effect of assignment on student

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Open Access

Peer-reviewed

Research Article

Assignment strategies modulate students’ academic performance in an online learning environment during the first and second COVID-19 related school closures

Roles Conceptualization, Data curation, Formal analysis, Methodology, Visualization, Writing – original draft

* E-mail: [email protected]

Affiliation Department of Psychology, University of Basel, Basel, Switzerland

Roles Conceptualization, Writing – review & editing

Affiliations Centre for Mathematical Cognition, School of Science, Loughborough University, Loughborough, United Kingdom, Leibniz-Institut fuer Wissensmedien, Tübingen, Germany, LEAD Graduate School and Research Network, University of Tuebingen, Tübingen, Germany

Affiliations Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI, United States of America, Carney Institute for Brain Science, Brown University, Providence, RI, United States of America, Institute of Cognitive Science, Osnabrück University, Osnabrück, Germany

• Markus Wolfgang Hermann Spitzer, 
• Korbinian Moeller, 
• Sebastian Musslick

• Published: May 3, 2023
• https://doi.org/10.1371/journal.pone.0284868
• Reader Comments

A growing number of studies seek to evaluate the impact of school closures during the ongoing COVID-19 pandemic. While most studies reported severe learning losses in students, some studies found positive effects of school closures on academic performance. However, it is still unclear which factors contribute to the differential effects observed in these studies. In this article, we examine the impact of assignment strategies for problem sets on the academic performance of students (n ≈ 16,000 from grades 4–10 who calculated ≈ 170,000 problem sets) in an online learning environment for mathematics, during the first and second period of pandemic-related school closures in Germany. We observed that, if teachers repeatedly assigned single problem sets (i.e., a small chunk of on average eight mathematical problems) to their class, students’ performance increased significantly during both periods of school closures compared to the same periods in the previous year (without school closures). In contrast, our analyses also indicated that, if teachers assigned bundles of problem sets (i.e., large chunks) or when students self-selected problem sets, students’ performance did not increase significantly. Moreover, students’ performance was generally higher when single problem sets were assigned, compared to the other two assignment types. Taken together, our results imply that teachers’ way of assigning problem sets in online learning environments can have a positive effect on students’ performance in mathematics.

Citation: Spitzer MWH, Moeller K, Musslick S (2023) Assignment strategies modulate students’ academic performance in an online learning environment during the first and second COVID-19 related school closures. PLoS ONE 18(5): e0284868. https://doi.org/10.1371/journal.pone.0284868

Editor: Ehsan Namaziandost, Ahvaz Jundishapur University: Ahvaz Jondishapour University of Medical Sciences, ISLAMIC REPUBLIC OF IRAN

Received: October 7, 2022; Accepted: April 10, 2023; Published: May 3, 2023

Copyright: © 2023 Spitzer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data are available at https://osf.io/astgp/ Identifier: DOI 10.17605/OSF.IO/ASTGP .

Funding: This work was financially supported by Schmidt Science Fellows, in partnership with the Rhodes Trust, in the form of a grant awarded to SM. No additional external funding was received for this study. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Starting from March 2020, the COVID-19 pandemic led to school closures around the world [ 1 ]. These closures required teachers to adopt different approaches to home schooling and distance learning —with varying degrees of success [ 2 , 3 ]. One such approach are online learning environments, the use of which grew rapidly since the outbreak of the ongoing COVID-19 pandemic [ 4 – 8 ]. For instance, the use of the curriculum-based online learning environment for mathematics Bettermarks increased exponentially in Germany among students (age range:10–16) from the point of first school closures [ 6 ]. The flexibility provided by such online learning environments, which can be used both remotely as well as in class, affords different strategies of assigning problem sets to students to work on. For instance, in Bettermarks , teachers can assign small chunks of problem sets (so called single problem sets ) within a book (e.g., Basics of Fractions ) or they can assign large chunks which include the entire book—including all single problem sets of this book—to their students. In addition, students may select problem sets themselves. These different problem set assignment strategies may have modulated students’ performance during school closure. If so, evaluating potential differential effects of assignment strategies on students’ academic performance (e.g., better performance for smaller bits for learning) would help to better understand what makes online learning more successful. To examine this hypothesis, we assessed the effect of different assignment strategies on student’s performance in Bettermarks during times of first and second school closures. In the following, we will first elaborate on the impact of school closures on students’ academic achievements before we will outline the details of the current study.

The effect of school closures on students’ academic performance

A growing number of studies investigated the influence of school closures on students’ academic performance [ 4 – 7 , 9 – 12 ]. Most of them reported detrimental effects on academic achievement [ 9 – 11 ], as well as student’s physical [ 13 , 14 ], mental [ 15 – 18 ], and social wellbeing [ 19 – 22 ]. For instance, Engzell et al. (2021) assessed student’s performance in national monitoring examinations [ 23 ] before and after school closures in the Netherlands and reported a 60% learning loss based on data from 350,000 students (age range 8–11). The detrimental performance effects observed in this study comport with earlier reports on learning losses during summer holidays [ 24 – 27 ]. In addition, complementing results from Engzell et al. (2021), Schult et al., 2021 reported learning losses for fifth grade students in Germany, equivalent to about one entire month of education after the first closure of schools which lasted for less than three months. Similar effects of school closures were also reported by Maldonado and De Witte (2020), who evaluated the effect of the closure of Flemish schools in Belgium in more than 4,000 students. In line with the two studies mentioned earlier [ 9 , 10 ], their findings revealed significant learning losses in mathematics, specifically for students from low socio-economic backgrounds.

Contrary to the detrimental effects described above, a separate line of studies reported no or even positive effects of school closures on academic performance [ 4 – 7 , 12 ]. For instance, Gore et al. (2020) found no significant influence of school closures on students’ academic learning outcomes in mathematics in an Australian cohort which included over 4,800 primary school students from New South Wales. In addition, Tomasik et al. (2020) analyzed data from 28,000 students who used an online learning environment before, during, and after school closures in Switzerland. Their results suggested severe learning losses in mathematics—in particular for low-performing Swiss students. However, Tomasik et al. (2020) also observed that some students seemed unaffected by school closures.

Finally, a growing number of studies reported the use of digital technologies to learn from distance during school closures [ 28 – 31 ] and some studies indicated that academic performance in online learning environments improved during school closures [ 4 – 6 ]. For instance, Meeter (2021) examined data from almost 100,000 students from the Netherlands and reported significant learning gains for the period of school closures in an online learning environment for mathematics. Similarly, Van der Velde (2021) investigated students’ performance when learning French in an online environment and found significant learning gains across more than 130,000 students from the Netherlands.

Such performance improvements in online learning have been attributed to a remarkable increase in their usage during school closures and concomitant increase in study time [ 4 , 5 ]. However, a recent within-student analysis of over 2,500 students in Germany (grades 4 to 10; age range:10–16) reported improvements in academic performance while controlling for the extend of online learning and problem set difficulty [ 6 ]. Results from this analysis suggest that students performed mathematical problem sets more accurately during the first period of school closures in Germany compared to the same time period in the year before (not affected by the COVID pandemic). Moreover, results from this study suggested a narrowing performance gap, with low-performing students showing more pronounced improvements in performance than already high-performing students [ 6 ].

While the above studies substantiate the relevance of online learning environments during school closures, they offer little insight into which factors contributed to the reported learning gains. Some suggested that such positive effects may result from a higher focus when learning at home compared to the classroom which may be nosier and thus more distracting [ 4 , 5 ]. Others argued for the beneficial effects of software integrated features, such as rapid feedback [ 32 – 37 ] or computer-based scaffolding in STEM subjects (i.e., science, technology, engineering, and mathematics) [ 38 – 42 ], both of which may aid learning over and above traditional teaching materials.

