “which illuminates the way forward for a less harmful and more socially responsible, resilient approach to work and its contemporary problems. Increasingly, projects related to the ‘future’ embrace principles and practices which enhance awareness, require post-conventional responses, encourage a ‘vision to action’, and help navigate the Anthropocene” ( Fergusson and van der Laan, 2021b , p. 19).

For these reasons, learning by reflecting has been centred at the heart of the work-based proto-theoretical model in Figure 1 . In this model, reflective practice should be seen as foundational to, and implicitly involved in, every subsequent mode of learning associated with work. My aim here is not to propose an overarching synthesis of all possible modes of learning but to open a dialogue about which modes might be applied in WBL through reflecting.

Learning by …

On the basis of learning by reflecting, 12 modes of learning can be identified as they relate to WBL, and these have been grouped into four main types: Group A: Empathetic Learning , which includes learning by (1) chatting, storytelling and yarning, (2) listening and asking questions and (3) observing, making and tinkering; Group B: Action-Oriented Learning , which includes learning by (4) doing and practicing, (5) imitating, discussing and repeating and (6) sketching, drawing and visualising; Group C: Scholarly and Applied Learning , which includes learning by (7) reading and writing, (8) researching and experimenting and (9) solving real-world problems; and Group D: Social and Environmental Learning , which includes learning by (10) teaching and training, (11) cooperating and helping others and (12) creating sustainable futures.

The 12 modes of learning adopted in this paper resulted from an investigative survey of the literature on work and learning in the eight categories described by Fergusson and van der Laan (2021a) , namely: (1) work-related learning (WRL); (2) work-based learning (WBL); (3) workplace learning (WPL); (4) work-applied learning (WAL); (5) work-based training (WBT); (6) work-integrated learning (WIL); (7) workplace-based learning (WPBL); and (8) work-based education (WBE).

The four main groups of learning have been developed as high-level constructs to help capture the essential nature of the modes identified within them and to thereby facilitate conceptual arrangement. How these four groups of learning organisationally relate to learning by reflecting and how the entire WBL endeavour is situated within a range of work environments, workplaces and domains of practice are shown in Figure 1 . In this representation of learning, more organic, informal modes of learning have been identified closer to the centre and more concrete, formal approaches closer to the outer permeable region which lies between the individual learner and the three main environments in which work is carried out: work spaces , which include any setting in which work is performed, such as an atelier, workshop or “in the field”; workplaces , which include formalised places of employment, such an offices or factories; and domains of practice , which somewhat formalise work but are more commonly associated with a combination of direct and in-direct service, such as those provided by social workers and in-home carers.

However, this organisation of modes of learning could be misleading for several reasons. Firstly, it could be assumed that the modes of learning are definitive, when in fact they are representational; other modes exist and should be considered in WBL. Secondly, it could be assumed that the model is hierarchical, with those modes of learning closer to reflective practice more important than those further away; this is not the case: all modes are potentially of equal importance, and no special value has been placed on any one mode.

Thirdly, it could be assumed that each mode of learning is discrete and practised in isolation; this is not the case: in work environments, learning is an organic, holistic and continuous process, occurring informally (such as chatting and observing), non-formally (such as cooperating and helping others) and formally (such as researching and training).

And finally, it could be assumed that the proto-theoretical model in Figure 1 is a reductionist one and that each mode of learning is independent of all others; this is also not the case: learning is an interdependent phenomenon, and different modes of learning overlap and are congruent with all others, particularly when applied in messy work environments. Such a phenomenon can be seen in the example of “learning by observing”, which is severally embraced as a strategy in imitating, drawing, visualising and so on.

For these reasons, Figure 2 advances Figure 1 by representing the dynamic relationship between modes of learning and how reflecting in WBL is considered not only foundational to every mode but also informs, guides and inspires every aspect of lifelong learning. Thus, the “doing” of WBL can include listening, observing, chatting, storytelling, imitating, repeating, reading, writing, sketching, drawing and visualising, and each may occur for every learner in a continuous and dynamic interrelationship of work experience and reflection.

