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Effective Research Assignments

Identify learning goals., clarify expectations., "scaffold" the assignment., test the assignment., collaborate with librarians..

Assignment Ideas
Studies on Student Research

Acknowledgement

These best practices were adapted from the handout "Tips for Designing Library Research Assignments" developed by Sarah McDaniel, of the Univ. of Wisconsin-Madison Libraries. Many thanks to her for permission to reuse this resource.

See Assignment Ideas to explore different possible approaches beyond a traditional research paper.

What abilities would you like students to develop through the assignment?
How will the learning goals and their importance be communicated in the assignment?

Your students may not have prior experience with academic research and resources. State (in writing) details like:

the assignment's purpose,
the purpose of research and sources for the assignment,
suggested resources for locating relevant sources,
expected citation practices,
terminology that may be unclear (e.g. Define terms like "database," "peer reviewed"),
assignment length and other parameters, and
grading/evaluation criteria ( Rubrics are one way to communicate assessment criteria to students. See, for example, AAC&U's VALUE rubric for information literacy .)

Also consider discussing how research is produced and disseminated in your discipline, and how you expect your students to participate in academic discourse in the context of your class.

Breaking a complex research assignment down into a sequence of smaller, more manageable parts:

models how to approach a research question and how to manage time effectively,
empowers students to focus on and to master key research and critical thinking skills,
provides opportunities for feedback, and
deters plagiarism.

Periodic class discussions about the assignment can also help students

reflect on the research process and its importance
encourage questions, and
help students develop a sense that what they are doing is a transferable process that they can use for other assignments.

By testing an assignment, you may identify practical roadblocks (e.g., too few copies of a book for too many students, a source is no longer available online).

Librarians can help with this process (e.g., suggest research strategies or resources, design customized supporting materials like handouts or course research guides).

Subject librarians can explore with you ways to support students in their research.

Next: Assignment Ideas >>
Last Updated: Oct 20, 2022 8:56 AM
URL: https://libguides.rowan.edu/research_assignments

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Sources and Your Assignment

The first step in any research process is to make sure you read your assignment carefully so that you understand what you are being asked to do. In addition to knowing how many sources you're expected to consult and what types of sources are relevant to your assignment, you should make sure you understand the role that sources should play in your paper.

For example, a common assignment at Harvard will ask you to test a theory by looking at that theory in relation to a text or series of texts. In this type of assignment, one source—i.e., the source that lays out the theory—will play a large role, as will the text or texts you're considering in relation to that theory. You may not be expected to consult any other sources. On the other hand, for an assignment that asks you to stake out a position for yourself in an ongoing debate, you may need to consult a number of sources to figure out the major positions in that debate before you can decide where you stand on the issue. For this type of assignment, you may also rely on sources to help you understand the context of the debate, to find the evidence that you will analyze to figure out where you stand on the issue, and to learn the definitions of relevant terms. Yet another assignment might ask you to formulate your own question on a broad topic and then answer that question. In this case, you will likely use sources in several different ways—as background information that will help you arrive at a question, as evidence and expert commentary that will help you answer that question, and as opposing views that you will take into consideration as you formulate your argument.

Locating Sources
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Effective research assignments: home, communicate your expectations.

Assess the quality of the sources your students cite as part of their overall grades, and explain clearly in your rubric how that evaluation will be made.
Spell out your expectations regarding sources. Instead of asking for scholarly sources, for example, you could ask your students to "cite at least two peer-reviewed journal articles and two primary sources".
Explain terminology and provide background regarding scholarly publishing. What’s peer-review? What are some differences between scholarly books and journal articles? When should one consult popular news sources? What’s a primary source?
Clearly communicate which style manual is required.
Include a policy on plagiarism in the assignment and discuss the purposes of proper attribution. Discuss examples: does paraphrasing another author’s ideas require a citation?
Provide examples of topics that are appropriate in scope for the assignment at hand, and provide feedback to individual students as they begin to develop and refine their topics.

Design and test your assignment An effective research assignment targets specific skills, for example, the ability to trace a scholarly argument through the literature or the ability to organize consulted resources into a bibliography.

Test the assignment yourself. Can you find the kinds of sources required? Are you required to evaluate the sources you find?
Ask students for feedback on the assignment. Are they having problems finding relevant materials? Do they understand your expectations?
If the assignment is particularly demanding, consider dividing a single research project into multiple assignments (outline, draft, final draft), each one focusing on a different aspect of the research process.

Ideas for alternative research assignments

Assign an annotated bibliography in which students identify primary and secondary sources, popular and scholarly publications, and detect and comment on forms of bias.
Ask for students to document the search tools they use (library catalog, article databases, Google, etc.) for a research paper and to reflect on the kinds of information they find in each.
Provide a resource list or a single source from which students’ research should begin. Discuss the utility of known sources for identifying keywords, key concepts, and other citations to inform further searching.
Assign students to prepare a guide for introducing their classmates to the essential literature on a given topic.
Have students compile a glossary of important terms specific to a given topic in your discipline.
Require students to edit an anthology of important scholarship on a specific topic and write an introduction explaining the development of the field over time.

Avoid these common mistakes

Since many scholarly sources are available online, it can be confusing for students when “Internet” or “Web” sources are forbidden. It’s helpful to describe why certain sources (such as Wikipedia) may not be allowed.
Make sure the resources required by the assignment are available to your students in the library or in library databases. You can also place hard-to-find required sources on course reserve .
Last Updated: May 4, 2022 10:41 AM
URL: https://guides.lib.berkeley.edu/effective-research-assignments

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Designing Research Assignments: Assignment Ideas

Student Research Needs
Assignment Guidelines
Assignment Ideas
Scaffolding Research Assignments
BEAM Method

Assignment Templates

Research diaries offer students an opportunity to reflect on the research process, think about how they will address challenges they encounter, and encourage students to think about and adjust their strategies.

Research Diary Template
Research Diary Instructions

Alternative Assignments

There are many different types of assignments that can help your students develop their information literacy and research skills.

The assignments listed below target different skills, and some may be more suitable for certain courses than others.

<< Previous: Assignment Guidelines
Next: Scaffolding Research Assignments >>
Last Updated: Jun 9, 2022 12:23 PM
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Research Assignment Design: Overview

Student Learning Outcomes
Evaluating Student Work
Generative AI

Prioritize your learning outcomes

Students can't do it all. Pick what to focus on. For the beginning researcher, research can be a complicated process with many steps to master effectively. Your assignment might want to prioritize some of those over others.

Students experience a greater cognitive load when researching because they lack domain knowledge. You can help students focus their energies by ensuring your assignment matches your priorities.

For example, to prioritize synthesizing arguments, design an assignment around reading and writing with sources, and limit the need for finding sources. To prioritize identifying the scope of research on a topic, require searching for sources.

How do I do this?

Determine and prioritize learning goals specific to the research process .
Imagine a student working through the assignment. Are there parts of it that demand a lot of work, but that don't match your priorities? If so, rethink the assignment.

Focus on the research and writing process

Prompts should address both the steps along the way (picking a topic, collecting data, synthesizing sources) and the completed assignment. When instructions focus only on the final product, students will view them as a checklist to complete.

For example, requiring a certain number of sources for a paper directs students' attention to the end product. Students will pick the first sources they find, rather than understanding the process of finding many possible sources, then selecting the best ones.

Give clear and concise directions, with explanations and examples, about why you want something a certain way.
Make learning objectives explicit, and provide feedback for each step of the research experience.
Provide opportunities for students to reflect on their learning.
Allow students time to explore and reframe as they research.
Discuss how students will know they've found enough information.

Scaffold learning

Break down and explicitly teach the different aptitudes students need to be successful. Research can overwhelm students, especially those new to the process or discipline.

Break your assignment down into smaller tasks to ensure that students reach learning objectives successively and successfully.
Approach this as an opportunity to help students develop research skills. Don't assume students already know how to do research. Learning is iterative, so even if they've had a library research session, a review is useful.
Recognize the emotional toll of research and give students the time they need to experience the full spectrum of feelings, as part of the instructional design.
Provide worksheets, handouts, or activities that help students navigate specific aspects of the research process.
Assist students over common stumbling blocks. What will get them past bottlenecks to learning in your discipline?

Create an authentic learning experience

Make your assignment relevant to real life experiences and skills. Students learn best and successfully transfer what they're learning when they connect with the assignment, feel the excitement of discovery, or solve challenges. Through disciplinary and experiential learning, students develop different perspectives from which to view the world.

Encourage curiosity. Give students the chance to experience some of the messiness of research, while limiting how far off track they can get through periodic check-ins.
Show students how to practice reading, research, and writing in your discipline. All these require interrelated, separate skills.
Address how students can transfer knowledge and skills.
Consider problem-based learning, have students examine real-world issues.

Need More Help?

Ways librarians can help.

Discuss your learning objectives and options for assignments with you
"Test-drive" your assignment to ensure students will be successful
Identify why students struggle and how to help them
Ensure appropriate resources are available
Identify library instructional resources to link in Canvas
Provide research instruction for your class
Research Assignment Stipend Support for your collaboration with a librarian on a new assignment.
How to Write an Effective Assignment Harvard University Derek Bok Center for Teaching and Learning

See Example Assignments

Introductory Research Paper Prompt
Executive Summary Assignment
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Last Updated: Mar 22, 2024 3:15 PM
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Effect of Assignment Choice on Student Academic Performance in an Online Class

Brief Practice
Published: 26 February 2021
Volume 14 , pages 1074–1078, ( 2021 )

Cite this article

Hannah MacNaul ORCID: orcid.org/0000-0001-6992-9991 1 , 2 ,
Rachel Garcia ORCID: orcid.org/0000-0003-1805-4499 2 ,
Catia Cividini-Motta ORCID: orcid.org/0000-0001-5679-9294 2 &
Ian Thacker ORCID: orcid.org/0000-0002-2492-2929 1

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Choice of assignment has been shown to increase student engagement, improve academic outcomes, and promote student satisfaction in higher education courses (Hanewicz, Platt, & Arendt, Distance Education , 38 (3), 273–287, 2017 ). However, in previous research, choice resulted in complex procedures and increased response effort for instructors (e.g., Arendt, Trego, & Allred, Journal of Applied Research in Higher Education , 8 (1), 2–17, 2016 ). Using simplified procedures, the current study employed a repeated-measures with an alternating-treatments design to evaluate the effects of assignment choice (flash cards, study guide) on the academic outcomes of 42 graduate students in an online, asynchronous course. Slight differences between conditions were observed, but differences were not statistically significant.

The Effect of Choice on Student Performance in Online Graduate Classes

Self-regulated spacing in a massive open online course is related to better learning

Matching Efficiency of Admission Procedures in Online-Education

Avoid common mistakes on your manuscript.

As access to the internet increases, more students pursuing higher education are completing online programs. In fact, nearly 50% of master’s-level applied behavior analysis training programs in the United States offer courses in an online format (Behavior Analyst Certification Board, 2021 ). Given the increase of students in online courses and programs, investigating instructional procedures to support students in meeting learning outcomes has become critical. In learner-centered teaching (LCT; Weimer, 2013 ), instructors aim to motivate students by giving them some control over the learning process, such as choice of assignments and choice of assignment deadlines.

In the academic context, the opportunity to select between two or more concurrently available assignments has been shown to increase student engagement, exam scores, and student satisfaction (e.g., Hanewicz et al., 2017 ). Moreover, various assignment formats—that is, flash cards and study guides—are empirically supported strategies that help students build fluency with material and improve efficiency in studying, respectively (Tincani, 2004 ). In a recent study, Jopp and Cohen ( 2020 ) identified only four studies (Arendt, Trego, & Allred, 2016 ; Cook, 2001 ; Hanewicz, Platt, & Arendt, 2017 ; Rideout, 2017 ) in which students were given a choice of assignments and, in all of these studies, choice was associated with a positive outcome (e.g., increased engagement and exam scores). However, in these studies, the arrangement of procedures in order to offer choice resulted in complex point systems (e.g., Rideout, 2017 ), a large number of assignment choices (e.g., 59 in Arendt et al., 2016 ), or a vast number of different due dates (e.g., Arendt et al., 2016 ). To address these limitations, Jopp and Cohen kept the number of assignments available in the course and their relative weights the same as in the previous iteration of the course; however, for three of the required assignments, students could choose one of the three available assignment options. In their study, assignment choice increased satisfaction with the course but did not increase learning outcomes (i.e., grade) in comparison to a previous semester when the course did not include choice. Nevertheless, students indicated that they did not have a good understanding of all of the different assignment options. Furthermore, in previous studies, students did not experience both the choice and no-choice conditions; thus, individual differences between groups may have moderated outcomes (e.g., Rideout, 2017 ).

As noted previously, choice has had a positive impact on student engagement; however, further research on procedures that can aid in the mastery of academic content while requiring few resources is warranted. This study sought to evaluate the effects of assignment choice on student academic outcomes. To extend this line of research, this study incorporated choice of assignment (i.e., flash cards and study guides) in a simpler manner, ensured that all students experienced all experimental conditions (i.e., using an alternating-treatments design), and exposed students to both assignments prior to the onset of the study.

Participants and Setting

Forty-two graduate students across two cohorts (fall 2019: n = 25; spring 2020: n = 17) who were enrolled in a fully online master’s program participated in the current study. Most students were female ( n = 39), and geographically, students were located around the United States. All students in each section participated in the study and were completing this course in partial fulfillment of the requirements to become a Board Certified Behavior Analyst. The course, which covered functional assessment methods, and instructor were the same across both cohorts. The course was administered via Canvas, a learning management platform previously used by the students in other courses. This was an 8-week asynchronous course wherein students were not required to meet on a certain day and time but had to progress through a module per week, and therefore the entire course, by certain deadlines. Modules were identical in setup, including a module description with learning objectives, a video introduction from the instructor, required readings, prerecorded lectures, a discussion board, and a quiz. Each component of the module was introduced in succession, meaning that completion of one task allowed the student to access the next task in the sequence. Additionally, in six out of eight modules, students completed an interactive practice assignment.