Another feature of online learning environments is the flexibility with which they can be used. Teachers may assign problem sets either one by one (i.e., in small chunks) following the progression on the content taught, in bulk (i.e., in larger chunks), or students may even select problem sets themselves. The most common form of assignment in, for instance, Bettermarks , are one by one assignments by teachers (65%), followed by bulk assignments by teachers (24%), and self-selected assignments (11%). As teacher-student interaction is known to affect students’ learning outcomes [ 43 ], we aimed at evaluating the role of problem set assignment strategies within online learning environments during school closures. In particular, we contrasted the most common form of assignment (single problem sets assigned by teachers) against the two alternative assignment strategies (teachers assign problem sets in bulk or students self-select their problem set) in determining students’ performance outcomes in Bettermarks .

The present study

In this study, we investigated performance-dependent changes as a function of assignment policy by contrasting the most common assignment policy (teachers assigning single problem sets one by one to their students) against two other assignment policies: (1) teachers assigning all problem sets in an online math book at once (e.g., Basics of Fractions ) or (2) students’ self-selected problem sets. We also examined whether time window-dependent performance changes (i.e., performance before vs. performance during school closures) were affected by the assignment policy. Finally, to evaluate the validity of our results we replicated these analyses for the second period of school closure in Germany and its respective time window in the previous year. As such, this study not only evaluated evidence for the first period of school-closures in Germany but also replicated the analyses for the second school-closure, which reaffirms reported effects. Similar to previous studies on the influence of school closures on learning losses within online learning environments [ 4 , 5 , 7 ], we compared performance-dependent changes as a function of assignment policy with a between-student analysis approach for the first period of school closures and replicated this analysis for the second period of school closures to examine the robustness of the first results. In addition, this also allowed us to examine whether results for the second period of school closures would be similar to the first period of school closures and thus would generalize the findings to more than one period of school closures. These analyses encompass data from more than 16,000 students (more than 1,900 classes) who calculated on more than 170,000 problem sets.

Based on the set of findings described above [ 4 – 6 ], we expected performance increases due to school closures in the online learning environment. Our hypotheses about the effects of assignment strategy derive from prior studies on spaced and massed learning [ 44 – 49 ]. In this context, it was repeatedly observed that spaced learning—breaking down learning content into smaller bits covered in several sessions (separated by up to several days)—led to significantly better learning outcomes than massed learning reflecting long but fewer learning session on one and the same topic. Against this background one might assume that students’ learning outcomes should be better when teachers’ assign problem sets one-by-one and not all at once. Thus, we expected that assignments of single problem sets, compared to assignments of entire books, may foster greater improvements in performance.

Finally, we were interested in whether assigning single problem sets (the most common assignment policy) or self-selecting single problem sets may have had different effects on performance because these two assignment policies might imply differences in extrinsic vs. intrinsic motivation to complete the respective problem sets [ 50 , 51 ]. In particular, allowing students freedom to choose has been associated with increased intrinsic motivation, effort, and task performance [ 52 – 55 ] with higher intrinsic motivation leading to increased performance [ 56 , 57 ]. Thus, one might expect better performance for students who selected single problem sets themselves as compared to being assigned single problem sets by teachers.

In summary, investigating differences between assignment policies on a large scale, may have important implications on how students best learn mathematics in online learning environments. Thus, we sought to explore whether differences between assignment policies which imply influences of spaced vs. massed learning (i.e., assigning single problem sets vs. all at once) and differences in motivational aspects (i.e., teacher-assigned vs. self-selected problem sets) exist and modulated effects of school closures.

Online learning environment

We made use of data collected with the Bettermarks online learning environment. The user interface of Bettermarks is depicted in Fig 1 . Teachers and students use this software in both private and public schools in all states in Germany, including different school types (i.e., from vocational to academic track schools). The online learning environment contains over 100 different text books covering the curriculum for mathematics in Germany for grades 4 to 10. These virtual mathematics text books cover over 2 000 different mathematical problem sets. Teachers and students with access to the software can freely decide when and how many problem sets they want to work on. Bettermarks is used to solve mathematical problem sets online, which can be done inside, but also outside the classroom (e.g., at home). Typically, Bettermarks is used to work on problem sets. Two different assignment possibilities exist. Teachers can assign mathematical problem sets (each including eight mathematical problems on average) to their students using the online learning environment. However, students may also work on problem sets on their own, independently of assignments they receive from their teachers. Students receive immediate feedback on each computed problem, indicating whether their answer was correct or not. After completing a problem set, they may choose to repeat it. However, on each new repetition, the parameterization of the problem set changes, and thus, students do not benefit from memorizing the answer of the same problem set.

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(1) Students and teachers can choose mathematical topics, such as “Calculating with Money” from a library. (2) Each mathematical topic contains several problem sets which contain several individual problems. (3) Immediate feedback is given on every problem. (4) Explanations can be provided.

https://doi.org/10.1371/journal.pone.0284868.g001

Students may also leave the system anytime during their learning process. The dataset is entirely anonymous, and thus, it is not possible to identify any personal information from students or teachers (e.g., gender or age). When signing up with the software, each software user agrees that their data will be stored anonymously and used for data analyses. This analysis considered a retrospective assessment of data collected by Bettermarks. All procedures were in accordance with the ethical standards of the national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

The software covers the curricula of mathematics from classes 4–10 in Germany. Mathematical topics are placed in different books such as Basics of Fractions . These books are described in more detail in the S1 Text . The software collects data on each computed problem set on (1) the accuracy of a student; (2) which problem set he/she worked on; (3) whether the problem set was assigned or self-selected; (4) the date the problem set was worked on, and (5) whether students completed the problem set or left the assignment without completion. Bettermarks implements an internal incentive structure according to which students gain a star within the system if they complete a problem set with 100% accuracy. They receive a coin if they perform a problem set with more than 60% accuracy. Fig 2 illustrates some example problem sets of the software. Fig 3 depicts its usage over the past five years.

Example problem sets of the books (A) Basics of Fractions , (B, C) Calculating Percents , and (D) “ Linear Equations ”.

https://doi.org/10.1371/journal.pone.0284868.g002

Each dot resembles the total number of students (left panel) and classes (middle panel) using the online learning environment, as well as the total number of problem sets completed (right panel) per month of the year. Different colors, lines and dot shape correspond to different years of usage. Note a stark increase in software usage in March 2020 when schools closed for the first time due to the COVID-19 pandemic.

https://doi.org/10.1371/journal.pone.0284868.g003

Importantly, the online learning environment did not change over the past years. This allowed us to investigate the performance on problem sets across different time points before and during school closures due to the COVID-19 pandemic.

Inclusion criteria for sample selection

Data considered in the current study (i.e., students, problem sets, dates, and the performance on these problem sets) were selected based on the following criteria which were set prior to data analysis (also see Fig 4 ). First, we only included students who calculated problem sets in books covering three major mathematical topics, namely fractions, percentages, and linear equations. We predefined these three book topics prior to all analyses and included no other book topics (see S1 Text ). Second, we only considered problem sets computed during the first period of school closures in 2020 (March 15 th , 2020, until June 1 st , 2020) and the same period in the year before (March 15 th , 2019, until June 1 st , 2019). In addition, we included data from the second period of school closures in 2021 (January 1 st , 2021 until February 28 th , 2021) and the same time window in the year before (January 1 st , 2020 until February 28 th , 2020). Third, we only included students who registered with the software before March 15 th , 2019. Forth, for each book topic, we only considered students who calculated at least five problem sets of a book, and who worked on a book before school closures or during school closures, but not both. In case students repeated a problem set, only the best result of each student on each problem set was included whereas all other repetitions were excluded from the analysis, so to approximate their best performance. Fifth, within a class, the same assignment strategy was always assigned. That is, if students of a class computed problem sets, all of them selected these problem sets on their own or via one of the two assignment types by their teacher. Finally, for single problem set assignments and entire book assignments, we only considered classes with at least 15 students, to assure that we assessed teacher-student interactions within a class context of a minimum size of 15 students. Based on these inclusion criteria, the dataset comprised a total of 16,646 students from 1,908 classes who calculated a total of 170,522 problem sets.