In the same way, “research”, particularly in higher education, can extend WBL to solving work-related problems, cooperating and helping others, teaching and training colleagues and improving organisations, government, society and the environment by creating sustainable futures, and may be the domain of multiple learner types. What is common to them all is the ability to reflect in a meaningful and critical way. The following four groupings seek to briefly explain the properties of the 12 modes of learning in the proto-theoretical model, along with representative citations from the work and learning literature.

Group A: empathetic learning

Learnings in Group A shown in Table 1 represent bidirectional channels of exchange within learners at work. Empathetic learning, as a generalised construct, relates in large part to the use of work environments as places of human exchange through what has become known as “appreciative inquiry”. This is why Wall et al . (2017 , p. 131) contend “in terms of learning and emotion in workplaces, evidence indicates that when people are more emotionally (and positively) engaged, workplace learning is more effective”.

Learning by chatting, storytelling and yarning

Research has found that informal social interactions, such as chatting at work, can improve cognitive function ( Ybarra et al. , 2011 ). One of the ways this approach to learning is operationalised in organisations and work is storytelling; according to Gabriel (2000 , p. 2), “stories open valuable windows into the emotional, political, and symbolic lives of organisations”. For many Indigenous people, informal and semi-formal oral communication is the most significant medium through which knowledge, culture and kinship are produced, practised and maintained. In Australia, for example, this diverse set of verbal practices associated with learning from elders is called yarning ( Walker et al. , 2014 ). These types of bidirectional verbal exchanges encourage empathy and learning about one's work environment and broader social and ecological context.

Learning by listening and asking questions

Listening is central to learning and “careful listening … can propel new cycles of expansive learning and agency” ( Bang and Vossoughi, 2016 , p. 182). Learning occurs when participants in work environments listen to each other and ask questions. For example, so-called “quality questioning” is viewed as essential for all learners ( Walsh and Sattes, 2016 ).

Learning by observing, making and tinkering

Learning by observation is also central to learning, and research has found that learners learn more if they have a visual experience and then a verbal instruction rather than a verbal instruction alone ( Bläsing et al. , 2018 ). Such findings rely on what is called feedback-in-practice and consequential learning ( DiGiacomo and Gutiérrez, 2016 ) where observation can lead to “trying out” or tinkering, both of which relate to fluid experimentation and open exploration. “In its ideal form, tinkering should be an ongoing process”, according to DiGiacomo and Gutiérrez (2016 , p. 144), because “activities that promote a ‘live’ quality, such that they allow learners to see how the parts of an activity relate the its whole, are especially important for engaging learners over time”. Thus, individuals can glean a great deal from watching their colleagues work and by making and tinkering themselves.

Group B: action-oriented learning

Learnings in Group B are presented in Table 2 . One of the primary features of WBL is its adherence to action learning, a mode of learning entwined with reflective practice ( Costley and Lester, 2012 ). In my model, “action” embraces not only generic doing and practicing, but also imitating, discussing, repeating, sketching, drawing and visualising.

Learning by doing and practicing

Also called “experiential learning”, learning by doing occurs when the learner is directly in touch with the realities being studied, practiced or experienced. Such a view has a considerable history in education, encapsulating the work of Dewey, Piaget and others (e.g. Kolb and Kolb, 2005 ). The principle of learning by doing is predicated on the notion that doing is better than watching ( Koedinger et al. , 2015 ) and has been embraced, for example, by “do-it-yourself” civic actors who launch initiatives to green cities and combat climate change by initiating “a learning and adjustment process, both for the urban space in question and themselves” ( Cloutier et al ., 2018 , p. 285).

Learning by imitating, discussing and repeating

Also referred to severally as mimetic learning, reinforcement learning and guided learning, imitation along with discussing and repeating, particularly when coupled with observation and other forms of doing, contribute to “holistic learning”. Imitating and repeating have become less fashionable in Western contemporary higher education, but in work contexts, they are suited to mechanical and systems learning, often drawing from expert demonstrations or working examples ( Li  et al. , 2017 ).