Materials included instructor-designed practice assignments (i.e., flash cards, study guides) developed using the online website GoConqr ( www.goconqr.com ). The flash cards and study guides covered the same subject matter and content areas (e.g., key terms and definitions), and both required approximately 15 min of the instructor’s time to develop. The practice assignments were embedded into Canvas and were presented either concurrently (i.e., choice condition) or in isolation (i.e., no-choice condition).

Dependent Variables

Dependent variables included student academic performance and preference of assignment format. Student academic performance consisted of the average score of all students per module quiz. Quizzes were worth a total of 20 points, and each consisted of scenario-based, multiple-choice, and short-answer questions, which were graded using an instructor-developed rubric. Student preference of assignment format was determined by the proportion of students who selected to complete each of the assignments during choice conditions.

Experimental Design and General Procedures

A repeated-measures with an embedded alternating-treatments design was employed to compare student performance across conditions. To mitigate any foreseen testing or sequence effects, treatment conditions were counterbalanced across cohorts and included choice, no-choice, and no-assignment (i.e., control condition) conditions. Across all conditions, students completed assigned readings, viewed the module lecture, and participated in the discussion board. Then, they either completed a practice assignment and a quiz (e.g., choice and no-choice conditions) or went straight from the discussion board to the quiz (e.g., no-assignment condition). When a practice assignment was available (choice and no-choice conditions), students were instructed to dedicate at least 10 min to the assignment, and they could complete the assignment as many times as desired until they reached a score of 100%. To receive full credit (i.e., 20 points), students were required to submit a screenshot of the score received, which also included the time spent on the assignment; thus, if a screenshot was not submitted and/or showed that students had not spent 10 min on the assignment, the students received zero points.

Exposure Phase

Students received instructions on the completion of each assignment type and completed an example of each assignment. However, these assignments covered content related to the syllabus and course structure. This exposure phase was implemented to give students the opportunity to experience both types of practice assignments prior to allowing them to choose between the two.

Choice Condition

In the choice condition, students had the option to select one assignment to complete, either flash cards or a study guide. The Canvas function Mastery Paths was utilized to present the choice of assignments. First, students selected “true” or “false” in response to a pledge statement (i.e., “I have completed all readings for this module, viewed the lecture, and participated in the discussion board.”). Following submission of a “true” response, students were given a choice between the two practice assignments. Upon the student’s selection of an assignment, the other option was no longer available. The selection of “false” in response to the pledge statement would redirect the student to the start of the module; however, no students selected “false” throughout the course of the study.

No-Choice Condition

In the no-choice condition, an assignment, either flash cards or a study guide, was assigned to the students by the instructor. There was no pledge statement, but all other components remained the same as in the choice condition.

No-Assignment Condition

In the no-assignment (i.e., control) condition, there was no pledge statement or practice assignment available for students to complete and, therefore, no points available. All other components remained the same as in the choice condition.

Procedural Fidelity

To assess procedural fidelity, a research assistant reviewed the Canvas page and recorded whether each student completed all components of each module (i.e., completing assigned readings, viewing lectures, and participating in the discussion board) in the prescribed sequence and prior to accessing the module assignment (choice and no-choice conditions only). In addition, during the choice and no-choice conditions, data were also collected on whether each participant completed only one practice assignment. Procedural fidelity was obtained for 100% of modules across both cohorts, and the average procedural fidelity score was 100%. It is important to note that data from Cohort 1 Module 1 are excluded from the procedural fidelity scores and the average quiz score across conditions because 16 of 25 students completed both the flash card and study guide assignments. Subsequently, procedural modifications were made.

Student average quiz scores were highest in the choice condition for both cohorts, with a mean of 17.29 ( SD = 2.79, n = 99) across cohorts (see Table 1 and Fig. 1 ). Although student performance was slightly higher in the choice condition compared to the no-choice ( M = 16.65, SD = 2.62, n = 123) and no-assignment ( M = 17.00, SD = 1.83, n = 82) conditions, the differences in performance between conditions, as well as relative differences between conditions, were not statistically significant for any pairwise comparison (all p > 16). A one-way analysis of variance revealed no significant differences in mean performance scores between conditions, F (2, 301) = 1.87, p = .157. Indeed, no two conditions revealed statistically significant differences between mean quiz scores when follow-up Benjamini–Hochberg pairwise comparisons were used ( p choice vs. no choice = .17, p choice vs. no assignment = .43, p no choice vs. no assignment = .43). Further, relative gains between conditions also revealed no statistically significant pairwise differences between conditions when comparing normalized gain scores ([ M post − M pre ]/ SD ) between conditions ( p choice vs. no choice = .28, p choice vs. no assignment = .73, p no choice vs. no assignment = .21). Similarly, a comparison between the no-assignment (control) condition and the remaining two conditions using planned contrasts revealed no statistically significant differences in mean performance ( t = .24, p = .810). The quiz scores for each module are presented in Table 1 . For Cohort 2, the no-assignment condition resulted in a higher average quiz score ( M = 16.85, SD = 2.06, n = 34) compared to the no-choice condition ( M = 15.4, SD = 2.58, n = 51).

Average cohort performance across conditions. Note. Bars represent 95% confidence intervals

The frequency of students’ selection between the two practice assignment modalities (e.g., student preference of assignment format) also yielded negligible differences. Across both cohorts, in 51.5% (49 of 101) of opportunities, students chose to complete flash cards, and in 48.5% (52 of 101) of opportunities, students chose to complete the study guide during choice conditions. The difference between these proportions was not statistically significant at conventional levels (χ 2 = .181, p = .67). However, individual data indicate that certain students often chose the same assignment across modules (data are available upon request).

In this study, choice was designed in a simplified manner compared to previous research, thus increasing the feasibility of implementation for instructors. In addition, the influence of individual differences on mean values was minimized by employing an alternating-treatments design. In the current study, providing students with a choice of assignment improved performance only slightly and, ultimately, did not have any negative effects. Furthermore, based on the aggregate data, students did not show a preference for a particular assignment; this is not consistent with the findings of previous research (e.g., Jopp & Cohen, 2020 ) in which a large portion (48%–88% across the three opportunities) of students selected the same assignment. However, as noted previously, some students often chose the same assignment across modules. This may be the case, as previous studies have identified a relationship between students’ approach to learning and their preference for differing assessments (Gijbels & Dochy, 2006 ). It is also likely that the selection of a particular assignment is correlated with the response effort associated with each assignment format, a hypothesis partially supported by Jopp and Cohen ( 2020 ).

Related to response effort, previous studies have noted that a limitation of providing the choice of assignments to students is that it results in the instructor spending more time creating and grading assignments (Arendt et al., 2016 ; Hanewicz et al., 2017 ). The current study avoided this issue by providing students with fewer choices of assignments, an unlimited number of attempts to complete each assignment, and designating grades as either complete or incomplete.

Given the shortage of research evaluating effective instructional practices for online learning environments, the increase in online instruction due to the COVID-19 pandemic, and our inconclusive results regarding the use of choice in higher education learning, additional research in this area is needed. Future studies could evaluate the impact of the type of assignment available and student preference for assignments based on grades, as well as choice, in combination with other instructional practices (e.g., differentiated instruction). In this study, the Mastery Paths function allowed for the choice of assignment, but this function may also benefit students in other ways. For example, students could receive choices of different assignments (e.g., short Assignment 1 or short Assignment 2; long Assignment 3 and short Assignment 1) based on their scores on a pretest quiz. Footnote 1 With this modification in the design of a course, differentiated instruction and choice of assignment could be automatically programmed into the course structure, promoting the involvement of LCT (Weimer, 2013 ); however, additional research is needed.

This study is not without limitations. As previously mentioned, data from Cohort 1’s Module 1 were excluded because students completed both assignments due to a procedural error in setting up the module. This issue was resolved but required the addition of a question (i.e., pledge statement); however, this pledge statement was not present in all conditions. Furthermore, for Cohort 2, the no-assignment condition resulted in higher average quiz scores compared to the no-choice condition (e.g., control condition). This may have been the case because Module 3 (a no-choice condition) for Cohort 2 was in March 2020, at the start of the pandemic. Given that the stay-at-home order may have impacted childcare and job security and added additional stressors for the students, the lower quiz score on this module may be a reflection of the added environmental changes and not directly an effect of the no-choice condition. Additionally, in both cohorts, performance on the end-of-module quizzes improved across the 8 weeks, perhaps because students learned what to expect during the quizzes and to identify the most relevant information from lectures, readings, and practice assignments. Future studies may attempt to replicate these procedures, but with the randomization of entire cohorts experiencing only one condition, followed by a comparison of the performance of each cohort across conditions. To address other limitations of the current study, future studies should assess the acceptability of the conditions (i.e., social validity) and evaluate variables (e.g., preference, response effort) that impact the selection of assignment.

A task analysis describing the steps necessary to use the Mastery Path function in Canvas is available under Supplemental materials .

Arendt, A., Trego, T., & Allred, J. (2016). Students reach beyond expectations with cafeteria style grading. Journal of Applied Research in Higher Education, 8 (1), 2–17. https://doi.org/10.1108/jarhe-03-2014-0048 .

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Behavior Analyst Certification Board. (2021). Verified course sequence directory. Association for Behavior Analysis International. Retrieved January 18, 2021 from https://www.abainternational.org/vcs/directory.aspx

Cook, A. (2001). Assessing the use of flexible assessment. Assessment and Evaluation in Higher Education, 26 (6), 539–549. https://doi.org/10.1080/02602930120093878 .

Gijbels, D., & Dochy, F. (2006). Students’ assessment preferences and approaches to learning: Can formative assessment make a difference? Educational Studies, 32 (4), 399–409. https://doi.org/10.1080/03055690600850354 .

Hanewicz, C., Platt, A., & Arendt, A. (2017). Creating a learner-centered teaching environment using student choice in assignments. Distance Education, 38 (3), 273–287. https://doi.org/10.1080/01587919.2017.1369349 .

Jopp, R., & Cohen, J. (2020). Choose your own assessment—Assessment choice for students in online higher education. Teaching in Higher Education . https://doi.org/10.1080/13562517.2020.1742680 . Advance online publication.

Rideout, C. (2017). Students’ choices and achievement in large undergraduate classes using a novel flexible assessment approach. Assessment and Evaluation in Higher Education, 43 (1), 68–78. https://doi.org/10.1080/02602938.2017.1294144 .

Tincani, M. (2004). Improving outcomes for college students with disabilities: Ten strategies for instructors. College Teaching, 52 (4), 128–133. https://doi.org/10.3200/CTCH.52.4.128-133 .

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Hannah MacNaul & Ian Thacker

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Hannah MacNaul, Rachel Garcia & Catia Cividini-Motta

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Research Highlights

• The Canvas Mastery Paths function allows instructors to automate choice of assignments into a course, as well as differentiate instruction across students.

• This study extends our understanding of effective teaching strategies in online instruction because results demonstrated that choice of assignments alone did not significantly improve student learning outcomes.

• In this study, choice of assignment was designed in a manner to allow feasibility of implementation by most instructors.

• This article includes step-by-step instructions for how to use the Canvas Mastery Paths function, provided as online Supplementary Material .

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MacNaul, H., Garcia, R., Cividini-Motta, C. et al. Effect of Assignment Choice on Student Academic Performance in an Online Class. Behav Analysis Practice 14 , 1074–1078 (2021). https://doi.org/10.1007/s40617-021-00566-8

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Accepted : 10 February 2021

Published : 26 February 2021

Issue Date : December 2021

DOI : https://doi.org/10.1007/s40617-021-00566-8

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The Reporter

School Assignment and School Effectiveness

A growing number of U.S. households have the opportunity to send their children to public schools outside of traditional neighborhood boundaries. Over the last decade there has been a proliferation of research on the design of centralized choice systems intended to make it easier for children to exercise choice. Millions of students have been assigned to schools using mechanisms either directly or indirectly inspired by academic work.

In recent research with several co-authors, I explore the equity, efficiency, and incentive properties of these choice systems. Aside from these properties, centralized assignment generates valuable data and quasi-experimental variation that can be used for evaluation of various educational practices and policies. I have worked with several researchers to exploit this variation to study productivity differences between schools and school models.

Immediate Acceptance

One of the most common school assignment systems is based on the concept of immediate acceptance : when applicants apply to a school, they are - offered a seat immediately if they qualify. A mechanism based on this principle was in place in Boston until 2005, and hence it is commonly known as the Boston mechanism. 1 A large number of Local Education Authorities in England also employed this mechanism, - called First Preference First.

One issue with this mechanism is that applicants do not have the incentive to rank their desired schools truthfully. That is, ranking a competitive school first may harm a student's chances at lower-ranked schools, creating strategic pressure on the applicant. Should an applicant take a risk at the school she really wants, or instead rank a safe choice first? In work with Tayfun Sönmez, I show that if families do not understand these incentives and rank their choices truthfully, then sophisticated families who understand the rules of the game benefit at the expense of the unsophisticated. 2

The poor incentive properties of immediate acceptance systems led authorities in Chicago to abandon their allocation scheme for the city's elite selective high schools in 2009. Officials in the Chicago Public Schools (CPS) observed that students with higher test scores were denied admission to their second-choice school, even though they had higher scores than students who ranked the school first. After eliciting preferences from more than 14,000 participants, CPS announced a new mechanism and asked participants to re-rank their choices. The new mechanism is a serial dictatorship where the highest-scoring student is assigned to her top choice, the next highest scoring student is assigned to her top choice among remaining schools, and so on. What is particularly surprising about this switch is that the new mechanism also did not have straightforward incentives because it limited the number of choices students could rank. Students could only rank four out of nine possible choices, necessitating strategic calculations on which choices to list and which ones to drop. In the subsequent year, they switched to a system with the same underlying algorithm, but allowed students to rank six schools.