Only students who were already registered before first school closures were included in all analyses.

https://doi.org/10.1371/journal.pone.0284868.g004

Independent and dependent variables

Independent variables encompass the categorical variable time window which indicated whether schools were closed or not and a categorical assignment variable which denoted assignment policy. We additionally controlled for the number of problem sets assigned to students by considering computed problem sets as a covariate in our analyses. We compared the most frequently applied assignment policy of teachers assigning single problem sets (labeled as single problem sets ) with two other possible assignment policies, namely assigning entire books (labeled as book ) and self-selecting problem sets (labeled as self-selected problem sets ). Note that teacher-directed assignment policies are independent from the number of students who receive the assignment. In other words, teachers may assign single problem sets or books to either one or multiple students. We considered three different dependent variables that derive from the binary incentive structure of Bettermarks . These included (1) a binary completion variable indicating whether students completed a problem set or not, irrespective of their performance (2) a binary stars variable indicating whether students received a star (i.e., achieved 100% accuracy on a given problem set), and (3) a binary coins variable indicating whether students received at least one coin (i.e., achieved at least 60% accuracy on a given problem set).

Data analysis

We applied this model across different analyses, with each analysis contrasting different levels of each independent variable. That is, one set of analyses contrasted the single problem set assignment policy against the book assignment policy, and another set contrasted the single problem set policy against the self-selected assignment policy. We also performed different analyses for different contrasts of time windows: in one set of analyses, we compared the first period of school closures in 2020 with the same period a year before and in another set of analyses, we compared the second lockdown period in 2021 with the same period in the previous year. This resulted in 3 (three dependent variables) x 2 (assignment policy contrasts) x 2 (time window contrasts) statistical models that we report on below. As six regression analyses were run for the first school closure, we corrected the significance level using Bonferroni correction meaning that we refer to significant results with p-values below .0083. To investigate the robustness of these results, we replicated each analysis for the second period of school closures.

First shutdown of schools

Results from these analyses are depicted in Figs 5 and 6 . All results are also shown in Tables 1 – 3 . In the following sections, we describe the respective analyses.

Predicted estimates from the regression models for performance under single problem set assignments (red) and book assignments (lightblue), for the school closure in 2020 (left panels) and 2021 (right panels) compared to the same time periods in the preceding years. Error bars indicate one standard error of the mean (SEM). Students showed greater completion rates, gained more stars, and gained more coins during single problem set assignments, compared to entire book assignments. In addition, students completed more problem sets and gained more coins during the first and second school closure when they were assigned single problem sets compared to the same time period in the previous year. This pattern did not replicate in cases where teachers assigned entire books.

https://doi.org/10.1371/journal.pone.0284868.g005

Predicted estimates from the regression models for performance for single problem set assignments (red) and self-selected problem sets (black), for the school closure in 2020 (left panel) and 2021 (right panel) compared to the same time periods in the preceding years. Error bars indicate one SEM. Students completed more problem sets and gained more coins during the first and second school closure if they were assigned single problem sets as compared to the same time periods in the preceding years. We did not observe the same pattern if students self-selected their problem sets.

https://doi.org/10.1371/journal.pone.0284868.g006

https://doi.org/10.1371/journal.pone.0284868.t001

https://doi.org/10.1371/journal.pone.0284868.t002

https://doi.org/10.1371/journal.pone.0284868.t003

Completion rate (single problem sets vs. entire books).

The main effect of time window was significant ( b = 0.14; z = 3.94; p < .001), with more completed problem sets during school closure as compared to the same time window in the previous year. The significant main effect of assignment ( b = 0.29; z = 11.62; p < .001) reflected higher completion rates if individual problem sets were assigned compared to books assignments. The main effect of computed problem sets was not significant ( b = -.001; z = -1.49; p = .136). Finally, the interaction of time window and assignment was significant ( b = .13; z = 5.20; p < .001), suggesting that the difference in completion rates between school closures and control periods was larger for students who got assigned single problem sets versus entire books.

We additionally conducted a post-hoc analysis on the effect of assignment strategy for the time period before school closures, controlling for number of assignments. Results of this logistic mixed model revealed a significant effect of assignment ( b = 0.15; z = 5.16; p < .001), with higher completion rates on single problem set assignments compared to entire book assignments. The main effect of computed assignments was significant ( b = -0.01; z = -3.00; p = .003) with lower completion rates when students computed more assignments.

Stars (single problem sets vs. entire books).

The main effect of time window was significant ( b = -.02; z = -0.55; p = .586). The main effect of assignment was significant ( b = .15; z = 5.94; p < .001), with more stars gained on single problem set assignments compared to book assignments. The main effect of computed problem sets was not significant ( b < .001; z = 0.91; p = .364). The interaction of time window and assignment was not significant ( b = .03; z = 1.12; p = .264).

As in the previous analysis, we carried out a post-hoc analysis on the effect of assignment before school closures. Results of the logistic mixed model with computed problem sets as covariate revealed a significant effect of assignment ( b = .12; z = 3.83; p = .001), with more coins gained before school closures on single problem set assignments compared to book assignments. The main effect of computed problem sets was not significant ( b < .001; z = -0.20; p = .844).

Coins (single problem sets vs. entire books).

The main effect of time window was significant ( b = .09; z = 2.85; p = .0043), with overall more coins gained during school closure as compared to the same time window in the previous year. The main effect of assignment was significant ( b = .25; z = 10.15; p < .001), with more coins gained on single problem set assignments compared to book assignments. The main effect of computed problem sets was not significant ( b < -.001; z = -0.40; p = .688). The interaction of time window and assignment was significant ( b = .10; z = 4.19; p < .001) suggesting that students who got assigned single problem sets showed a greater difference in acquired coins as a function of time window, as compared to students who got assigned entire books.

We conducted a post-hoc analysis to investigate the effect of assignment before school closures in isolation. Results of this logistic regression model revealed a significant effect of assignment ( b = .14; z = 4.68; p < .001), with more coins gained before school closures on single problem set assignments compared to book assignments. The main effect of computed problem sets was not significant ( b = .002; z = -1.00; p = .050).

Completion rate (single problem sets vs. self-selected problem sets).

Again, the main effect of time window was significant ( b = 0.20; z = 6.12; p < .001) indicating that overall, students completed more problem sets during school closure as compared to the same time window in the previous year. On the other hand, the main effect of assignment was not significant ( b = -0.10; z = -0.47; p = .637). The main effect of computed problem sets was not significant ( b < .002; z = -2.29; p = .022). The interaction of time window and assignment was significant ( b = 14; z = 4.24; p < .001). The interaction indicates that the difference in completion rate as a function of school closures was larger for students who got assigned single problem sets by their teachers compared to students who selected their problem sets.

The post-hoc analysis on the main effect of assignment before school closures was not significant ( b = -0.45; z = -1.85; p = .064). The main effect of computed problem sets was significant ( b = -0.005; z = -2.96; p = .003) with lower completion rates when more assignments were computed.

Stars (single problem sets vs. self-selected problem sets).

The main effect of time window was not significant ( b = .007; z = 0.22; p = .827). The main effect of assignment was not significant ( b = -.03; z = -0.12; p = .906). The main effect of computed problem sets was not significant ( b < -.001; z = -0.27; p = .786). The interaction of time window and assignment was not significant ( b = .04; z = 1.14; p = .255).