Learning by sketching, drawing and visualising

While learning by observing and seeing can be linked to watching, they are also fundamental to perception and looking in the arts. Thus, observing is seen as a critical learning when associated with sketching, drawing and visualising, modes of learning which often accompany scientific inquiry (e.g. Gameira et al. , 2018 ) and work practices and processes. These three modes mean, for example, in the context of design thinking that “ongoing experimentation and testing as concepts are made more concrete and users are involved in developing or assessing prototypes. Field experiments, prototypes, and visualization techniques such as drawings and pictures can be used to enable continuous learning and concept sharing and [can] clarify the characteristics of the idea and make it more amenable to critical consideration and feedback” ( Micheli et al. , 2019 , p. 136). The same conclusion apparently applies to learning by story-telling, which Micheli et al. describe as a form of visualisation, and the work of Pink (2015) on methodologies in visual ethnographic research also bear directly on learning by observing and seeing.

Group C: scholarly and applied learning

Learnings in Group C are presented in Table 3 . The last 25 years have seen an increased focus in higher education, industry and government on WBL. As a result, the issue of harnessing research and scholarship to address work-related problems has been highlighted, resulting in the implementation of government-supported WBL higher degree research programmes, such as the Professional Studies programme at University of Southern Queensland in Australia with which the author is affiliated. The three main elements of scholarship in WBL are learning by reading and writing, learning by researching and experimenting and learning by solving real-world problems.

Learning by reading and writing

Reading and writing, the so-called “academic literacies”, are fundamental to all forms of learning, not just learning which occurs in the workplace. Researchers therefore concur that reading and writing are foundational to WBL and are crucial to the development of metacognitive skills and work-related success. Benefits from reading and writing include a range of affective and cognitive outcomes. Such initiatives as writing workshops, which seek to inculcate scholarly habits associated with learning by reading and writing, may act as a “force for deeper change” ( Boose and Hutchings, 2016 , p. 42).

Learning by researching and experimenting

Work-based research has emerged in recent years as a powerful tool for changing the future of work ( McCormack and Kiss, 2015 ), and its linkages to reflection and experimentation have been identified ( Grosemans et al. , 2015 ). However, evidencing the full range and extent of the impact of research on work environments is beyond the scope of this study.

Learning by solving real-world problems

In work-based research, the individual learner explores a topic related to her professional role within an organisation or community of practice. Her inquiry typically involves pragmatic, insider research as a way of investigating real-world problems and thereby improves practice in a broad professional context, utilising reflective practice, creating positionality and contributing to constructive impact. Miller and Maelloro (2016) suggest that collective reflection and reflective observation directly contribute to such an outcome.

Group D: social and environmental learning

Learnings which involve organisations, communities, societies and the global community are summarised in Table 4 and include learning by teaching and training, by cooperating and helping others and by creating sustainable futures. These modes of learning relate to the most applied, socially driven learning types in WBL, often identified with altruism and social activism and justice.

Learning by teaching and training

Learning by reflecting lies at the heart of learning through teaching and training ( Margolinas et al ., 2005 ). Work environments can provide opportunities for didactic interaction, but in such circumstances, the teacher or trainer can (and should) be a learner as much as a leader. Teaching, training and learning in the workplace always provide for bidirectionality of knowledge and skills, and everyone thereby becomes an epistemic agent. Criteria associated with this mode include: learning how to unlearn what we think we know to make room for new knowledge; remembering what it is like to be a student; when at work, everyone learns; we learn empathy when we teach others; only practice teaches theory; teachers teach, but great teachers learn; and great teachers find ways to inspire themselves through learning new things. Moreover, teachers learn through reflection ( Grosemans et al. , 2015 ).

Learning by cooperating and helping others

An individual can learn by giving, cooperating and helping others in the work environment. This is sometimes called “service learning” or “citizen science”. Benefits of learning to provide include insights into one's own learning and thus go beyond simple reciprocated assistance ( Shah et al ., 2018 ); learning by cooperating in citizen science has been described as a way to “develop positive action on behalf of the environment” ( Phillips et al. , 2018 , p. 1).