A few years earlier, by an Act of Parliament, authorities in England outlawed First Preference First arrangements citing concerns - that the procedure is unfair to unsophisticated participants. Following this legal ruling, many districts adopted variants of the deferred acceptance algorithm, known in England as Equal Preferences. 3 Using this procedure, first formally studied by David Gale and Lloyd Shapley in 1962, applicants start by applying to their - first choice. Schools tentatively accept their preferred applicants up to capacity and reject the rest. Any rejected student applies to his next most preferred choice, and schools update their set of provisional acceptances by comparing these new proposals to students tentatively held over from the previous round. The algorithm terminates when there are no new proposals from rejected students.

The key idea is that assignments are deferred until there are no new proposals, and only then are they finalized. Unlike the First Preference First system, a student ranking a school second can displace one who ranks it first, if the school prefers that student. The reason it is called Equal Preferences is that when schools receive proposals, they do not discriminate among applicants based on where they were ranked on the applicant's preference form. As in the Chicago case, the Local Education Authorities that adopted Equal Preferences often limited the number of choices students could rank. Table 1 describes some of these transitions. 4

Table 1: School Admission Reforms

Note: Boston refers to the Boston mechanism, FPF refers to First Preference First mechanisms, GS refers to the student-proposing deferred acceptance algorithm of Gale and Shapley, and SD refers to a serial dictatorship.

Sönmez and I develop a way to rank systems based on their propensity toward manipulation. 5 Our approach makes it possible to evaluate whether the new systems are less manipulable than their predecessors. While our criterion is non-consequentialist, it allows for relative comparisons of two systems without ideal incentive properties. As shown in Table 1 it also has important positive content where, with the exception of Seattle in 2009, every example involves the adoption of a less manipulable system according to our measure.

Design of School Lotteries

An important issue in student assignment systems involves resolving situations where two applicants have identical claims for school seats, but there is only one seat left. This can happen, for instance, when two students obtain the same priority at a school because they reside in the school's walk zone, and there are more walk-zone applicants than seats. One might suspect that using separate lotteries at each school would be more fair than a single lottery because under a single lottery, if an applicant has a better lottery number than another applicant, that remains true at each school. However, together with Atila Abdulkadiroğlu and Alvin Roth, I show that a single lottery draw across all schools has better properties than school-specific lottery draws when using deferred acceptance. 6 In the case of New York City where there are 90,000 applicants each year, more than 2,000 additional applicants obtain their first choice with a single lottery draw compared to school-specific draws. 7

Another popular mechanism is based on Gale's top trading cycles (TTC) algorithm. Roughly speaking, this procedure endows students with schools and allows them to trade with one another in an ordered market where trades among top choices occur before trades among lower choices. Suppose Ann wants school 1 as her top choice but has the highest priority at school 2, while Bob wants school 2 as his top choice but has the highest priority at school 1. In the TTC algorithm, Ann and Bob would trade their assignments. In 2012, the OneApp assignment system used in the Recovery School District in New Orleans employed a mechanism based on TTC. 8 In general, there is no preferred way to conduct lotteries for TTC. Together with Jay Sethuraman, I show that in the special case where schools do not have priorities, the allocations produced with a single lottery draw and with school-specific draws are identical. 9

Boston's Choice Plan

Much of the initial work on student assignment was motivated by Boston's iconic school choice system, and it continues to inspire new scientific developments. In Boston and elsewhere, students wish to attend schools close to their home, especially at elementary school entry points. Districts recognize this by prioritizing applicants in the school's walk zone, a geographic area surrounding the school. On the other hand, such policies can increase segregation across schools as students who live near highly desired schools fill up the seats and prevent those from outside the neighborhood from having an opportunity to attend.

To ensure that out-of-neighborhood applicants - have an opportunity to attend a particular school, many choice systems follow Boston's in having a slot-specific priority structure. In Boston, for half of the school seats, applicants with walk-zone priority are ordered ahead of those who do not have walk-zone priority. For the other half, students from the walk zone are treated in the same way as students from outside the zone. This 50-50 split represents a compromise between those in favor of neighborhood schools and those favoring more choice.

When a student is eligible to attend a school both because of walk-zone priority and because of the district-wide assignment rule, the assignment mechanism must deal with another type of indifference. Since students care only about their school assignment, they are indifferent about whether they consume a walk-zone or a non-walk-zone slot. The mechanism's precedence order specifies the order in which slots are depleted. Together with Umut Dur, Scott Kominers, and Sönmez, I - show that student precedence has dramatic consequences for achieving distributional objectives. 10 In Boston, for instance, the precedence rule entirely undermined the intended effect of the 50-50 policy and the outcome was nearly identical to that without walk-zone priority at all. The reason is that applicants first depleted walk-zone slots before non-walk-zone slots. A walk-zone applicant who did not obtain a walk-zone slot competes with the general pool of applicants for non-walk-zone slots, but only after this applicant has been rejected from the walk-zone pool. This rejection induces a form of adverse selection - the applicant is rejected so he must have an unusually bad lottery number - that renders rejected walk-zone applicants not competitive for non-walk-zone slots. As a result, almost no students from the walk zone are assigned to the non-walk-zone slots, undermining the 50-50 compromise.

We develop a framework to study these features of slot-specific priorities and identify counterfactual policies that more faithfully implement policy goals. As a result of our work, Boston - substantially changed its walk-zone policy in 2014.

Boston has also completely redesigned how it determines the set of options students are allowed to rank on their choice menu. Until 2014, residents were restricted to applying to schools in one of three zones of the city and a handful of citywide schools. In 2014, the city adopted a zone-free plan where choice menus are customized based on an applicant's address. The choice menus are designed to ensure that each student is able to apply to - enough of the closest highly rated schools. Peng Shi and I use historical choices expressed in Boston to estimate models of school demand. We use these models to extrapolate the choices applicants would make under these new choice menus. Our results were discussed by school officials and played a significant role in the adoption of the new plan. We plan to update these predictions in a two-part project that will evaluate the performance of structural models of demand forecasting. Because our predictions were made in advance of the policy change, there is no scope for post-analysis bias. 11 We intend to revisit our predictions after applicants have expressed new choices in the spring of 2014, and to use the new data to assess the strengths and weaknesses of counterfactual prediction using discrete choice models of school demand.

Measurement of School Effects

Much of the excitement about school assignment mechanisms comes from the potential to engineer practical solutions that might improve welfare. In my view, an equally important role of common enrollment systems is in producing valuable data that can be used to evaluate the impact of various educational initiatives.

A longstanding question in education has been about the effects of attending charter schools, which are publicly funded schools with enhanced autonomy. When a charter school is over-subscribed, in many jurisdictions students are admitted via lottery. Records on schools' admissions in decentralized and uncoordinated systems tend to be poorly kept and infrequently audited. Together with several co-authors, I collect admissions records from Boston-area charter schools and study the effects of attending an over-subscribed charter school on short-run measures of student achievement. We find large and significant test score gains for charter lottery winners in middle and high school. 12 In subsequent work, I find that charter lottery winners at Boston high schools increase SAT and AP scores, along with evidence of a substantial shift from two- to four-year colleges. 13 In contrast, in work with Joshua Angrist and Christopher Walters, I find more mixed evidence on the performance of charter schools outside of urban areas of Massachusetts. 14

Charters are not assigned centrally in Boston, though they are now beginning to be assigned together with traditional district schools in unified enrollment systems in cities like Denver, Newark, and New Orleans. Alternative schools known as exam schools, which group together the highest-achieving students in the district, are centrally assigned in many cities based on admissions test scores. Together with Abdulkadiroğlu and Angrist, I exploit admissions discontinuities to measure the value of attending schools with high-achieving peers. On a wide range of academic outcomes, we find that marginal applicants who are accepted at exam schools do not score higher on subsequent performance metrics, such as standardized tests, than their near-peers who did not matriculate at exam schools. 15

Another school model I have investigated using lottery-based variation in a centralized match is the small high school. Together with Abdulkadiroğlu and Weiwei Hu, we exploit variation in New York City's high school match to study the effects of attending an over-subscribed small high school, which typically has fewer than 500 students across grades 9 to 12. Unlike charter schools, these schools are run with teachers who are part of the city's collective bargaining agreement. Students are much more disadvantaged than typical New York City high school students. Our results offer some of the first evidence that traditional district schools can produce achievement gains comparable to high-achieving charter schools. 16 Based on surveys, many small high schools have similar characteristics to high-achieving charter schools including high expectations and data-driven instruction. These results highlight the potential for within-district reform strategies to substantially improve student achievement.

1. A. Abdulkadiroğlu and T. Sönmez,-"School Choice: A Mechanism Design Approach," American Economic Review , 93 (2003), pp. 729-47.

2. P. A. Pathak and T. Sönmez,-"Leveling the Playing Field: Sincere and Sophisticated Players in the Boston Mechanism," American Economic Review, 98 (2008), pp. 1636-52.

3. D. Gale and L. Shapley,-"College Admissions and the Stability of Marriage," American Mathematical Monthly , 69 (1962), pp. 9‒15.

4. Table 1 is reproduced from P. A. Pathak and T. Sönmez, "School Admissions Reform in Chicago and England: Comparing Mechanisms by their Vulnerability to Manipulation," NBER Working Paper No. 16783 , February 2011, and American Economic Review , 103 (2013), pp. 80-106. This paper provides further description of the mechanisms referenced in the table.

5. Pathak and Sönmez, 2013, op. cit.

6. A. Abdulkadiroğlu, P. A. Pathak, and A. E. Roth, "Strategy-proofness versus Efficiency in Matching with Indifferences: Redesigning the New York City High School Match," NBER Working Paper No. 14864 , April 2009, and American Economic Review , 99 (2009), pp. 1954-78.

7. See Table 1 in Abdulkadiroğlu, Pathak, and Roth, 2009, op. cit.

8. A. Vanacore, "Centralized Enrollment in Recovery School District Gets Tryout," New Orleans Times-Picayune , April 16, 2012.

9. P. A. Pathak and J. Sethuraman, "Lotteries in Student Assignment: An Equivalence Result," NBER Working Paper No. 16140 , June 2010, and Theoretical Economics , 6 (2011), pp. 1-17.

10. U. M. Dur, S. D. Kominers, P. A. Pathak, and T. Sönmez, "The Demise of Walk Zones in Boston: Priorities vs. Precedence in School Choice," NBER Working Paper No. 18981 , April 2013.

11. P. A. Pathak and P. Shi, "Demand Modeling, Forecasting, and Counterfactuals, Part I," NBER Working Paper No. 19859 , January 2014.

12. A. Abdulkadiroğlu, J.D. Angrist, S.M. Dynarski, T. J. Kane, and P. Pathak, "Accountability and Flexibility in Public Schools: Evidence from Boston's Charters and Pilots," NBER Working Paper No. 15549 , November 2009, and Quarterly Journal of Economics , 126 (2009), pp. 699-748.

13. J. D. Angrist, S. R. Cohodes, S. M. Dynarski, P. A. Pathak, and C. R. Walters, "Stand and Deliver: Effects of Boston's Charter High Schools on College Preparation, Entry, and Choice," NBER Working Paper No. 19275 , July 2013.

14. J. D. Angrist, P. A. Pathak, and C. R. Walters, "Explaining Charter School Effectiveness," NBER Working Paper No. 17332 , August 2011, and American Economic Journal: Applied Economics , 5 (2013), pp. 1-27.

15. A. Abdulkadiroğlu, J. D. Angrist, and P. A. Pathak, "The Elite Illusion: Achievement Effects at Boston and New York Exam Schools," NBER Working Paper No. 17264 , July 2011, and Econometrica , 82 (2014), pp. 137-96.

16. A. Abdulkadiroğlu, W. Hu, and P. A. Pathak,-"Small High Schools and Student Achievement: Lottery-Based Evidence from New York City," NBER Working Paper No. 19576 , October 2013.

Researchers

Students' Achievement and Homework Assignment Strategies

Rubén fernández-alonso.

1 Department of Education Sciences, University of Oviedo, Oviedo, Spain

2 Department of Education, Principality of Asturias Government, Oviedo, Spain

Marcos Álvarez-Díaz

Javier suárez-Álvarez.

3 Department of Psychology, University of Oviedo, Oviedo, Spain

José Muñiz

The optimum time students should spend on homework has been widely researched although the results are far from unanimous. The main objective of this research is to analyze how homework assignment strategies in schools affect students' academic performance and the differences in students' time spent on homework. Participants were a representative sample of Spanish adolescents ( N = 26,543) with a mean age of 14.4 (±0.75), 49.7% girls. A test battery was used to measure academic performance in four subjects: Spanish, Mathematics, Science, and Citizenship. A questionnaire allowed the measurement of the indicators used for the description of homework and control variables. Two three-level hierarchical-linear models (student, school, autonomous community) were produced for each subject being evaluated. The relationship between academic results and homework time is negative at the individual level but positive at school level. An increase in the amount of homework a school assigns is associated with an increase in the differences in student time spent on homework. An optimum amount of homework is proposed which schools should assign to maximize gains in achievement for students overall.