The post-hoc analysis on the main effect of assignment before school closures did not reveal a significant main effect for assignment ( b = -0.19; z = -0.82; p = .412). The main effect for computed problem sets was also not significant ( b < -0.01; z = -1.10; p = .271).

Coins (single problem sets vs. self-selected problem sets).

The main effect of time window was significant ( b = .13; z = 4.03; p < .001), with overall more coins gained during school closure as compared to the same time window in the previous year. The main effect of assignment was not significant ( b = -.09; z = -0.44; p = .662). The main effect of computed problem sets was not significant ( b < -.001; z = -1.04; p = .297). The interaction of time window and assignment was significant ( b = .13; z = 4.05; p < .001) indicating that students who got assigned single problem sets by their teachers showed a greater difference in acquired coins as a function of time window relative to students who self-selected their problem sets.

The post-hoc analysis on the main effect of assignment before school closures did not reveal a significant main effect for assignment ( b = -0.43; z = -1.77; p = .077). The main effect for computed problem sets was also not significant ( b < -0.01; z = -2.11; p = .034).

Second lockdown

Figs 5 and 6 as well as Tables 1 – 3 show the results of the analyses. Each analysis is described in the following sections.

The main effect of time window was not significant ( b = -.01; z = -.35; p = .73). The main effect of assignment was significant ( b = .54; z = 22.31; p < .001), with higher completion rates on problem sets if they were assigned one by one as opposed to as entire books. The main effect of computed problem sets was not significant ( b = -.001; z = -1.76; p = .079). The interaction of time window and assignment was significant ( b = .30; z = 12.48; p < .001), suggesting that the difference in completion rates as a function of time window was larger for single problem set assignments as compared to book assignments.

The post-hoc analysis on the main effect of assignment before school closures was significant ( b = -0.27; z = 5.34; p < .001), with higher completion rates on single problem set assignments compared to entire book assignments. The main effect of computed problem sets was not significant ( b < -0.01; z = -0.05; p = .960).

The main effect of time window was significant ( b = -.15; z = -3.81; p < .001), with less stars gained during school closure. The main effect of assignment was significant ( b = .34; z = 13.55; p < .001), with more stars gained on single problem set assignments, compared to entire books assignments. The main effect of computed problem sets was not significant ( b < .001; z = 1.64; p = .101). The interaction of time window and assignment was significant ( b = .15; z = 5.78; p < .001), indicating that the difference between stars gained during the two time windows was more expressed for single problem set assignments as compared to book assignments.

The post-hoc analysis on the main effect of assignment before school closures was significant ( b = 0.21; z = 3.74; p < .001), with more stars gained on single problem set assignments compared to entire book assignments. The main effect of computed problem sets was not significant ( b < 0.01; z = 0.87; p = .375).

The main effect of time window was not significant ( b = .01; z = .19; p = .853). The main effect of assignment was significant ( b = .46; z = 19.07; p < .001), with more coins gained on single problem set assignments compared to book assignments. The main effect of computed problem sets was not significant ( b < -.001; z = -1.22; p = .224). The interaction of time window and assignment was significant ( b = .23; z = 9.52; p < .001) with the difference in coins gained between the two time windows being more pronounced for single problem set assignments as opposed to book assignments.

The post-hoc analysis on the main effect of assignment before school closures was significant ( b = 0.22; z = 4.44; p < .001), with more coins gained on single problem set assignments compared to entire book assignments. The main effect of computed problem sets was not significant ( b < 0.01; z = 1.04; p = .297).

The main effect of time window was not significant ( b = -.05; z = -1.31; p = .191). The main effect of assignment was significant ( b = .65; z = 2.93; p = .003), with higher completion rates for single problem set assignments as compared to book assignments. The main effect of computed problem sets was not significant ( b = -.001; z = -1.09; p = .274). The interaction of time window and assignment was significant ( b = .41; z = 9.92; p < .001), suggesting greater differences in completion rate as a function of time window if problem sets were assigned one by one as opposed to being self-selected.

The post-hoc analysis on the main effect of assignment before school closures was not significant ( b = -0.24; z = 0.63; p = .526). The main effect of computed problem sets was not significant ( b < -0.01; z = -0.71; p = .478).

The main effect of time window was significant ( b = -.15; z = -3.22; p = .845), with less stars gained during school closure. The main effect of assignment was significant ( b = .66; z = 2.94; p = .003), with more stars gained if single problem set were assigned as compared to being self-selected. The main effect of computed problem sets was not significant ( b < .001; z = 1.55; p = .121). The interaction of time window and assignment was significant ( b = .17; z = 3.65; p < .001), indicating that the lockdown period was associated with more stars compared to the same time frame in the previous year if problem sets were assigned one by one by teachers.

The post-hoc analysis on the main effect of assignment before school closures was not significant ( b = -0.15; z = 0.39; p = .696). The main effect of computed problem sets was not significant ( b < -0.01; z = -0.17; p = .865).

The main effect of time window was not significant ( b = -.06; z = 2.71; p = .007). The main effect of assignment was significant ( b = .66; z = 2.86; p = .004), with more coins gained on single problem set assignments, compared to self-selected problem sets. The main effect of computed problem sets was not significant ( b < -.001; z = -0.29; p = .774). The interaction of time window and assignment was significant ( b = .37; z = 8.73; p < .001) with the difference in coins gained between the two time windows being more pronounced for single problem set assignments by teachers.

The post-hoc analysis on the main effect of assignment before school closures was not significant ( b = 0.05; z = 0.13; p = .894). The main effect of computed problem sets was not significant ( b < 0.01; z = 0.55; p = .581).

In this study, we investigated the influence of teachers’ assignment policies during school closures on the performance of students (grade 4–10; age range:10–16) in an online learning environment for mathematics with a between-student analysis approach. We observed that if teachers assigned single problem sets to students, the probability of completing a problem set, as well as the probability of achieving at least 60% accuracy on a given problem set was significantly and consistently higher for students who computed these single problem sets during the first (i.e., 15.03. to 1.06.2020) and second period of school closures (i.e., 01.01.to 28.02.2021) in Germany as compared to students who computed the same problem sets during the same period the year before, respectively. However, we did not find this effect if teachers assigned entire books or if students self-selected problem sets. We observed this effect across three mathematical topics (i.e., fractions, percentages and interest, and linear equations; see S1 Text ). Taken together, these results indicate that the beneficial effects of assigning single problem sets persist across two different periods of school closures and across different mathematical topics and pertain to students’ completion rates, as well as their performance (achieving at least 60% accuracy).

It is noteworthy that this study differed from previous analyses of student performance in the same learning software [ 6 , 61 , 62 ]. An important difference pertains to the way in which we operationalized students’ performance. In an earlier study, we measured students’ performance in terms of their average error rate, normalized by problem set difficulty [ 6 ]. Here, we employed binary factors coding students’ accuracy (e.g., whether they completed a problem set, gained a star, or gained a coin) as we restricted the analysis to specific mathematical contents which were studied most frequently within the online learning environment. An advantage of such an approach is that we were able to evaluate whether our analyses replicate across book topics. The three mathematical topics (fractions, percentages and interest, and linear equations) considered are of tremendous importance for students to perform well on as they have been observed to be robust predictors for future mathematical achievement [ 63 – 67 ], socio-economic status and overall income during adulthood [ 68 – 72 ].