Learning by creating sustainable futures

Aboytes and Barth (2020 , p. 993) call learning by creating sustainable futures “transformational learning” and consider it “critical to enhancing and catalysing social transformations towards sustainability” through “learning that leads to the transformation of unsustainable mindsets” (p. 994). Allen et al . (2019 , p. 781) likewise maintain that such an approach to learning meets “the need for an ecocentric stance to sustainability that reflexively embeds humans in —rather than detached from —nature”. Perhaps the most urgent of all 12 modes, learning by creating sustainable futures means learning to live sustainably on this planet, drawing on resources that can be replenished and are in tune with a balanced ecosphere and describing and changing currently unsustainable patterns of human thinking and activity. Work-based learning and research have the capability to contribute to this endeavour, and examples emerging from WBL higher degree programmes are now doing so ( Fergusson et al. , 2020b ).

The proto-theoretical model presented in Figures 1 and 2 locates learning by reflecting at the centre of WBL, a concept consistent with the published literature on work and learning. WBL also provides participants of work with multiple opportunities to learn (and become more experienced and qualified) and similarly provides multiple modes through which learning can occur on the basis of knowledge and skills in reflective practice. Moreover, WBL by necessity must accommodate many different types of learner and must be open, flexible and inclusive enough to expect and embrace diversity, change and fit-for-futures research.

Ideally, work environments, and the higher degree WBL programmes which support them, should provide learning via every available mode of learning, thereby allowing students to find their own best orientation to learning and encourage it by any means. Such a prospect has the potential for WBL to result in delivering a quadruple dividend, a significant benefit to: oneself; an organisation; original knowledge; and perhaps most importantly resulting in a benefit to a more sustainable human and social future.

Proto-theoretical model of learning by reflecting and other modes of learning in WBL

Dynamic and interconnected relationship of learning by reflecting to modes of learning by other means in WBL

Empathetic learning with principles, sample quotes and citations

Action-oriented learning with principles, sample quotes and citations

Scholarly and applied learning with principles, sample quotes and citations

Social and environmental learning with principles, sample quotes and citations

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Models and Tools of Knowledge Acquisition

• First Online: 30 May 2021

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• Rojers P. Joseph   ORCID: orcid.org/0000-0003-4794-9857 19 &
• T. M. Arun   ORCID: orcid.org/0000-0001-9434-5429 19  

Part of the book series: Modeling and Optimization in Science and Technologies ((MOST,volume 18))

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The growth of communication channels, personal computers and the internet has radically altered the importance and use of knowledge within an economy, leading to the emergence of a knowledge economy. Digital technologies have transformed the way firms acquire and process external knowledge for effective strategy-making. In a knowledge/digital economy, firms use novel techniques such as Competitive Intelligence (CI) as well as emerging technologies such as Application Programming Interfaces (APIs) and Artificial Intelligence (AI) for knowledge acquisition. In this paper we discuss how CI, AI, and API enhance the effectiveness of the knowledge acquisition process by firms. Overall, the tools, models, and emerging technologies used in knowledge acquisition and knowledge management lead to cost savings at various levels, greater convenience for users, and more effective acquisition and use of knowledge by firms.

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Joseph, R.P., Arun, T.M. (2021). Models and Tools of Knowledge Acquisition. In: Patnaik, S., Tajeddini, K., Jain, V. (eds) Computational Management. Modeling and Optimization in Science and Technologies, vol 18. Springer, Cham. https://doi.org/10.1007/978-3-030-72929-5_3

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1.1 Methods of Knowing

Learning objectives.

• Describe the 5 methods of acquiring knowledge
• Understand the benefits and problems with each.

Take a minute to ponder some of what you know and how you acquired that knowledge. Perhaps you know that you should make your bed in the morning because your mother or father told you this is what you should do, perhaps you know that swans are white because all of the swans you have seen are white, or perhaps you know that your friend is lying to you because she is acting strange and won’t look you in the eye. But should we trust knowledge from these sources? The methods of acquiring knowledge can be broken down into five categories each with its own strengths and weaknesses.