The role of homework in academic achievement is an age-old debate (Walberg et al., 1985 ) that has swung between times when it was thought to be a tool for improving a country's competitiveness and times when it was almost outlawed. So Cooper ( 2001 ) talks about the battle over homework and the debates and rows continue (Walberg et al., 1985 , 1986 ; Barber, 1986 ). It is considered a complicated subject (Corno, 1996 ), mysterious (Trautwein and Köller, 2003 ), a chameleon (Trautwein et al., 2009b ), or Janus-faced (Flunger et al., 2015 ). One must agree with Cooper et al. ( 2006 ) that homework is a practice full of contradictions, where positive and negative effects coincide. As such, depending on our preferences, it is possible to find data which support the argument that homework benefits all students (Cooper, 1989 ), or that it does not matter and should be abolished (Barber, 1986 ). Equally, one might argue a compensatory effect as it favors students with more difficulties (Epstein and Van Voorhis, 2001 ), or on the contrary, that it is a source of inequality as it specifically benefits those better placed on the social ladder (Rømming, 2011 ). Furthermore, this issue has jumped over the school wall and entered the home, contributing to the polemic by becoming a common topic about which it is possible to have an opinion without being well informed, something that Goldstein ( 1960 ) warned of decades ago after reviewing almost 300 pieces of writing on the topic in Education Index and finding that only 6% were empirical studies.

The relationship between homework time and educational outcomes has traditionally been the most researched aspect (Cooper, 1989 ; Cooper et al., 2006 ; Fan et al., 2017 ), although conclusions have evolved over time. The first experimental studies (Paschal et al., 1984 ) worked from the hypothesis that time spent on homework was a reflection of an individual student's commitment and diligence and as such the relationship between time spent on homework and achievement should be positive. This was roughly the idea at the end of the twentieth century, when more positive effects had been found than negative (Cooper, 1989 ), although it was also known that the relationship was not strictly linear (Cooper and Valentine, 2001 ), and that its strength depended on the student's age- stronger in post-compulsory secondary education than in compulsory education and almost zero in primary education (Cooper et al., 2012 ). With the turn of the century, hierarchical-linear models ran counter to this idea by showing that homework was a multilevel situation and the effect of homework on outcomes depended on classroom factors (e.g., frequency or amount of assigned homework) more than on an individual's attitude (Trautwein and Köller, 2003 ). Research with a multilevel approach indicated that individual variations in time spent had little effect on academic results (Farrow et al., 1999 ; De Jong et al., 2000 ; Dettmers et al., 2010 ; Murillo and Martínez-Garrido, 2013 ; Fernández-Alonso et al., 2014 ; Núñez et al., 2014 ; Servicio de Evaluación Educativa del Principado de Asturias, 2016 ) and that when statistically significant results were found, the effect was negative (Trautwein, 2007 ; Trautwein et al., 2009b ; Lubbers et al., 2010 ; Chang et al., 2014 ). The reasons for this null or negative relationship lie in the fact that those variables which are positively associated with homework time are antagonistic when predicting academic performance. For example, some students may not need to spend much time on homework because they learn quickly and have good cognitive skills and previous knowledge (Trautwein, 2007 ; Dettmers et al., 2010 ), or maybe because they are not very persistent in their work and do not finish homework tasks (Flunger et al., 2015 ). Similarly, students may spend more time on homework because they have difficulties learning and concentrating, low expectations and motivation or because they need more direct help (Trautwein et al., 2006 ), or maybe because they put in a lot of effort and take a lot of care with their work (Flunger et al., 2015 ). Something similar happens with sociological variables such as gender: Girls spend more time on homework (Gershenson and Holt, 2015 ) but, compared to boys, in standardized tests they have better results in reading and worse results in Science and Mathematics (OECD, 2013a ).

On the other hand, thanks to multilevel studies, systematic effects on performance have been found when homework time is considered at the class or school level. De Jong et al. ( 2000 ) found that the number of assigned homework tasks in a year was positively and significantly related to results in mathematics. Equally, the volume or amount of homework (mean homework time for the group) and the frequency of homework assignment have positive effects on achievement. The data suggests that when frequency and volume are considered together, the former has more impact on results than the latter (Trautwein et al., 2002 ; Trautwein, 2007 ). In fact, it has been estimated that in classrooms where homework is always assigned there are gains in mathematics and science of 20% of a standard deviation over those classrooms which sometimes assign homework (Fernández-Alonso et al., 2015 ). Significant results have also been found in research which considered only homework volume at the classroom or school level. Dettmers et al. ( 2009 ) concluded that the school-level effect of homework is positive in the majority of participating countries in PISA 2003, and the OECD ( 2013b ), with data from PISA 2012, confirms that schools in which students have more weekly homework demonstrate better results once certain school and student-background variables are discounted. To put it briefly, homework has a multilevel nature (Trautwein and Köller, 2003 ) in which the variables have different significance and effects according to the level of analysis, in this case a positive effect at class level, and a negative or null effect in most cases at the level of the individual. Furthermore, the fact that the clearest effects are seen at the classroom and school level highlights the role of homework policy in schools and teaching, over and above the time individual students spend on homework.

From this complex context, this current study aims to explore the relationships between the strategies schools use to assign homework and the consequences that has on students' academic performance and on the students' own homework strategies. There are two specific objectives, firstly, to systematically analyze the differential effect of time spent on homework on educational performance, both at school and individual level. We hypothesize a positive effect for homework time at school level, and a negative effect at the individual level. Secondly, the influence of homework quantity assigned by schools on the distribution of time spent by students on homework will be investigated. This will test the previously unexplored hypothesis that an increase in the amount of homework assigned by each school will create an increase in differences, both in time spent on homework by the students, and in academic results. Confirming this hypothesis would mean that an excessive amount of homework assigned by schools would penalize those students who for various reasons (pace of work, gaps in learning, difficulties concentrating, overexertion) need to spend more time completing their homework than their peers. In order to resolve this apparent paradox we will calculate the optimum volume of homework that schools should assign in order to benefit the largest number of students without contributing to an increase in differences, that is, without harming educational equity.

Participants

The population was defined as those students in year 8 of compulsory education in the academic year 2009/10 in Spain. In order to provide a representative sample, a stratified random sampling was carried out from the 19 autonomous regions in Spain. The sample was selected from each stratum according to a two-stage cluster design (OECD, 2009 , 2011 , 2014a ; Ministerio de Educación, 2011 ). In the first stage, the primary units of the sample were the schools, which were selected with a probability proportional to the number of students in the 8th grade. The more 8th grade students in a given school, the higher the likelihood of the school being selected. In the second stage, 35 students were selected from each school through simple, systematic sampling. A detailed, step-by-step description of the sampling procedure may be found in OECD ( 2011 ). The subsequent sample numbered 29,153 students from 933 schools. Some students were excluded due to lack of information (absences on the test day), or for having special educational needs. The baseline sample was finally made up of 26,543 students. The mean student age was 14.4 with a standard deviation of 0.75, rank of age from 13 to 16. Some 66.2% attended a state school; 49.7% were girls; 87.8% were Spanish nationals; 73.5% were in the school year appropriate to their age, the remaining 26.5% were at least 1 year behind in terms of their age.

Test application, marking, and data recording were contracted out via public tendering, and were carried out by qualified personnel unconnected to the schools. The evaluation, was performed on two consecutive days, each day having two 50 min sessions separated by a break. At the end of the second day the students completed a context questionnaire which included questions related to homework. The evaluation was carried out in compliance with current ethical standards in Spain. Families of the students selected to participate in the evaluation were informed about the study by the school administrations, and were able to choose whether those students would participate in the study or not.

Instruments

Tests of academic performance.

The performance test battery consisted of 342 items evaluating four subjects: Spanish (106 items), mathematics (73 items), science (78), and citizenship (85). The items, completed on paper, were in various formats and were subject to binary scoring, except 21 items which were coded on a polytomous scale, between 0 and 2 points (Ministerio de Educación, 2011 ). As a single student is not capable of answering the complete item pool in the time given, the items were distributed across various booklets following a matrix design (Fernández-Alonso and Muñiz, 2011 ). The mean Cronbach α for the booklets ranged from 0.72 (mathematics) to 0.89 (Spanish). Student scores were calculated adjusting the bank of items to Rasch's IRT model using the ConQuest 2.0 program (Wu et al., 2007 ) and were expressed in a scale with mean and standard deviation of 500 and 100 points respectively. The student's scores were divided into five categories, estimated using the plausible values method. In large scale assessments this method is better at recovering the true population parameters (e.g., mean, standard deviation) than estimates of scores using methods of maximum likelihood or expected a-posteriori estimations (Mislevy et al., 1992 ; OECD, 2009 ; von Davier et al., 2009 ).

Homework variables

A questionnaire was made up of a mix of items which allowed the calculation of the indicators used for the description of homework variables. Daily minutes spent on homework was calculated from a multiple choice question with the following options: (a) Generally I don't have homework; (b) 1 h or less; (c) Between 1 and 2 h; (d) Between 2 and 3 h; (e) More than 3 h. The options were recoded as follows: (a) = 0 min.; (b) = 45 min.; (c) = 90 min.; (d) = 150 min.; (e) = 210 min. According to Trautwein and Köller ( 2003 ) the average homework time of the students in a school could be regarded as a good proxy for the amount of homework assigned by the teacher. So the mean of this variable for each school was used as an estimator of Amount or volume of homework assigned .

Control variables

Four variables were included to describe sociological factors about the students, three were binary: Gender (1 = female ); Nationality (1 = Spanish; 0 = other ); School type (1 = state school; 0 = private ). The fourth variable was Socioeconomic and cultural index (SECI), which is constructed with information about family qualifications and professions, along with the availability of various material and cultural resources at home. It is expressed in standardized points, N(0,1) . Three variables were used to gather educational history: Appropriate School Year (1 = being in the school year appropriate to their age ; 0 = repeated a school year) . The other two adjustment variables were Academic Expectations and Motivation which were included for two reasons: they are both closely connected to academic achievement (Suárez-Álvarez et al., 2014 ). Their position as adjustment factors is justified because, in an ex-post facto descriptive design such as this, both expectations and motivation may be thought of as background variables that the student brings with them on the day of the test. Academic expectations for finishing education was measured with a multiple-choice item where the score corresponds to the years spent in education in order to reach that level of qualification: compulsory secondary education (10 points); further secondary education (12 points); non-university higher education (14 points); University qualification (16 points). Motivation was constructed from the answers to six four-point Likert items, where 1 means strongly disagree with the sentence and 4 means strongly agree. Students scoring highly in this variable are agreeing with statements such as “at school I learn useful and interesting things.” A Confirmatory Factor Analysis was performed using a Maximum Likelihood robust estimation method (MLMV) and the items fit an essentially unidimensional scale: CFI = 0.954; TLI = 0.915; SRMR = 0.037; RMSEA = 0.087 (90% CI = 0.084–0.091).

As this was an official evaluation, the tests used were created by experts in the various fields, contracted by the Spanish Ministry of Education in collaboration with the regional education authorities.

Data analyses

Firstly the descriptive statistics and Pearson correlations between the variables were calculated. Then, using the HLM 6.03 program (Raudenbush et al., 2004 ), two three-level hierarchical-linear models (student, school, autonomous community) were produced for each subject being evaluated: a null model (without predictor variables) and a random intercept model in which adjustment variables and homework variables were introduced at the same time. Given that HLM does not return standardized coefficients, all of the variables were standardized around the general mean, which allows the interpretation of the results as classical standardized regression analysis coefficients. Levels 2 and 3 variables were constructed from means of standardized level 1 variables and were not re-standardized. Level 1 variables were introduced without centering except for four cases: study time, motivation, expectation, and socioeconomic and cultural level which were centered on the school mean to control composition effects (Xu and Wu, 2013 ) and estimate the effect of differences in homework time among the students within the same school. The range of missing variable cases was very small, between 1 and 3%. Recovery was carried out using the procedure described in Fernández-Alonso et al. ( 2012 ).

The results are presented in two ways: the tables show standardized coefficients while in the figures the data are presented in a real scale, taking advantage of the fact that a scale with a 100 point standard deviation allows the expression of the effect of the variables and the differences between groups as percentage increases in standardized points.

Table Table1 1 shows the descriptive statistics and the matrix of correlations between the study variables. As can be seen in the table, the relationship between the variables turned out to be in the expected direction, with the closest correlations between the different academic performance scores and socioeconomic level, appropriate school year, and student expectations. The nationality variable gave the highest asymmetry and kurtosis, which was to be expected as the majority of the sample are Spanish.

Descriptive statistics and Pearson correlation matrix between the variables .

Table Table2 2 shows the distribution of variance in the null model. In the four subjects taken together, 85% of the variance was found at the student level, 10% was variance between schools, and 5% variance between regions. Although the 10% of variance between schools could seem modest, underlying that there were large differences. For example, in Spanish the 95% plausible value range for the school means ranged between 577 and 439 points, practically 1.5 standard deviations, which shows that schools have a significant impact on student results.

Distribution of the variance in the null model .

Table Table3 3 gives the standardized coefficients of the independent variables of the four multilevel models, as well as the percentage of variance explained by each level.

Multilevel models for prediction of achievement in four subjects .

β, Standardized weight; SE, Standard Error; SECI, Socioeconomic and cultural index; AC, Autonomous Communities .

The results indicated that the adjustment variables behaved satisfactorily, with enough control to analyze the net effects of the homework variables. This was backed up by two results, firstly, the two variables with highest standardized coefficients were those related to educational history: academic expectations at the time of the test, and being in the school year corresponding to age. Motivation demonstrated a smaller effect but one which was significant in all cases. Secondly, the adjustment variables explained the majority of the variance in the results. The percentages of total explained variance in Table Table2 2 were calculated with all variables. However, if the strategy had been to introduce the adjustment variables first and then add in the homework variables, the explanatory gain in the second model would have been about 2% in each subject.