Another advantage of this analysis is the assessment of completed problem sets in combination with students’ performance on mathematical problem sets. This allowed us to examine whether students completed more and got better on these completed problem sets. Without considering completion rates in online learning environment, it may be that students just complete easy problem sets, but do not complete rather challenging problem sets. Thus, finding that students completed more problem sets and increased their performance at the same time on these completed problem sets compared to two previous cohorts suggests that students actually performed better. If students would have shown increased performance but decreased completion rates, then this could have suggested that students just completed rather simpler problem sets.

Finally, we investigated whether the performance of two cohorts of students (before each lockdown) differed from another two cohorts of students (during each lockdown) with respect to completion rates, achieving 60% accuracy and achieving 100% accuracy on the same problem sets. We considered the performance on specific mathematical topics with a between-student analysis approach (akin to previous analyses on COVID-19 related performance changes [ 4 , 5 , 7 ]) as improvements on the same problem sets within the same students over longer time horizons would have been expected.

The reported analyses may shed light on previously observed performance improvements of students using online learning environments during the first COVID-19 related school closure in 2020 [ 4 – 6 ]. These observations seem unexpected given that most studies suggest detrimental effects of school closures [ 9 – 11 ]. As such, the results of the present study suggest that students may learn better during school closures when small bits of information were assigned.

The beneficial effects of teachers assigning problem sets one after another, as opposed to assigning them in bundles or letting students self-select, highlight the importance of how learning materials should be presented to students. In particular, these results suggest that assigning smaller bits of learning content in terms of smaller sets of problem sets as compared to entire books as well as compared to letting students self-select problem sets was most beneficial for students’ mathematical learning.

As such, our results are in line with previous evidence suggesting that spacing out learning content over time, provided to students in small chunks, leads to enhanced learning outcomes [ 44 – 49 ]. Moreover, our results rather speak in favor of a large effect teacher incentives may have, as single problem set assignments were associated with overall increased performance and increased completion rates compared to self-selected assignments. These results may be explained by motivational theories of effort allocation which propose a positive connection between incentives and academic performance [ 73 – 75 ]. Based on these theories, one may speculate that students’ performance depends on incentives provided by the teachers. More precisely, students may be more extrinsically motivated to complete and perform well on problem sets when they were assigned by their teachers (e.g., as part of homework) compared to when they select to perform these problems themselves. Importantly, this rather extrinsic motivational view does not speak against intrinsic motivational effects which the self-selection of problem sets might have [ 76 ], but it may be that the effect of extrinsic incentives was larger than the effect of intrinsic incentives. Nevertheless, the positive effect single problem set assignments had, compared to self-selected problem sets, which increased during school closures, highlights the important role of teachers within online learning environments—especially when teachers and students do not see each other in school.

Importantly, students were exposed to the same learning content (the three different mathematical topics fractions, percentages and interest, and linear equations), independent of the assignment strategy (i.e., single problem sets, books, and self-selected problem sets). Thus, overall students from different assignment strategies should have been exposed to problem sets of the same difficulty. To investigate the robustness of our results across different book topics, we ran further control analyzes replicating our original analyses for each book topic separately (see S1 Text ). These results showed that the obtained pattern of results was replicated for each book. Thus, differences in difficulty between books did not influence the effect of assignment strategy. Therefore, we are confident our results are not biased by differences in difficulty between book topics.

Another important avenue for future investigation is the effect of teacher-student interactions within online learning environments on students’ performance. For instance, teacher-student interactions within classrooms have been reported as a key source eliciting mathematical anxiety [ 77 , 78 ] which in turn was reported to reduce mathematical performance [ 79 – 82 ]. In contrast, our results suggest that students who got problem sets assigned by their teachers via an online learning platform outperformed students who studied on their own (i.e., with no teacher-student interaction). Thus, future research may investigate whether teacher-student interactions within online learning environments may elicit less math anxiety such that online teacher-student interactions may reduce negative aspects such as mathematical anxiety.

When interpreting the results of the present study some limitations need to be considered. First, it should be acknowledged that performance improvements observed in this study may also be influenced by other factors such as an increase in the overall usage of learning software during lockdowns and school closures [ 61 , 83 , 84 ]. However, control analyses indicated that the mere degree of software usage did not explain observed differences between time windows. Aside from software usage, one might argue that students exhibited different degrees of motivation in the different time windows, leading to differences in performance [ 73 – 75 ]. The latter may result from the fact that online exercises were the only exercises available during school closures. Other potentially confounding factors include increased support from family members when studying at home, as well as a higher focus when learning from home [ 4 , 5 ]. Such motivational factors might indeed have driven the main effect of time window. However, even if such motivational factors are at play, one would not necessarily expect these to differentially affect the interaction between time window and problem set assignment. That is, we observed selective performance improvements when single problem sets were assigned. Third, our results stem from students from 10–16 years of age and are thus limited to this specific age range. An interesting future avenue would be to test the generalizability of our results to older or younger cohorts of students who studied with an online learning environment during COVID-19 related school closures.

Another limitation of this study is that the data do not include information on why some teachers assigned single problem sets whereas others assigned problem sets in bundles. The learning environment offers both assignment policies and the data suggest that both policies are used by teachers. Nevertheless, assigning single problem set leads to higher completion rates and higher probabilities of gaining a coin (60% accuracy) or a star (100% accuracy) within the online learning environment. Importantly, this effect was already present before school closures. During both school closures, the positive effect of assigning single problem sets seemed to be increased. The present findings thus point toward a beneficial way of how to assign mathematical problem sets to students from classes 4–10 (age range 10–16) best—in small bits rather than big bundles. These results are in line with previous studies on spaced learning [ 44 – 49 ] which repeatedly showed that assigning smaller bits of learning content into smaller bits led to increased learning outcomes compared to massed learning (few but learning sessions).

Working with data obtained from online learning environments may not always allow to control for specific influencing variables such as socioeconomic status, gender, or age as these may be considered sensitive information and may not be shared. Additionally, these data may typically not incorporate specific aspects such as how teachers incentivize their students as well as additional information on students’ general traits and abilities (e.g., self-regulated learning abilities), which were repeatedly observed to influence students’ performance in general [ 85 ], and specifically during school closures [ 86 , 87 ]. For instance, the reported results of differential outcomes depending on the assignment strategy may be explained by self-regulated learning. In particular, the decision-load for bulk assignments as well as when students self-selected problem sets (note that students self-select problem sets from a book which is the same as a bulk of assignments) is much higher, so students may struggle to make the right choice where to begin when learning in a self-regulated way. Conversely, when provided with particular small problem sets, students can more easily motivate themselves to perform them because they don’t have to make as many decisions about what to start with. Nevertheless, analyzing these data does allow to evaluate average trends estimated from student samples which are typically considerably larger than sample sizes from experimental studies and in the present case worked on curricular-based mathematical problem sets. As such, investigating such large-scale data from online learning environments (for other online learning environments see: [ 5 , 7 , 88 , 89 ]) comes with a trade-off between not being able to control for covariates typically assessed in experimental research (e.g., age and gender) and investigating several subgroups in the population (e.g., males vs. females or groups of differing socioeconomic background), and the benefit of being able to examine data from thousands of students providing information on general trends in the (student) population (classes 4–10; age-range: 10–16).

In the present study, we evaluated the influence of assignment policy on students’ (classes 4–19; age-range 10–16) performance in an online learning environment for mathematics during school closures. Results suggest performance improvements during school closures due to the COVID-19 pandemic, relative to the years before. Importantly, however, our results also suggest that the degree of students’ improvements was specified by the way students were assigned problem sets: We observed significant performance benefits if students were assigned problem sets one by one as compared to in entire books or having students self-select problem sets. These results indicated that assigning smaller bits of information seemingly was a beneficial strategy during school closures during the pandemic. The results also highlight the importance of teachers for online learning environments—a finding that encourages further research on the exact role of teacher behavior within online learning environments.