The first method of knowing is intuition. When we use our intuition, we are relying on our guts, our emotions, and/or our instincts to guide us. Rather than examining facts or using rational thought, intuition involves believing what feels true. The problem with relying on intuition is that our intuitions can be wrong because they are driven by cognitive and motivational biases rather than logical reasoning or scientific evidence. While the strange behavior of your friend may lead you to think s/he is lying to you it may just be that s/he is holding in a bit of gas or is preoccupied with some other issue that is irrelevant to you. However, weighing alternatives and thinking of all the different possibilities can be paralyzing for some people and sometimes decisions based on intuition are actually superior to those based on analysis (people interested in this idea should read Malcolm Gladwell’s book Blink) [1] .

Perhaps one of the most common methods of acquiring knowledge is through authority. This method involves accepting new ideas because some authority figure states that they are true. These authorities include parents, the media, doctors, Priests and other religious authorities, the government, and professors. While in an ideal world we should be able to trust authority figures, history has taught us otherwise and many instances of atrocities against humanity are a consequence of people unquestioningly following authority (e.g., Salem Witch Trials, Nazi War Crimes). On a more benign level, while your parents may have told you that you should make your bed in the morning, making your bed provides the warm damp environment in which mites thrive. Keeping the sheets open provides a less hospitable environment for mites. These examples illustrate that the problem with using authority to obtain knowledge is that they may be wrong, they may just be using their intuition to arrive at their conclusions, and they may have their own reasons to mislead you. Nevertheless, much of the information we acquire is through authority because we don’t have time to question and independently research every piece of knowledge we learn through authority. But we can learn to evaluate the credentials of authority figures, to evaluate the methods they used to arrive at their conclusions, and evaluate whether they have any reasons to mislead us.

Rationalism

Rationalism involves using logic and reasoning to acquire new knowledge. Using this method premises are stated and logical rules are followed to arrive at sound conclusions. For instance, if I am given the premise that all swans are white and the premise that this is a swan then I can come to the rational conclusion that this swan is white without actually seeing the swan. The problem with this method is that if the premises are wrong or there is an error in logic then the conclusion will not be valid. For instance, the premise that all swans are white is incorrect; there are black swans in Australia. Also, unless formally trained in the rules of logic it is easy to make an error. Nevertheless, if the premises are correct and logical rules are followed appropriately then this is sound means of acquiring knowledge.

Empiricism involves acquiring knowledge through observation and experience. Once again many of you may have believed that all swans are white because you have only ever seen white swans. For centuries people believed the world is flat because it appears to be flat. These examples and the many visual illusions that trick our senses illustrate the problems with relying on empiricism alone to derive knowledge. We are limited in what we can experience and observe and our senses can deceive us. Moreover, our prior experiences can alter the way we perceive events. Nevertheless, empiricism is at the heart of the scientific method. Science relies on observations. But not just any observations, science relies on structured observations which is known as systematic empiricism.

The Scientific Method

The scientific method is a process of systematically collecting and evaluating evidence to test ideas and answer questions. While scientists may use intuition, authority, rationalism, and empiricism to generate new ideas they don’t stop there. Scientists go a step further by using systematic empiricism to make careful observations under various controlled conditions in order to test their ideas and they use rationalism to arrive at valid conclusions. While the scientific method is the most likely of all of the methods to produce valid knowledge, like all methods of acquiring knowledge it also has its drawbacks. One major problem is that it is not always feasible to use the scientific method; this method can require considerable time and resources. Another problem with the scientific method is that it cannot be used to answer all questions. As described in the following section, the scientific method can only be used to address empirical questions. This book and your research methods course are designed to provide you with an in-depth examination of how psychologists use the scientific method to advance our understanding of human behavior and the mind.

• Gladwell, M. E. (2007). Blink: The power of thinking without thinking.  How to think straight about psychology (9th ed.). New York: Little, Brown & Company. ↵

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