The amount of homework turned out to be positively and significantly associated with the results in the four subjects. In a 100 point scale of standard deviation, controlling for other variables, it was estimated that for each 10 min added to the daily volume of homework, schools would achieve between 4.1 and 4.8 points more in each subject, with the exception of mathematics where the increase would be around 2.5 points. In other words, an increase of between 15 and 29 points in the school mean is predicted for each additional hour of homework volume of the school as a whole. This school level gain, however, would only occur if the students spent exactly the same time on homework as their school mean. As the regression coefficient of student homework time is negative and the variable is centered on the level of the school, the model predicts deterioration in results for those students who spend more time than their class mean on homework, and an improvement for those who finish their homework more quickly than the mean of their classmates.

Furthermore, the results demonstrated a positive association between the amount of homework assigned in a school and the differences in time needed by the students to complete their homework. Figure Figure1 1 shows the relationship between volume of homework (expressed as mean daily minutes of homework by school) and the differences in time spent by students (expressed as the standard deviation from the mean school daily minutes). The correlation between the variables was 0.69 and the regression gradient indicates that schools which assigned 60 min of homework per day had a standard deviation in time spent by students on homework of approximately 25 min, whereas in those schools assigning 120 min of homework, the standard deviation was twice as long, and was over 50 min. So schools which assigned more homework also tended to demonstrate greater differences in the time students need to spend on that homework.

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Relationship between school homework volume and differences in time needed by students to complete homework .

Figure Figure2 2 shows the effect on results in mathematics of the combination of homework time, homework amount, and the variance of homework time associated with the amount of homework assigned in two types of schools: in type 1 schools the amount of homework assigned is 1 h, and in type 2 schools the amount of homework 2 h. The result in mathematics was used as a dependent variable because, as previously noted, it was the subject where the effect was smallest and as such is the most conservative prediction. With other subjects the results might be even clearer.

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Prediction of results for quick and slow students according to school homework size .

Looking at the first standard deviation of student homework time shown in the first graph, it was estimated that in type 1 schools, which assign 1 h of daily homework, a quick student (one who finishes their homework before 85% of their classmates) would spend a little over half an hour (35 min), whereas the slower student, who spends more time than 85% of classmates, would need almost an hour and a half of work each day (85 min). In type 2 schools, where the homework amount is 2 h a day, the differences increase from just over an hour (65 min for a quick student) to almost 3 h (175 min for a slow student). Figure Figure2 2 shows how the differences in performance would vary within a school between the more and lesser able students according to amount of homework assigned. In type 1 schools, with 1 h of homework per day, the difference in achievement between quick and slow students would be around 5% of a standard deviation, while in schools assigning 2 h per day the difference would be 12%. On the other hand, the slow student in a type 2 school would score 6 points more than the quick student in a type 1 school. However, to achieve this, the slow student in a type 2 school would need to spend five times as much time on homework in a week (20.4 weekly hours rather than 4.1). It seems like a lot of work for such a small gain.

Discussion and conclusions

The data in this study reaffirm the multilevel nature of homework (Trautwein and Köller, 2003 ) and support this study's first hypothesis: the amount of homework (mean daily minutes the student spends on homework) is positively associated with academic results, whereas the time students spent on homework considered individually is negatively associated with academic results. These findings are in line with previous research, which indicate that school-level variables, such as amount of homework assigned, have more explanatory power than individual variables such as time spent (De Jong et al., 2000 ; Dettmers et al., 2010 ; Scheerens et al., 2013 ; Fernández-Alonso et al., 2015 ). In this case it was found that for each additional hour of homework assigned by a school, a gain of 25% of a standard deviation is expected in all subjects except mathematics, where the gain is around 15%. On the basis of this evidence, common sense would dictate the conclusion that frequent and abundant homework assignment may be one way to improve school efficiency.

However, as noted previously, the relationship between homework and achievement is paradoxical- appearances are deceptive and first conclusions are not always confirmed. Analysis demonstrates another two complementary pieces of data which, read together, raise questions about the previous conclusion. In the first place, time spent on homework at the individual level was found to have a negative effect on achievement, which confirms the findings of other multilevel-approach research (Trautwein, 2007 ; Trautwein et al., 2009b ; Chang et al., 2014 ; Fernández-Alonso et al., 2016 ). Furthermore, it was found that an increase in assigned homework volume is associated with an increase in the differences in time students need to complete it. Taken together, the conclusion is that, schools with more homework tend to exhibit more variation in student achievement. These results seem to confirm our second hypothesis, as a positive covariation was found between the amount of homework in a school (the mean homework time by school) and the increase in differences within the school, both in student homework time and in the academic results themselves. The data seem to be in line with those who argue that homework is a source of inequity because it affects those less academically-advantaged students and students with greater limitations in their home environments (Kohn, 2006 ; Rømming, 2011 ; OECD, 2013b ).

This new data has clear implications for educational action and school homework policies, especially in compulsory education. If quality compulsory education is that which offers the best results for the largest number (Barber and Mourshed, 2007 ; Mourshed et al., 2010 ), then assigning an excessive volume of homework at those school levels could accentuate differences, affecting students who are slower, have more gaps in their knowledge, or are less privileged, and can make them feel overwhelmed by the amount of homework assigned to them (Martinez, 2011 ; OECD, 2014b ; Suárez et al., 2016 ). The data show that in a school with 60 min of assigned homework, a quick student will need just 4 h a week to finish their homework, whereas a slow student will spend 10 h a week, 2.5 times longer, with the additional aggravation of scoring one twentieth of a standard deviation below their quicker classmates. And in a school assigning 120 min of homework per day, a quick student will need 7.5 h per week whereas a slow student will have to triple this time (20 h per week) to achieve a result one eighth worse, that is, more time for a relatively worse result.

It might be argued that the differences are not very large, as between 1 and 2 h of assigned homework, the level of inequality increases 7% on a standardized scale. But this percentage increase has been estimated after statistically, or artificially, accounting for sociological and psychological student factors and other variables at school and region level. The adjustment variables influence both achievement and time spent on homework, so it is likely that in a real classroom situation the differences estimated here might be even larger. This is especially important in comprehensive education systems, like the Spanish (Eurydice, 2015 ), in which the classroom groups are extremely heterogeneous, with a variety of students in the same class in terms of ability, interest, and motivation, in which the aforementioned variables may operate more strongly.

The results of this research must be interpreted bearing in mind a number of limitations. The most significant limitation in the research design is the lack of a measure of previous achievement, whether an ad hoc test (Murillo and Martínez-Garrido, 2013 ) or school grades (Núñez et al., 2014 ), which would allow adjustment of the data. In an attempt to alleviate this, our research has placed special emphasis on the construction of variables which would work to exclude academic history from the model. The use of the repetition of school year variable was unavoidable because Spain has one of the highest levels of repetition in the European Union (Eurydice, 2011 ) and repeating students achieve worse academic results (Ministerio de Educación, 2011 ). Similarly, the expectation and motivation variables were included in the group of adjustment factors assuming that in this research they could be considered background variables. In this way, once the background factors are discounted, the homework variables explain 2% of the total variance, which is similar to estimations from other multilevel studies (De Jong et al., 2000 ; Trautwein, 2007 ; Dettmers et al., 2009 ; Fernández-Alonso et al., 2016 ). On the other hand, the statistical models used to analyze the data are correlational, and as such, one can only speak of an association between variables and not of directionality or causality in the analysis. As Trautwein and Lüdtke ( 2009 ) noted, the word “effect” must be understood as “predictive effect.” In other words, it is possible to say that the amount of homework is connected to performance; however, it is not possible to say in which direction the association runs. Another aspect to be borne in mind is that the homework time measures are generic -not segregated by subject- when it its understood that time spent and homework behavior are not consistent across all subjects (Trautwein et al., 2006 ; Trautwein and Lüdtke, 2007 ). Nonetheless, when the dependent variable is academic results it has been found that the relationship between homework time and achievement is relatively stable across all subjects (Lubbers et al., 2010 ; Chang et al., 2014 ) which leads us to believe that the results given here would have changed very little even if the homework-related variables had been separated by subject.

Future lines of research should be aimed toward the creation of comprehensive models which incorporate a holistic vision of homework. It must be recognized that not all of the time spent on homework by a student is time well spent (Valle et al., 2015 ). In addition, research has demonstrated the importance of other variables related to student behavior such as rate of completion, the homework environment, organization, and task management, autonomy, parenting styles, effort, and the use of study techniques (Zimmerman and Kitsantas, 2005 ; Xu, 2008 , 2013 ; Kitsantas and Zimmerman, 2009 ; Kitsantas et al., 2011 ; Ramdass and Zimmerman, 2011 ; Bembenutty and White, 2013 ; Xu and Wu, 2013 ; Xu et al., 2014 ; Rosário et al., 2015a ; Osorio and González-Cámara, 2016 ; Valle et al., 2016 ), as well as the role of expectation, value given to the task, and personality traits (Lubbers et al., 2010 ; Goetz et al., 2012 ; Pedrosa et al., 2016 ). Along the same lines, research has also indicated other important variables related to teacher homework policies, such as reasons for assignment, control and feedback, assignment characteristics, and the adaptation of tasks to the students' level of learning (Trautwein et al., 2009a ; Dettmers et al., 2010 ; Patall et al., 2010 ; Buijs and Admiraal, 2013 ; Murillo and Martínez-Garrido, 2013 ; Rosário et al., 2015b ). All of these should be considered in a comprehensive model of homework.

In short, the data seem to indicate that in year 8 of compulsory education, 60–70 min of homework a day is a recommendation that, slightly more optimistically than Cooper's ( 2001 ) “10 min rule,” gives a reasonable gain for the whole school, without exaggerating differences or harming students with greater learning difficulties or who work more slowly, and is in line with other available evidence (Fernández-Alonso et al., 2015 ). These results have significant implications when it comes to setting educational policy in schools, sending a clear message to head teachers, teachers and those responsible for education. The results of this research show that assigning large volumes of homework increases inequality between students in pursuit of minimal gains in achievement for those who least need it. Therefore, in terms of school efficiency, and with the aim of improving equity in schools it is recommended that educational policies be established which optimize all students' achievement.

Ethics statement

This study was carried out in accordance with the recommendations of the University of Oviedo with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the University of Oviedo.

Author contributions

RF and JM have designed the research; RF and JS have analyzed the data; MA and JM have interpreted the data; RF, MA, and JS have drafted the paper; JM has revised it critically; all authors have provided final approval of the version to be published and have ensured the accuracy and integrity of the work.

This research was funded by the Ministerio de Economía y Competitividad del Gobierno de España. References: PSI2014-56114-P, BES2012-053488. We would like to express our utmost gratitude to the Ministerio de Educación Cultura y Deporte del Gobierno de España and to the Consejería de Educación y Cultura del Gobierno del Principado de Asturias, without whose collaboration this research would not have been possible.

Conflict of interest statement

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.

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A Guide to Pursuing Research Projects in High School

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Most common high school pursuits and interests can be fit fairly neatly into the academic or extracurricular categories. There are of course required courses that you take, and then there are the activities that you pursue outside of school hours, usually for your own enjoyment. You may play on a sports team, participate in a service project, or pursue visual arts. In most cases, even if your interests are somewhat untraditional, you can somehow package them in a way that neatly qualifies them as an extracurricular activity.

But what if your interests outside of school are more academic in nature? What if you’ve long been fascinated by the potential that carbon sequestration holds to limit the effects of climate change? What if you’re interested in the history of civil disobedience, or the ability of exams to measure actual comprehension? Whatever the case may be, there are some topics of interest that just don’t fit neatly into any extracurricular club or activity.

If you find yourself longing to pursue an interest such as this, you might consider conducting your own research project. While the concept may seem daunting at first, if you break it down into smaller, manageable tasks, you’ll quickly find that you probably already have the skills necessary to get started.

In this post, we will outline the process for conducting a long-term research project independently, including several avenues for pursuing recognition of your work and a step-by-step guide to completing your project. If you’re interested in pursuing an independent research project during high school, keep reading.

Why Pursue an Independent Research Project?

An independent research project is a great way to explore an area of interest that you otherwise would not get to learn about outside of school. By undertaking a research project on your own, not only will you explore a personal area of interest in more depth, but also you will demonstrate your dedication to pursuing knowledge for the sake of learning and your ability to work independently over a prolonged period.

Independent research projects, when conducted well and presented appropriately on a college application, can be a great advantage to you on your college admissions.

How to Choose a Topic for a Research Project

If you’re interested in pursuing a research project, you probably already have a topic in mind. In fact, the desire to conduct a research project usually stems from an existing interest, not just from the idea to conduct research on a vague or undetermined subject matter.

You should aim to narrow your research project to something that has some academic relevance. Perhaps it is related to your existing coursework. Maybe it reflects work you hope to pursue in the future, either academically or professionally. Try to fine-tune your project enough that you can easily explain the driving force behind it and its relevance to your future career path.

While you don’t need to decide on your exact topic or thesis quite yet, you should have a general idea of what your project will entail before moving forward.

Are There Existing Avenues for Undertaking a Research Project At Your School?

While you could certainly conduct your research project completely independently from your school, it is usually easier and more productive to conduct it in a way that is somehow connected to the rest of your schooling.

If the project is STEM-oriented, think about whether it would fit into a science fair or other STEM competition in which your school already competes. Also consider the AP Capstone Program if your school offers it. The second course in this sequence is AP Research , and it requires an in-depth research project as its culminating assessment.