Supporting information

https://doi.org/10.1371/journal.pone.0284868.s001

Acknowledgments

We thank Bettermarks for sharing their data with us. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of Bettermarks. Bettermarks had no role in study design and data analysis, decision to publish, or preparation of the manuscript.

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ORIGINAL RESEARCH article

Students' achievement and homework assignment strategies.

• 1 Department of Education Sciences, University of Oviedo, Oviedo, Spain
• 2 Department of Education, Principality of Asturias Government, Oviedo, Spain
• 3 Department of Psychology, University of Oviedo, Oviedo, Spain

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 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.

Table 1. Descriptive statistics and Pearson correlation matrix between the variables .

Table 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.

Table 2. Distribution of the variance in the null model .

Table 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.

Table 3. Multilevel models for prediction of achievement in four subjects .

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 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 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.

Figure 1. Relationship between school homework volume and differences in time needed by students to complete homework .

Figure 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.

Figure 2. 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 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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Keywords: homework time, equity, compulsory secondary education, hierarchical modeling, adolescents

Citation: Fernández-Alonso R, Álvarez-Díaz M, Suárez-Álvarez J and Muñiz J (2017) Students' Achievement and Homework Assignment Strategies. Front. Psychol . 8:286. doi: 10.3389/fpsyg.2017.00286

Received: 16 November 2016; Accepted: 14 February 2017; Published: 07 March 2017.

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Copyright © 2017 Fernández-Alonso, Álvarez-Díaz, Suárez-Álvarez and Muñiz. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Javier Suárez-Álvarez, [email protected]

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• v.14(4); 2021 Dec

Effect of Assignment Choice on Student Academic Performance in an Online Class

Hannah macnaul.

1 Department of Educational Psychology, University of Texas at San Antonio, San Antonio, TX 78207 USA

2 Department of Child and Family Studies, University of South Florida, Tampa, FL USA

Rachel Garcia

Catia cividini-motta, ian thacker, associated data.

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.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40617-021-00566-8.

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 ​ Table1 1 and Fig. ​ Fig.1). 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 ​ Table1. 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 quiz scores for each module

Note. Data were excluded for Cohort 1, Module (M) 1 as several students completed both assignments (intended to be choice condition). C = choice; FC = flash cards; NA = no assignment; NC = no choice; SG = study guide.

a The start of the COVID-19 pandemic, March 2020.

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. 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.

(DOCX 14 kb)

Declarations

We have no conflicts of interest to disclose.

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

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 .

Publisher’s Note

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

• Arendt A, Trego T, Allred J. Students reach beyond expectations with cafeteria style grading. Journal of Applied Research in Higher Education. 2016; 8 (1):2–17. doi: 10.1108/jarhe-03-2014-0048. [ CrossRef ] [ Google Scholar ]
• 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. Assessing the use of flexible assessment. Assessment and Evaluation in Higher Education. 2001; 26 (6):539–549. doi: 10.1080/02602930120093878. [ CrossRef ] [ Google Scholar ]
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• Hanewicz C, Platt A, Arendt A. Creating a learner-centered teaching environment using student choice in assignments. Distance Education. 2017; 38 (3):273–287. doi: 10.1080/01587919.2017.1369349. [ CrossRef ] [ Google Scholar ]
• Jopp, R., & Cohen, J. (2020). Choose your own assessment—Assessment choice for students in online higher education. Teaching in Higher Education . 10.1080/13562517.2020.1742680. Advance online publication.
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Unlocking Academic Success: The Top 12 Benefits of Assignments

Is it possible that you would reach the end of your degree but didn't attempt any assignment in your academic career? Not really. Well, the importance of assignments is not hidden from us. We all are aware of its significance. Completing assignments is a daunting task, but do you have any idea about their benefits? If not, then keep reading this article. We'll explain the benefits of assignments in detail and how to finish them fast. Before moving forward, let's have a brief overview of what an assignment is and its purpose.

What is an assignment? 

Assignments play an important part in the learning process of students. It is a well known assessment method for teachers as well. Additionally, it is not only for students but also for professors. With the help of assignments, professors can evaluate the skills, expertise, and knowledge of students. It also helps teachers assess whether or not pupils have met the learning objectives. Moreover, it allows them to gauge how much students have learned from their lessons. 

In education, an "assignment" means a piece of schoolwork that teachers give to students. It provides a range of opportunities to practice, learn, and show what you've learned. When teachers assign assignments, they provide their students with a summary of the knowledge they have learned. Additionally, they assess whether students have understood the acquired knowledge. If not, what concerns do they may have?  

Purpose of Giving Assignments to Students

Teachers give homework to help students in their learning. Doing homework shows they are good at it, responsible, and can manage their time wisely. College professors also give homework to check how well students understand what they learned. Clarity is required when planning an assignment on a number of issues. As a result, the following factors are taken into account by your teacher when creating the structure for your assignment.

• Will it be an individual or group assignment?
• How can it be made more effective for students?
• Should I combine two approaches for this project?
• Do I need to observe how students are working on the assignment? Or should I check it once they've finished it?
• What standards must I follow when evaluating this assignment?

What are the aspects of assignment evaluation?

Instructors usually follow these three aspects when evaluating an assignment.

The assignment and the method used to evaluate the results are in line with the learning objectives.

Reliability

Teachers draw distinctions and assign grades based on the outcomes. The score is consistently calculated based on the predefined parameters. It guarantees that the grades are evaluated in a meaningful way.

Objectivity

An assignment's goal should be obvious. The primary goal of this assignment is to teach students what they will learn. Also, how to finish that assignment. Teachers need to specify what they expect from the assignment and how they are going to evaluate it. 

Types of Writing Assignments

There are different types of writings that teachers assign to students at the college or university level. Some of writing assignment types are:

It presents the author's viewpoint on a subject with supporting data and may also argue its case. The essay structure consists of three main components: introduction, body paragraphs, and conclusion. Essays are of different types, such as analytical essays, compare and contrast essays, and persuasive essays. You can also buy essays from an online writing service. 

A report offers information about an issue in a clear and organized manner. You may have learned this information through reading, research, experiments, and measurements in the field or lab. You might also have gained it from your personal experiences. Additionally, reports have different structures depending on the subject or discipline. The basic structure of the report consists of an abstract, introduction, methodology, findings, discussion, conclusion, and appendices. 

Literature reviews

A literature review may be assigned as a standalone assignment. In the literature review, the goal is to summarize the key research relating to your topic. Alternatively, it might be a section of a lengthy project, like a research report or thesis. The goal would be to justify the need for more research on the topic you have selected.

Annotated bibliographies

A literature review or essay synthesizes various sources and incorporates them into a single discussion about a topic. In contrast, an annotated bibliography evaluates and summarizes each reading independently. Each reading is typically presented alphabetically based on the first letter of the lead author's surname. It is difficult to generate an annotated bibliography. But you can get expert help by hiring an online annotated bibliography writing service . 

Case studies

In general, a case study requires the integration of theory and practice. This helps you connect theoretical ideas to real professional or practical situations. A case may be a person, any event, idea, etc. You are analyzing the case by mapping it against a theoretical explanation to understand and see the big picture – What has happened? It may take the form of a report or an essay. Consult your lecturer or tutor and review the assignment question.

Research paper

The research paper starts with a topic and your research question. Add data from trustworthy sites and properly cite those sources. Moreover, add a claim or argument as your thesis statement. If you don't know how to write a research paper , you can check our latest guide.

Response paper

In the response paper, discuss what you've read or learned about a particular problem or subject. Evaluate concepts about other readings, talks, or debates. Write in a combination of formal and informal styles. (make sure to consult your professor's guidelines)

Top 12 Benefits of Assignments

For hard working students, assignments can offer many benefits once they get used to them. They help you get the grades you want and show what you have learned in your classes. You'll see the benefits of assignments more clearly when you learn about their different types and what your teacher expects. Assignments are an absolute way to do well in your classes.

We have already talked about what an assignment is and its purpose. Let's explore the impact of homework assignment on students' learning.

1. Enhance the student's knowledge

Teachers assign assignments on a variety of subjects and topics. This will help the students to gain knowledge when they work on different kinds of topics. It is one of the best benefits that students receive from assignments. They are also introduced to significant ideas and insightful information.

Suppose your assignment topic is too complex. You have to spend extra time and effort to conduct detailed research to understand the topic. This way, you will not only be able to complete your assignment. But also gain a lot of new information.

There can be a lot of pressure to memorize information exactly. This pressure may lead to simply repeating it when studying for an exam. Students find it challenging to truly grasp the concepts covered in their courses. This results in a lack of deep understanding. On the other hand, when you undertake a challenging assignment, you'll be applying knowledge to real world issues. These issues often have multiple possible solutions. You'll find that developing this kind of thinking and improving your assignment writing skills will help you throughout the course and the rest of your academic career.

2. Improve student's problem solving skills

Another benefit of assignments is when students work on complex projects; their analytical and critical thinking skills are also enhanced. This is an extremely useful skill for students to possess. Since it will help them in their academic and professional journey. We continue to learn from this process regardless of our age.

A great technique to master your course material is to challenge yourself. Give yourself a complex problem to solve and strive to find a solution. Similar to the benefits of homework , you can only improve at something by putting it into practice and giving it a lot of thought. We are always working on these analytical and problem solving skills, and going back to school will force you to develop them even more. 

3. Boost your writing caliber

We frequently find ourselves with a lot on our minds but unable to properly and clearly explain it in front of the audience. Assignments help us in improving our writing skills. When you have a habit of writing, then you can communicate easily. Your writing skills will improve because your academic task requires you to write. Another benefit of assignments is that they assist you in writing concisely and clearly.

4. Help to think under pressure

Sometimes, you might be assigned a very difficult assignment that requires a lot of knowledge, and you are not familiar with it. Handling these complex tasks assists you in persevering when you don't have enough information. It also helps you to grow confidence in your skills to find the right solution.

Additionally, all students and professionals need to learn how to think under pressure. The assignment gives you the opportunity to do so. Since you probably only have a few days to finish the assignment. You'll need to not only manage your busy schedule to finish it. But also squeeze in a lot of learning and application of what you've learned. Possessing this ability will be beneficial because it will enable you to think clearly under pressure, which will help you succeed in school and in your career.

5. Help in boosting grades

There is more pressure to perform well on exams when a course has few exams that make up for an important part of your final grade. Smaller assignments that account for a smaller portion of your final grade mean that even if you don't perform well on one of them, you will still have more chances to improve your grade.

You can feel more at ease knowing that your grades are divided in this manner. This provides you with multiple chances to work towards a higher grade. Many students prefer smaller assessments. These relieve them of worrying about a single test significantly impacting their final grade.

6. Build time management skills

A study conducted among students revealed that students who completed more assignments performed better in their overall academics. They also achieved higher scores in specific subjects.

Due to these tasks, students gain more time management skills, which further empowers them. They learn the ability to allocate their time between assigned tasks and prioritized activities. They are aware of what needs to be done first. How to solve problems faster, and how to turn in their work ahead of schedule. Furthermore, this practice teaches them to use their time wisely.   

7. Enhance organizing and planning skills

Completing an assignment requires thoughtful planning. Students' organizational skills are improved through the information search, sorting, and use of relevant data. Following that, students will be able to plan out when and how to complete their assigned work. Attempting assignments allows them to effectively handle their learning habits. They also help them to apply their knowledge wisely to improve their academic performance.  

8. Understand how to apply in real life scenarios

Applying theoretical concepts to real world situations also gets easier when one learns how to write theoretical assignments. This enables them to be prepared to deal with any problems that arise in the future.

9. Boosts your knowledge of technical subjects and ideas

When a subject is taught in a classroom environment, it's normal for students to not understand it. They are forced to spend more time comprehending and finishing their work when they are assigned assignments on those subjects, though.

This enables them to respond to those questions with ease and proficiency. Regardless of a concept's technicality, you'll gain a strong command over it. This happens when you write multiple articles on the same topic or idea.

10. Improve research skills

Doing homework and assignments also helps students get better at researching. When a professor assigns any assignment, students perform thorough research on different topics. This allows them to learn the ability to find useful information and sort it accordingly. Their professional life is positively impacted, and their academic performance is improved by this habit.

11. Learn the art of tasks prioritizing

When handling a lot of assignments, you will learn to prioritize the task based on its importance. It is a crucial skill that is needed in professional life. Prioritizing your work will help you to complete all your tasks on time. You will be able to meet the deadlines.

12. Making a personal study space

You can get help from your colleagues and online resources. But the task of implementing that knowledge is your own. This is exactly what you need to understand concepts.

As you work on your assignments, you can create a relaxing study space that increases productivity. You'll be able to create a unique working style by doing this. In addition, you can focus on creativity, productivity, learning, and pursuing interests.

Of course, everything has a negative aspect, even though there are definite advantages. Sometimes, students may question the true value of assignments. They wonder if there are any restrictions on this particular grading scheme. Students usually wonder this when they are having difficulty with their coursework or with specific concepts. These carry significant burdens. They can be stressful for students struggling with course material.

However, this belief has a reason. Even experts can't agree on the best way to evaluate a student's performance in a course. This sparks a lot of discussion.

How to finish assignments fast?

Firstly, make a plan of what steps you will cover in your assignment. It includes how much time is required to complete the assignment. Then, list out all the tasks that you will do in your assignment. Identify what you need to complete this assignment, like a calculator, books, paper, and pen. Find a relaxing and quiet place to work without any distractions. Switch off your phone. Have some light snacks and water. Take quick breaks between assignment tasks. When you're done with the assignment, reward yourself.

Concluding Remarks

Now, you have a clear understanding of what the assignment means and its importance. And how it is beneficial for the student's academic career. Would you like additional information? Or do you simply not have the time to complete it? Stop worrying! You can find the solution at Nerdpapers, all under one roof. Our professionals have years of experience. So, if a student gets stuck on a project or assignment, they can take a variety of actions to help them finish it on time. Not only can our native experts produce high quality assignments. But they can also help you achieve good grades at reasonable costs. Therefore, hire subject related experts for appropriate guidance and assistance rather than compromising your grades. Whether you are a college, university, or high school student, there are several benefits of assignment writing.

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Persuasive essay topics – how to choose one for you, how to write a persuasive essay- expert tips.

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THE IMPACT OF HOMEWORK TIME ON STUDENT LEARNING

This study investigated the impact of homework time on student achievement. The participants in this study included 30 students, 15 males and 15 females, in secondary school class. Students in this study completed two units with homework and two units without homework. Data was collected through student and parent surveys, homework completion checklists, quiz and test scores, and student interviews. Results indicated that homework had a minimal effect on student achievement and that parental involvement had a positive impact on homework completion rates. It is important to assign purposeful homework assignments and to monitor the amount of homework assigned to ensure its quality and completion.

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Farhana Quddus

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Homework stress is a common complaint among middle school and high school students and their parents worldwide. Although complex, the challenges surrounding homework stress affect everyone at one time or another. This literature review examines the history behind the decades long “great homework debate,” reasons for homework stress, and parental involvement in the homework process. The literature supports ideas that parental styles and socioeconomic status are two main influencers determining whether or not parental involvement is helpful for a student’s stress levels and academic success. Students need to learn how to solve problems and manage their time to be successful and are looking to parents as successful models of these behaviors. More research is needed to determine if interventions such as meditation and yoga, or the involvement of grandparents or other older adults in the community may have positive impact on student stress levels.