If neither of these formal avenues are available, or neither provides a good fit, look into the possibility of pursuing your project as an independent study. If your school offers independent studies for credit, you can usually get information about them from your adviser. These types of projects usually require an extended application process that must be followed closely if you want to gain approval.

Finally, even if you can’t take advantage of one of the options above, if you have achieved advanced standing or enough credits, your school might still allow you to undertake an extended individual research project through some type of formal arrangement. Talk with a teacher, mentor, or adviser to learn what your options are. Clearly communicate your innate desire to learn more about this specific topic and be prepared to give some background on the issue that you want to research.

Steps for Undertaking the Research Project

1. find a mentor or adviser.

You will need someone to help guide and advise your work, so finding a willing and able mentor should be one of your first steps. This should ideally be a person with existing expertise in the subject area you wish to pursue. In the least, this person should share your interest and passion for the topic.

A teacher at your school who can also serve as an adviser is ideal, and may even be a requirement if you are formally pursuing the project as an independent study for credit. If that is not possible, you can certainly find a mentor somewhere else, even remotely if necessary.

Find out if your subject matter pertains to any local industries or companies, or if there are any scientists or professionals nearby who specialize in it. Consider checking the instructors of local summer programs or judges from past science fairs at your school. Also consider a professional who has written an article that interested you in the field.

Before you approach a mentor to request their help, familiarize yourself with his or her work. Be able to speak articulately about what has drawn you to him or her specifically. Put some thought into informed questions you might ask him or her. Be upfront about your needs if you are going to require any specific guidance or extended time or energy from your mentor. It might be difficult to find someone at first, but keep trying. Finding a mentor for your project is an important step.

2. Set a Timeline and Stick to It

Once you’ve found a mentor, you can get started laying out the timeline for your project. When you do this, list each step of your project as specifically as possible. These will include at a minimum: background research, writing a thesis statement, in depth research phase, outlining your final paper, drafting your paper, editing your paper, and publishing your paper.

You will probably have a completion date in mind, whether it’s required by the school or simply the end of the semester or school year. Work backwards from your completion date to set a realistic timeframe for each of these steps.

It helps to have a calendar displayed prominently with your deadlines listed clearly on it to keep you on track. Also be sure to put your deadlines into your school assignment book or Google calendar so that you can see how they overlap and affect your other commitments.

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3. Conducting Research

After you’ve completed your deadline calendar, you’re ready to get started with the fun stuff: the actual research. There are many sources for finding high quality research materials. You can use your school library, your local library, and sometimes even the library at local colleges or universities. Sometimes the libraries at colleges are open only to registered students and faculty, but if you contact a library official or a member of the department related to your research project, you might be able to gain access for research purposes.

You may also take advantage of online research tools. Google Scholar is a good place to find peer-reviewed, high quality publications. You may also find out if your school has a subscription to any online research databases like Ebsco , or JSTOR . These databases provide digital compilations of hundreds of research journals, both current and archived.

Be careful what you choose to use as sources, though. You need to ensure that every source you rely on is high-quality and fact-based. Many internet resources now are not as accurate as they might appear. Some are outdated and some are just wrong. Remember that just about anyone can publish something online these days, so you can’t rely on information that you find on just any old website. Be particularly wary of pages like Wikipedia that look like fact-based resources but are actually drawn from unfiltered user submissions.

As you research your topic, take careful notes to track your work. Choose a system to organize your notes, such as writing on notecards that can be easily organized, or using different colored pens to color code different subtopics of your research. By carefully organizing your notes, you’ll be better set up to organize your paper.

4. Organize Your Paper

Once you’ve completed the research phase of your project, you’re ready to organize your paper. Go through your notes carefully to see how they support your thesis. If they don’t, be prepared and open to changing your thesis. Always allow the research to guide the direction of your paper, and not vice versa.

Organize your notes into the order that makes most sense in your paper. Use them to guide an outline of your paper. Once they are in order, write out a rough outline of your paper.

Prewriting is an important step to writing your paper. It allows you to go into the drafting phase with as much preparation as possible so that your writing will have a clear direction when you begin.

5. Write Your Paper

After your organization and prewriting, you’re ready to draft your paper. Try to break this phase up into smaller pieces so that you don’t burn out. Your final product will probably be one of the longest papers you’ve ever written, usually ranging from 15-30 pages depending on your subject, so you’ll want to pace yourself.

Break up your writing deadlines into more specific sub-deadlines to help guide your work. Set goals for completing the introduction, various sections of the body, and your conclusion.

6. Edit Your Paper

There will be multiple stages of editing that need to happen. First, you will self-edit your first draft. Then, you will likely turn a draft of your paper in to your mentor for another round of editing. Some students even choose to have a peer or family member edit a draft at some point. After several rounds of editing, you will be prepared to publish your work.

7. Publish Your Work

Publication sounds like a very official completion of your project, but in reality publishing can take many different forms. It’s really just the final draft of your project, however you decide to produce it.

For some students, publication means submitting a draft of your project to an actual journal or formal publication. For others, it means creating a polished draft and a display board that you will present at a school or public event. For still others it might just be a polished, final draft bound and turned into your mentor.

However you decide to publish your work, be mindful that this should be a reflection of an entire semester or year of work, and it should reflect the very height of your learning and abilities. You should be proud of your final product.

If you’re a high school student with in-depth interests in a subject area that doesn’t fit neatly into any of your existing extracurriculars or academic courses, you should consider pursuing a research project to reflect your interest and dedication. Not only will your pursuit allow you to further explore a subject that’s interesting to you, but also it will be a clear example of your independence and commitment on your college applications.

Looking for help navigating the road to college as a high school student? Download our free guide for 9th graders and our free guide for 10th graders . Our guides go in-depth about subjects ranging from academics , choosing courses , standardized tests , extracurricular activities , and much more !

For more information about research and independent projects in high school, check out these posts:

Ultimate Guide to the AP Research Course and Assessment
How to Choose a Project for Your AP Research Course
How to Get a Research Assistant Position in High School
An Introduction to the AP Capstone Diploma
How to Choose a Winning Science Fair Project Idea
How to Plan and Implement an Independent Study in High School

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MOC Student Projects on Country & Cluster Competitiveness

The competitive assessments listed on this page have been prepared by teams of graduate students mostly from Harvard Business School and the Harvard Kennedy School of Government and other universities as part of the requirements for the Microeconomics of Competitiveness. Each study focuses on the competitiveness of a specific cluster in a country or region and includes specific action recommendations.

These studies represent a valuable resource for researchers, government officials, and other leaders. Students have given permission to publish their work here; the copyright for each report is retained by the student authors. References to the reports should include a full list of the authors.

Student Projects by Country

Argentina Soy Cluster (2016)
Armenia IT Services Cluster (2006)
Australia Liquefied Natural Gas (LNG) Cluster (2016)
South Australia Wine Cluster (2010)
Australia Renewable Energy (2008)
Belgium Chocolate Cluster (2016)
Wallonia Aeronautic Cluster (2013)
Belgium Pharmaceuticals (2011)
The Botswana Textiles Cluster (2007)
Brazilian Petrochemical Cluster (2017)
Sao Paulo Plastics (2013)
Leather Footwear in Brazil (2012)
Brazil Aviation (2011)
Bio-ethanol Cluster in Brazil (2009)
Brazil Biotech Cluster: Minas Gerais (2009)
The Poultry Cluster in Brazil (2006)
Bulgaria's Apparel Cluster (2007)
Alberta Energy Cluster (2010)
Ontario Financial Services (2008)
Transportation and Logistics Cluster in Northeast China (2017)
Wind Turbine Cluster in Inner Mongolia (2009)
The Chinese Apparel Cluster in Guangdong (2006)
Bogota Software Cluster (2013)
The Sugar Cane Cluster in Colombia (2007)
Colombia Shrimp Aquaculture (2008)
Costa Rica Data Centers (2016)
Costa Rica Medical Tourism (2016)
Ship & Boatbuilding in Croatia (2009)
The Danish Wind Cluster (2017)
The Danish Design Cluster (2007)

Dominican Republic

The Dominican Republic Tourism Cluster (2012)
Tourism in the Dominican Republic (2007)
The Textile Cluster in Egypt (2012)
The Offshoring Cluster in Egypt (2009)
France's Competitiveness in AI (2017)
Toulouse Aerospace Cluster (2013)
France Wine Cluster (2013)
Baden-Wuerttemberg Automobile Cluster (2015)
Germany Wind Power Cluster (2010)
Germany’s Photovoltaic Cluster (2009)
Hamburg Aviation Cluster (2009)
Biotechnology and Life Sciences in Munich (2007)
Ghana Cocoa Sector (2017)
Greece Shipping Cluster (2010)
The Fresh Produce Cluster in Guatemala (2009)
The Apparel Cluster in Honduras (2007)
Hong Kong Financial Services (2008)
Iceland Financial Services (2008)
The Antiretroviral Drug Cluster in India (2017)
Andhra Pradesh Pharmaceutical Cluster (2013)
Tamil Nadu (India) Automotive Cluster (2012)
Tirupur (India) Knitwear (2011)
India (Maharashtra) Automotive Cluster (2010)
Maharashtra Biopharmaceutical Cluster (2009)
Bangalore Biotechnology (2008)
Gujarat Diamonds (2008)
Bollywood — Maharashtra and India’s Film Cluster (2008)
Karnataka Offshore IT and Business Process Outsourcing Services Cluster (2006)
Bali Tourism Cluster (2013)
Ireland Financial Services Cluster (2017)
Ireland Internet Cluster (2013)
Ireland ICT Cluster (2010)
The Dublin International Financial Services Cluster (2006)
Israel Aerospace Cluster (2015)
Jerusalem Tourism Cluster (2013)
Israeli Biotechnology Cluster (2006)
Italy Tourism (2011)
The Italian Sports Car Cluster (2006)
Japan Automobile Cluster (2016)
Japan Skin Care Cluster (2013)
The Japanese Gaming Cluster (2012)
Japan Flat Panel Displays (2011)
The Video Games Cluster in Japan (2009)
Jordan Tourism Cluster (2009)
Kazakhstan Oil and Gas Cluster (2010)
Kazakhstan Energy Cluster (2007)
Kenya ITC Services Cluster (2016)
Kenya Tourism Cluster (2016)
Kenya Business Process Offshoring (2011)
Kenya Tea (2009)
Kenya Coffee (2008)
Kenya's Cut-Flower Cluster (2007)
Korea Showbiz Cluster (2013)
Korea Shipbuilding Cluster (2010)
Korea Online Game Cluster (2006)
Textile and Apparel Cluster in Kyrgyzstan (2012)
The Macedonian Wine Cluster (2006)
The Shrimp Cluster in Madagascar (2006)
Malaysia Semiconductor Cluster (2015)
Malaysia Palm Oil (2011)
Malaysia Financial Services (2008)
Queretaro Aerospace Cluster (2015)
Mexico Central Region Automotive Cluster (2013)
Mexico Chocolate Cluster (2010)
Electronics Cluster in Guadalajara Mexico (2009)
Baja California Sur Tourism (2008)
Monaco Tourism (2011)
Mongolia Mining Services Cluster (2010)
Morocco Automotive Cluster (2015)
Morocco Aeronautics Cluster (2013)
Morocco Tourism (2008)
Nepal Tourism Cluster (2015)
Nepal Tourism (2011)

Netherlands

Netherlands Medical Devices Cluster (2013)
Netherlands Dairy (2011)

New Zealand

New Zealand's Marine Cluster (2009)
The Nicaraguan Coffee Cluster (2006)
Lagos ICT Services Cluster (2017)
Nollywood — The Nigerian Film Industry (2008)
Nigeria Financial Services (2008)
Norway’s Fish and Fish Products Cluster (2017)
Textiles Cluster in Pakistan (2007)
Lima Financial Services Cluster (2016)
Asparagus Cluster in Peru (2012)
Peru Tourism Cluster (2010)

Philippines

The Philippines Electronics Components Manufacturing (2017)
Medical Tourism in the Philippines (2008)
The Philippines Contact Center Cluster (2007)
The Tourism Cluster in Lisbon (2017)
The Automotive Cluster in Portugal (2007)
Romania Apparel Cluster (2010)
The Moscow Financial Services Cluster (2012)
Moscow Transportation (2006)

Saudi Arabia

Saudi Arabia Chemicals Cluster (2016)
Singapore Higher Education (2016)
Slovakia Automobile Cluster (2016)

South Africa

The Johannesburg Software Cluster (2017)
South Africa Iron Ore Cluster (2013)
South Africa Automotive Cluster (2012)
The South African Wine Cluster (2009)
Textiles & Apparel Cluster in South Africa (2009)
The South African Wine Cluster (2006)
Andalucia (Spain) Tourism (2011)
Apparel Cluster in Galicia Spain (2009)
The Spanish Wind Power Cluster (2007)

Switzerland

Banking in Switzerland (2017)
Switzerland Private Banking Cluster (2010)
Switzerland Watchmaking (2010)
Taiwan: Semiconductor Cluster (2007)
Tanzania Horticulture Cluster (2010)
Tanzania’s Tourism Cluster (2006)
Thailand Automotive (2011)
Thailand Automotive Cluster (2007)
Thailand Medical Tourism Cluster (2006)

Trinidad & Tobago

Tourism in Trinidad and Tobago (2006)
Tourism Cluster in Tunisia (2012)
Tunisian Tourism Cluster (2008)
Turkey Textiles and Apparel Cluster (2012)
Turkey Automotive (2011)
Turkey & The Construction Services Cluster (2007)
Uganda Fishing Cluster (2010)

United Arab Emirates

Dubai Logistics Cluster (2015)
Abu Dhabi (UAE) Petrochemical Cluster (2012)
Dubai (UAE) Tourism (2011)
The Transport and Logistics Cluster in UAE (2007)
Dubai Financial Services Cluster (2006)

United Kingdom

The Future of the UK Midlands Automotive Cluster (2017)
London FinTech Cluster (2016)
IT Hardware Cluster in Cambridge, UK (2012)
UK Competitiveness and the International Financial Services Cluster in London (2007)

United States

Massachusetts Clean Energy Cluster (2017)
Ohio Automotive Cluster (2017)
Chicago Biotech Cluster (2016)
San Diego Craft Beer Cluster (2016)
Kentucky Bourbon Cluster (2015)
New York City Apparel Cluster (2015)
Pennsylvania Natural Gas Cluster (2013)
New York Motion Picture Cluster (2013)
Massachusetts Robotics Cluster (2012)
Miami, Florida Marine Transportation Cluster (2012)
South Carolina Automotive Sector (2012)
Tennessee Music Cluster (2012)
California Solar Energy (2011)
Silicon Valley (California) Internet-Based Services (2011)
Minnesota Medical Devices (2011)
Massachusetts Higher Education and Knowledge Cluster (2010)
The North Carolina Furniture Cluster (2009)
Automotive Cluster in Michigan USA (2009)
Washington D.C. Information Technology and Services Cluster (2008)
The Chicago Processed Food Cluster (2006)
The Los Angeles Motion Picture Industry Cluster (2006)

Student Projects by Cluster

Aerospace vehicles & defense, agricultural products.

Asparagus in Peru (2012)
Textiles and Apparel Cluster in Turkey (2012)
Bulgaria's Apparel Cluster (2007)
South African Automotive Cluster (2012)
South Carolina (USA) Automotive Cluster (2012)

Biopharmaceuticals

Bangalore (India) Biotechnology (2008)

Business Services

Karnataka (India) Offshore IT and Business Process Outsourcing Services Cluster (2006)

Construction Services

Education & knowledge creation.

Massachusetts Higher Education and Knowledge Cluster (2010)

Entertainment

Nollywood The Nigerian Film Industry (2008)

Financial Services

The Moscow (Russia) Financial Services Cluster (2012)
Ontario (Canada) Financial Services (2008)
UK Competitiveness and the International Financial Services Cluster in London (2007)

Fishing & Fishing Products

Health services, hospitality & tourism.

Baja California Sur (Mexico) Tourism (2008)

Information Technology

The Johannesburg Software Cluster (2017)

Jewelry & Precious Metals

Gujarat (India) Diamonds (2008)

Marine Equipment

Medical devices, metal manufacturing, metal mining, oil & gas products & services.

Abu Dhabi (UAE) Petrochemical Cluster (2012)
Norway Oil and Gas Cluster (2012)

Processed Food

Power generation & transmission, transportation & logistics.

The Miami Florida Marine Transportation Cluster (2012)
The Transport and Logistics Cluster in the United Arab Emirates (2007)

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2024 Planting Seeds of Innovation Colloquium

Leveraging generative AI to modernize nursing education

May 13, 2024 Brett Stursa

Martin Michalowski

The proliferation of new generative artificial intelligence (AI) tools can be challenging for nurse educators and clinicians to keep up with, as the potential benefits also come with new challenges.

Associate Professor Martin Michalowski, PhD, FAMIA, examines generative AI in nursing education and provides recommendations for nurse educators to optimize its use in recent publications.

Michalowski’s most recent article, The ChatGPT Effect: Nursing Education and Generative Artificial Intelligence, published in the February issue of the Journal of Nursing Education, examines generative AI in nursing education more broadly and urges nurse educators to harness its potential.

Prompt engineering when using generative AI in nursing education, published in the January issue of Nurse Education in Practice, makes recommendations to integrate prompt engineering — the process of refining questions to get better results — in nursing education.

“Generative AI is one of the key required competencies and it needs to be integrated into the education nurses receive both as concepts to understand and as tools to use,” says Michalowski. “Similar to concepts in machine learning, natural language processing, automated reasoning, and other AI subfields, generative AI is transforming the provision of care. Therefore, it is important that nurses understand how to use it, and how its use impacts health care systems, providers and patients.”

Currently, he says that the most effective uses of generative AI in nursing classrooms is creating mock patient-related data and providing patient scenarios for practice.

“When applying learned theories or tools where patient-related data is needed, generative AI models are very useful for building synthetic data in different formats, like tables, free text notes, etc.,” says Michalowski.

“Additionally, generative AI enables critical thinking through the creation of patient use cases/scenarios. This application is one of the few where hallucinations — presenting output patterns as fact while they are clinically or factually incorrect — is acceptable. Students need to apply what they learned and use critical thinking to identify inaccuracies and contradictions in the use case. The instructor can also tailor the output use cases by providing important context for the learning exercise.”

Michalowski says it’s imperative nurse educators integrate AI competencies into their classrooms to ensure students are well equipped as future clinicians.

“Nurses have an incredible opportunity to lead health care’s transformation of clinical care with AI. They touch all aspects of the care process, understand the clinical problems and interface with patients. They are positioned to be the bridge between AI developers, health care practitioners and stakeholders,” says Michalowski. “However, to fully realize this potential they need basic AI competencies that aren’t currently part of their education.”

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Innovative Statistics Project Ideas for Insightful Analysis

Table of contents

1.1 AP Statistics Topics for Project
1.2 Statistics Project Topics for High School Students
1.3 Statistical Survey Topics
1.4 Statistical Experiment Ideas
1.5 Easy Stats Project Ideas
1.6 Business Ideas for Statistics Project
1.7 Socio-Economic Easy Statistics Project Ideas
1.8 Experiment Ideas for Statistics and Analysis
2 Conclusion: Navigating the World of Data Through Statistics

Diving into the world of data, statistics presents a unique blend of challenges and opportunities to uncover patterns, test hypotheses, and make informed decisions. It is a fascinating field that offers many opportunities for exploration and discovery. This article is designed to inspire students, educators, and statistics enthusiasts with various project ideas. We will cover:

Challenging concepts suitable for advanced placement courses.
Accessible ideas that are engaging and educational for younger students.
Ideas for conducting surveys and analyzing the results.
Topics that explore the application of statistics in business and socio-economic areas.

Each category of topics for the statistics project provides unique insights into the world of statistics, offering opportunities for learning and application. Let’s dive into these ideas and explore the exciting world of statistical analysis.

Top Statistics Project Ideas for High School

Statistics is not only about numbers and data; it’s a unique lens for interpreting the world. Ideal for students, educators, or anyone with a curiosity about statistical analysis, these project ideas offer an interactive, hands-on approach to learning. These projects range from fundamental concepts suitable for beginners to more intricate studies for advanced learners. They are designed to ignite interest in statistics by demonstrating its real-world applications, making it accessible and enjoyable for people of all skill levels.

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AP Statistics Topics for Project

Analyzing Variance in Climate Data Over Decades.
The Correlation Between Economic Indicators and Standard of Living.
Statistical Analysis of Voter Behavior Patterns.
Probability Models in Sports: Predicting Outcomes.
The Effectiveness of Different Teaching Methods: A Statistical Study.
Analysis of Demographic Data in Public Health.
Time Series Analysis of Stock Market Trends.
Investigating the Impact of Social Media on Academic Performance.
Survival Analysis in Clinical Trial Data.
Regression Analysis on Housing Prices and Market Factors.

Statistics Project Topics for High School Students

The Mathematics of Personal Finance: Budgeting and Spending Habits.
Analysis of Class Performance: Test Scores and Study Habits.
A Statistical Comparison of Local Public Transportation Options.
Survey on Dietary Habits and Physical Health Among Teenagers.
Analyzing the Popularity of Various Music Genres in School.
The Impact of Sleep on Academic Performance: A Statistical Approach.
Statistical Study on the Use of Technology in Education.
Comparing Athletic Performance Across Different Sports.
Trends in Social Media Usage Among High School Students.
The Effect of Part-Time Jobs on Student Academic Achievement.

Statistical Survey Topics

Public Opinion on Environmental Conservation Efforts.
Consumer Preferences in the Fast Food Industry.
Attitudes Towards Online Learning vs. Traditional Classroom Learning.
Survey on Workplace Satisfaction and Productivity.
Public Health: Attitudes Towards Vaccination.
Trends in Mobile Phone Usage and Preferences.
Community Response to Local Government Policies.
Consumer Behavior in Online vs. Offline Shopping.
Perceptions of Public Safety and Law Enforcement.
Social Media Influence on Political Opinions.

Statistical Experiment Ideas

The Effect of Light on Plant Growth.
Memory Retention: Visual vs. Auditory Information.
Caffeine Consumption and Cognitive Performance.
The Impact of Exercise on Stress Levels.
Testing the Efficacy of Natural vs. Chemical Fertilizers.
The Influence of Color on Mood and Perception.
Sleep Patterns: Analyzing Factors Affecting Sleep Quality.
The Effectiveness of Different Types of Water Filters.
Analyzing the Impact of Room Temperature on Concentration.
Testing the Strength of Different Brands of Batteries.

Easy Stats Project Ideas

Average Daily Screen Time Among Students.
Analyzing the Most Common Birth Months.
Favorite School Subjects Among Peers.
Average Time Spent on Homework Weekly.
Frequency of Public Transport Usage.
Comparison of Pet Ownership in the Community.
Favorite Types of Movies or TV Shows.
Daily Water Consumption Habits.
Common Breakfast Choices and Their Nutritional Value.
Steps Count: A Week-Long Study.

Business Ideas for Statistics Project

Analyzing Customer Satisfaction in Retail Stores.
Market Analysis of a New Product Launch.
Employee Performance Metrics and Organizational Success.
Sales Data Analysis for E-commerce Websites.
Impact of Advertising on Consumer Buying Behavior.
Analysis of Supply Chain Efficiency.
Customer Loyalty and Retention Strategies.
Trend Analysis in Social Media Marketing.
Financial Risk Assessment in Investment Decisions.
Market Segmentation and Targeting Strategies.

Socio-Economic Easy Statistics Project Ideas

Income Inequality and Its Impact on Education.
The Correlation Between Unemployment Rates and Crime Levels.
Analyzing the Effects of Minimum Wage Changes.
The Relationship Between Public Health Expenditure and Population Health.
Demographic Analysis of Housing Affordability.
The Impact of Immigration on Local Economies.
Analysis of Gender Pay Gap in Different Industries.
Statistical Study of Homelessness Causes and Solutions.
Education Levels and Their Impact on Job Opportunities.
Analyzing Trends in Government Social Spending.

Experiment Ideas for Statistics and Analysis

Multivariate Analysis of Global Climate Change Data.
Time-Series Analysis in Predicting Economic Recessions.
Logistic Regression in Medical Outcome Prediction.
Machine Learning Applications in Statistical Modeling.
Network Analysis in Social Media Data.
Bayesian Analysis of Scientific Research Data.
The Use of Factor Analysis in Psychology Studies.
Spatial Data Analysis in Geographic Information Systems (GIS).
Predictive Analysis in Customer Relationship Management (CRM).
Cluster Analysis in Market Research.

Conclusion: Navigating the World of Data Through Statistics

In this exploration of good statistics project ideas, we’ve ventured through various topics, from the straightforward to the complex, from personal finance to global climate change. These ideas are gateways to understanding the world of data and statistics, and platforms for cultivating critical thinking and analytical skills. Whether you’re a high school student, a college student, or a professional, engaging in these projects can deepen your appreciation of how statistics shapes our understanding of the world around us. These projects encourage exploration, inquiry, and a deeper engagement with the world of numbers, trends, and patterns – the essence of statistics.

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Assignment Russia

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Chapter One

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Becoming a foreign correspondent in the crucible of the cold war.

A personal journey through some of the darkest moments of the cold war and the early days of television news

Marvin Kalb, the award-winning journalist who has written extensively about the world he reported on during his long career, now turns his eye on the young man who became that journalist. Chosen by legendary broadcaster Edward R. Murrow to become one of what came to be known as the Murrow Boys, Kalb in this newest volume of his memoirs takes readers back to his first days as a journalist, and what also were the first days of broadcast news.

Kalb captures the excitement of being present at the creation of a whole new way of bringing news immediately to the public. And what news. Cold War tensions were high between Eisenhower’s America and Khrushchev’s Soviet Union. Kalb is at the center, occupying a unique spot as a student of Russia tasked with explaining Moscow to Washington and the American public. He joins a cast of legendary figures along the way, from Murrow himself to Eric Severeid, Howard K. Smith, Richard Hottelet, Charles Kuralt, and Daniel Schorr among many others. He finds himself assigned as Moscow correspondent of CBS News just as the U2 incident—the downing of a US spy plane over Russian territory—is unfolding.

As readers of his first volume, The Year I Was Peter the Great , will recall, being the right person, in the right place, at the right time found Kalb face to face with Khrushchev. Assignment Russia sees Kalb once again an eyewitness to history—and a writer and analyst who has helped shape the first draft of that history.

Related Books

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September 25, 2018

Assignment Russia Book Events

April 9: Politics & Prose LIVE! Marvin Kalb—Assignment Russia: Becoming a Foreign Corresponding in the Crucible of the Cold War – with Jake Tapper
April 13: National Press Club Virtual Book Event —Marvin Kalb, Assignment Russia
April 15: Brookings Event —Assignment Russia: A conversation on journalism and the Cold War
April 29: Shorenstein Center on Media, Politics, and Public Policy Event —Assignment Russia: Becoming a Foreign Correspondent in the Crucible of the Cold War
May 25: George Washington University —Assignment Russia: Becoming a Foreign Correspondent in the Crucible of the Cold War

Praise for Assignment Russia

“It is impossible to put this engrossing book down—it illuminates so many dark corners of the Cold War. With a master correspondent’s insight, skepticism, sensitivity, and great clarity, Kalb brings vividly to life all the hopes and fears of the most consequential foe this nation has had.” —Ken Burns, filmmaker

“A fascinating memoir of Marvin Kalb’s Cold War adventures as he sought to penetrate the mysteries of Nikita Khrushchev’s Soviet Russia while building his career as one of broadcast journalism’s legends.” —Jack Matlock, U.S. ambassador to Russia (1987–1991)

“Marvin Kalb’s engaging Assignment Russia is like Hamilton’s ‘The Room Where It Happens.’ It is a delightful narrative of Kalb’s personal encounters with some of the most famous characters of the 1950s and 1960s, like CBS’s legendary Edward R. Murrow, who hired Kalb, or Soviet leader Nikita Khrushchev, who nicknamed him ‘Peter the Great.’ It is also an engrossing memoir of a foreign correspondent’s adventures in the enemy camp during the Cold War. I loved it, I learned from it, and, I dare say, had fun reading it.” —Lesley Stahl, co-anchor, CBS’s 60 Minutes

“Marvin Kalb’s great new book Assignment Russia is a rollicking and engaging memoir that takes you to the front lines of the Cold War, to a mic in the early days of broadcast news, and into the mind and career of one of ‘Murrow’s Boys.’ It’s an important book from a legend in journalism, a book you can’t put down.” —Jake Tapper, CNN anchor and chief Washington correspondent

“A nostalgic treat for older readers…a wake-up call for younger ones.” —Edward Kosner, The Wall Street Journal

“Kalb’s fond, generous memoir, which vividly delineates a bygone era of early journalism, will appeal to students of 20th-century American history as well as aspiring broadcast journalists. The author was involved in many significant Cold War moments, and he brings us directly into that world. Hopefully Kalb is back at his desk; readers will be eager for the next volume.” — Kirkus Reviews

“Readers should be forewarned that once they pick up the book, it will be hard to put it down until they reach the end.” —Naseer Ahmad, Pakistan Link

Marvin Kalb is a former senior adviser to the Pulitzer Center on Crisis Reporting, a Harvard Professor emeritus, former network news correspondent at NBC and CBS, senior fellow nonresident at the Brookings Institution, and author of 16 other books, the most recent of which is the first volume of his memoirs, The Year I Was Peter the Great (Brookings).

Media Coverage

Journalist Marvin Kalb on dangers, thrills of reporting from Russia during the Cold War

Why Navalny’s Attempt To Dismantle Putin’s Regime Feels Out Of Reach

An American journalist in Cold War Moscow

The Rise of Marvin Kalb

Veteran journalist Marvin Kalb on covering Cold War Russia

Whine Line, Marvin Kalb

Q&A with Marvin Kalb

M. Kalb, Foreign Affairs Reporter

Wednesday, April 14th: Marvin Kalb

State Circle: April 30, 2021

An earnest young correspondent in Cold War Moscow

‘Assignment Russia’ Review: Murrow’s Man in Moscow

Book Review: Assignment Russia, Marvin Kalb’s Memoir

Assignment Russia: Becoming a Foreign Correspondent in the Crucible of the Cold War

Marvin Kalb in the World

Marvin Kalb at Home and Abroad

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What's the best state for you », putin to push growing moscow-beijing trade in china's northeast.

Putin to Push Growing Moscow-Beijing Trade in China's Northeast

Russian President Vladimir Putin delivers a speech during the 8th Russian-Chinese EXPO and the 4th Russian-Chinese Forum on Interregional Cooperation in Harbin, China, May 17, 2024. Sputnik/Mikhail Metzel/Pool via REUTERS

By Bernard Orr

BEIJING (Reuters) - After sealing pledges of a "new era" of strategic partnership with China's Xi Jinping, Russian President Vladimir Putin on Friday is set to highlight the growing importance of trade near the Russian border in China's northeast.

Putin ends his two-day, red-carpet visit to China in Harbin in Heilongjiang province, which has long-running trade and cultural ties to Russia, touring a Russian-China Expo and a forum on interregional cooperation.

Facing political isolation and Western sanctions over Russia's two-year-old invasion of Ukraine, Putin is increasingly turning to China to support its war economy.

Amid the pomp of a full state visit, Putin and Xi signed a joint statement on Thursday that hailed the "new era", countering the U.S. across a sweep of security and economic issues and a shared global view.

"The China-Russia relationship today is hard-earned, and the two sides need to cherish and nurture it," Xi told Putin.

"China is willing to... jointly achieve the development and rejuvenation of our respective countries, and work together to uphold fairness and justice in the world."

The joint statement fleshes out the "no limits" partnership the two declared in February 2022, days before Putin sent tens of thousands of troops into neighbouring Ukraine, launching the deadliest land war in Europe since World War Two.

The Latest Photos From Ukraine

A woman walks backdropped by bas-relief sculptures depicting war scenes in the National Museum of the History of Ukraine in the Second World War in Kyiv, Ukraine, Monday, April 8, 2024. (AP Photo/Vadim Ghirda)

Beijing is helping Moscow's war effort by providing drone and missile technology, satellite imagery and machine tools, U.S. officials said last month, although China says it has not provided weaponry to any party.

Russia's isolation from other powers has fuelled its trade with China, which surged 26.3% last year to a record $240.1, Chinese customs data shows. Russia has overtaken Saudi Arabia as China's top source of crude oil, with shipments jumping more than 24% despite Western sanctions.

An editorial in China's state-controlled Global Times newspaper on Friday cited the importance of burgeoning trade ties to the wider relationship, saying China had been Russia's largest trading partner for 13 straight years.

"These achievements are not easy and have been achieved by both countries overcoming various external challenges and unfavorable factors, highlighting the solid foundation of the China-Russia relationship," the editorial said.

Putin is flanked by a large trade delegation, which includes Finance Minister Anton Siluanov and Central Bank Governor Elvira Nabiullina.

Others include the heads of Russia's largest banks - Sberbank CEO German Gref and VTB chief Andrei Kostin - billionaire Oleg Deripaska, top oil producer Rosneft chief Igor Sechin and liquefied natural gas giant Novatek's boss, Leonid Mikhelson.

It was not immediately clear if Putin would make any further stops in Asia.

(Reporting by Bernard Orr and Beijing newsroom; Writing by Greg Torode; Editing by William Mallard)

Photos You Should See - May 2024

TOPSHOT - A woman wades through flood waters at an inundated residential area in Garissa, on May 9, 2024. Kenya is grappling with one of its worst floods in recent history, the latest in a string of weather catastrophes, following weeks of extreme rainfall scientists have linked to a changing climate. At least 257 people have been killed and more than 55,000 households have been displaced as murky waters submerge entire villages, destroy roads and inundate dams. (Photo by LUIS TATO / AFP) (Photo by LUIS TATO/AFP via Getty Images)

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COMMENTS

Effective Research Assignments
empowers students to focus on and to master key research and critical thinking skills, provides opportunities for feedback, and. deters plagiarism. Periodic class discussions about the assignment can also help students. reflect on the research process and its importance. encourage questions, and. help students develop a sense that what they are ...
How to Write a Research Paper
A research paper is a piece of academic writing that provides analysis, interpretation, and argument based on in-depth independent research. Research papers are similar to academic essays, but they are usually longer and more detailed assignments, designed to assess not only your writing skills but also your skills in scholarly research ...
How to Find Trustworthy Sources for School Assignments
Research the website: Look up the company that owns the website and see how well-known and trusted it is for the information you're citing. You'll want to use sites that are: Well-known and well-respected. Credible. Check media coverage: Look for a Media or Press page on the website.
Sources and Your Assignment
Sources and Your Assignment. The first step in any research process is to make sure you read your assignment carefully so that you understand what you are being asked to do. In addition to knowing how many sources you're expected to consult and what types of sources are relevant to your assignment, you should make sure you understand the role ...
Library Guides: Effective Research Assignments: Home
Provide examples of topics that are appropriate in scope for the assignment at hand, and provide feedback to individual students as they begin to develop and refine their topics. Design and test your assignment. An effective research assignment targets specific skills, for example, the ability to trace a scholarly argument through the ...
Academic Guides: Common Assignments: Journal Entries
This guide includes tips on writing common course assignments. Both in traditional and online classrooms, journal entries are used as tools for student reflection. By consciously thinking about and comparing issues, life experiences, and course readings, students are better able to understand links between theory and practice and to generate ...
PDF How Handouts for Research Assignments Guide Today's College Students
In this 2010 mid-year progress report, we present findings from a content analysis of 191 handouts voluntarily submitted from instructors at 28 U.S. colleges and universities. The handouts in our sample were distributed in the last year to students for course-related research assignments.2.
LibGuides: Designing Research Assignments: Assignment Ideas
Alternative Assignments. There are many different types of assignments that can help your students develop their information literacy and research skills. The assignments listed below target different skills, and some may be more suitable for certain courses than others. Research Skills: Searching, Analysis, Evaluating Sources.
Research Guides: Research Assignment Design: Overview
Students experience a greater cognitive load when researching because they lack domain knowledge. You can help students focus their energies by ensuring your assignment matches your priorities. For example, to prioritize synthesizing arguments, design an assignment around reading and writing with sources, and limit the need for finding sources ...
Effect of Assignment Choice on Student Academic Performance ...
Choice of assignment has been shown to increase student engagement, improve academic outcomes, and promote student satisfaction in higher education courses (Hanewicz, Platt, & Arendt, Distance Education, 38(3), 273-287, 2017). However, in previous research, choice resulted in complex procedures and increased response effort for instructors (e.g., Arendt, Trego, & Allred, Journal of Applied ...
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Academic Assignment Samples and Examples
The basic structure is of three parts: introduction, discussion, and conclusion. It is, however, advisable to follow the structural guidelines from your tutor. For example, our master's sample assignment includes lots of headings and sub-headings. Undergraduate assignments are shorter and present a statistical analysis only.
Free, Downloadable Educational Templates for Students
Revised on July 23, 2023. We have designed several free templates to help you get started on a variety of academic topics. These range from formatting your thesis or dissertation to writing a table of contents or a list of abbreviations. We also have templates for various citation styles, including APA (6 and 7), MLA, and Chicago.
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The main objective of this research is to analyze how homework assignment strategies in schools affect students' academic performance and the differences in students' time spent on homework. Participants were a representative sample of Spanish adolescents (N = 26,543) with a mean age of 14.4 (±0.75), 49.7% girls. A test battery was used to ...
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study explores the existing research on the roles of assignment de-sign and highlights the current state of assignment design in Chi-na's primary and secondary schools. In terms of assignment loads, content, formats, and stratification, suggestions are made. Science Insights Education Frontiers 2022; 11(1):1509-1516. Doi: 10.15354/sief.22.or014
APA resources to help teachers engage students in research
These additional free APA resources are also helpful to teachers: Psychology topics: Access research, podcasts, and publications on nearly 100 topics. APA Dictionary of Psychology: Over 25,000 authoritative entries across 90 subfields of psychology. APA Style Journal Article Reporting Standards: These standards offer guidance on what ...
A Guide to Pursuing Research Projects in High School
Set goals for completing the introduction, various sections of the body, and your conclusion. 6. Edit Your Paper. There will be multiple stages of editing that need to happen. First, you will self-edit your first draft. Then, you will likely turn a draft of your paper in to your mentor for another round of editing.
Socioeconomic-Based School Assignment Policy and Racial Segregation
WCPSS' school assignment policy arguably does just the opposite, and below we examine how the design of the policy facilitates this uncommon pattern of effects. ... M atthew A. L enard is a PhD student at the Harvard Graduate School of Education. His research focuses on the economics of education, teacher labor markets, and program and policy ...
Sample Student Projects
MOC Student Projects on Country & Cluster Competitiveness. The competitive assessments listed on this page have been prepared by teams of graduate students mostly from Harvard Business School and the Harvard Kennedy School of Government and other universities as part of the requirements for the Microeconomics of Competitiveness.
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Leveraging generative AI to modernize nursing education
Leveraging generative AI to modernize nursing education. Michalowski urges nurse educators to harness the potential of generative AI. May 13, 2024. Brett Stursa. Martin Michalowski. The proliferation of new generative artificial intelligence (AI) tools can be challenging for nurse educators and clinicians to keep up with, as the potential ...
Statistics Project Topics: From Data to Discovery
1.2 Statistics Project Topics for High School Students. 1.3 Statistical Survey Topics. 1.4 Statistical Experiment Ideas. 1.5 Easy Stats Project Ideas. 1.6 Business Ideas for Statistics Project. 1.7 Socio-Economic Easy Statistics Project Ideas. 1.8 Experiment Ideas for Statistics and Analysis. 2 Conclusion: Navigating the World of Data Through ...
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Assignment Russia
Marvin Kalb—Assignment Russia: Becoming a Foreign Corresponding in the Crucible of the Cold War - with Jake Tapper. April 13: National Press Club Virtual Book Event —Marvin Kalb, Assignment ...
Russia Says US 'Playing With Fire' in 'Indirect War' With Moscow
US News is a recognized leader in college, grad school, hospital, mutual fund, and car rankings. Track elected officials, research health conditions, and find news you can use in politics ...
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Putin to Push Growing Moscow-Beijing Trade in China's Northeast
US News is a recognized leader in college, grad school, hospital, mutual fund, and car rankings. Track elected officials, research health conditions, and find news you can use in politics ...