International Journal of English and Education

Denis A Tan

Homework or assignment is widely known as an educational activity, which primary purpose is to help the students improve their performances however some studies showed that assignment has a negative impact on students’ social lives and more assignment increases stress level and physical problems. In the Philippines, a “No Homework Policy” during weekends for all student levels was issued by President Rodrigo R. Duterte. This study explores the possible effect of having and not having assignments on weekends on the transmuted mean scores and performance of high school students. The study was conducted at Central Mindanao University Laboratory High School (CMULHS), in Maramag, Bukidnon using two sections of Grade 11 students. The data was treated using the descriptive statistics. ANCOVA was used to determine if a significant difference exist. Result of the study reveals that class with assignments had a higher transmuted mean scores as compared to those without assignments. An increased performance from midterm to final term was noted in the with assignment group however, there is no significant difference in the performance of the students with and without assignments.

This review specifically focuses on the correlations between various parent strategies and student achievements in compulsory education. Therefore, Hoover-Dempsey's framework on parental involvement in homework will be updated with more recent findings from the international scientific literature. When parents facilitate, structure or emotionally support the homework process and, as such, are not actively involved in assisting in homework tasks, then the literature indicates indecisive or negative results. However, when parents are directly involved in assisting their children during homework tasks, then positive correlations were found throughout the literature, in particular when parents engage in meta-strategies or support the child's understanding of homework. While policy is primarily focused on providing instruments for parents to facilitate or structure the homework process, the current review suggests that parents need to be better informed on specific strategies that accommodate the student's need when assisting in homework tasks in order to improve achievements.

iipinge joseph

Hằng Nguyen

Procedia - Social and Behavioral Sciences

Dr. Ahmed A A M Raba' , khaled Dweikat

Although homework has existed for a long time in education as a form of student independent practice of material covered in class, it continues to be an issue of debate. There is considerable debate over the effectiveness of homework among researchers, administrators, teachers, parents, and students. Therefore, the present study aimed at exploring the influence of homework assignments on the high basic school students' achievement in public schools in Nablus directorate from the teachers' perspectives. It also aimed to check the role of gender, qualification and experience in the influence of homework assignments on the high basic school students' achievement. To achieve these aims, the researchers used a 21-item questionnaire which was distributed among a random sample of 30 male and female teachers during the first semester of the scholastic year 2014 – 2015. The results of the study revealed that the total score of the teachers' perspectives of the influence of homework assignments on students' achievement was acceptable with a total percent of 61.40%. In the light of the study findings, the researcher recommended teachers to give regular homework assignments of high quality rather than quantity. Another recommendation is given to the school principals to cooperate with teachers in this regard. One more recommendation is directed to the curriculum centre to include homework assignments of high quality after each unit.

prudence jamal

The present research aims to explore the effect of parental involvement in the academic achievement of their children. The research was conducted in Allama Iqbal Town, Lahore city. A total of 150 students (boys and girls) of 9 th class of secondary schools (public and private) were taken as respondents. Four schools were selected through simple random sampling which include one boy and one girl from each of the public and private schools categories for equal representation of both boy and girl students in the sample frame of present study. Survey questionnaire was used as a tool for data collection. After the analysis of data, it was found that parental involvement has significance effect in better academic performance of their children. The present research has proved that parental involvement enhanced the academic achievements of their children.

Haruni Machumu

The study explores the extent of parental involvement in school activities and its relationship with schooling process among primary school children. Parental involvement questionnaire and children academic questionnaire with two rating scales each were administered to 288 children and 125 teachers from six primary schools. The study found a positive significant relationship between parental involvement in school activities and children’s academic standing (r =.766, p<.01) and the provision of key school items related to schooling outcomes (r =.733, p<.01) respectively. Parents-teacher conferences and parent-teacher face-to-face contacts were perceived to be desirable modes of communications that impacts children’s school academic achievement.

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What Happens to Biden’s Student Loan Repayment Plan Now?

More than eight million borrowers are enrolled in the income-driven plan known as SAVE. The Education Department is assessing the rulings.

By Tara Siegel Bernard

President Biden’s new student loan repayment plan was hobbled on Monday after two federal judges in Kansas and Missouri issued separate rulings that temporarily blocked some of the plan’s benefits, leaving questions about its fate.

The preliminary injunctions, which suspend parts of the program known as SAVE, leave millions of borrowers in limbo until lawsuits filed by two groups of Republican-led states challenging the legality of the plan are decided.

That means the Biden administration cannot reduce borrowers’ monthly bills by as much as half starting July 1, as had been scheduled, and it must pause debt forgiveness to SAVE enrollees. The administration has canceled $5.5 billion in debt for more than 414,000 borrowers through the plan, which opened in August.

If you’re among the eight million borrowers making payments through SAVE — the Saving on a Valuable Education plan — you probably have many questions. Here’s what we know so far, though the Education Department has yet to release its official guidance.

Let’s back up for a minute. What does SAVE do?

Like the income-driven repayment plans that came before it, the SAVE program ties borrowers’ monthly payments to their income and household size. After payments are made for a certain period of years, generally 20 or 25, any remaining debt is canceled.

But the SAVE plan — which replaced the Revised Pay as You Earn program, or REPAYE — is more generous than its predecessor plans in several ways.

Ask us your questions about the SAVE student loan repayment plan.

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For students, summer means beaches, barbecues and no homework assignments. However, experts say the warm months off from school can be harmful for kids' success. NBC News' Zinhle Essamuah takes a deeper look into the "summer slide" responsible for the decline of test scores and a growing problem amplified by the pandemic. June 26, 2024

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Student-loan borrowers who were set to get debt cancellation or lower payments through Biden's new repayment plan won't get it — for now. Here's what you need to know.

• Two federal judges blocked parts of the SAVE income-driven, student-loan repayment plan on Monday.
• The rulings mean that student-loan forgiveness and lower payments set to begin in July cannot move forward.
• The Justice Department is appealing the rulings, and the courts have yet to make final decisions. 

Legal challenges against President Joe Biden's student-debt relief efforts are back — and the latest rulings are bad news for his new repayment plan.

On Monday evening, district courts in Kansas and Missouri handed down rulings blocking parts of the new SAVE income-driven repayment plan , first introduced last summer with the goal of giving borrowers more affordable payments and a shorter timeline for loan forgiveness.

The first lawsuit was filed in March in Kansas by 11 GOP state attorneys general, and the second was filed in April in Missouri by seven GOP state attorneys general. In both cases, the plaintiffs requested that the courts block the SAVE plan and the loan forgiveness that comes with it, arguing that the relief is beyond the administration's authority.

Monday's district court rulings were different, but both dealt blows to the SAVE plan. Kansas Judge Daniel Crabtree ruled that new provisions through SAVE set to go into effect July 1, like lower monthly payments, cannot be implemented as the legal process progresses. Missouri Judge John Ross ruled that the plan's provision to cancel student debt for borrowers with original balances of $12,000 or lower who made as few as 10 years of qualifying is now blocked, as well.

Education Secretary Miguel Cardona condemned the rulings on Monday, saying in a statement that "the Department of Justice will continue to vigorously defend the SAVE Plan."

"Republican elected officials and special interests sued to block their own constituents from being able to benefit from this plan – even though the Department has relied on the authority under the Higher Education Act three times over the last 30 years to implement income-driven repayment plans," Cardona said.

"While we continue to review these rulings, the SAVE plan still means lower monthly payments for millions of borrowers - including more than 4 million borrowers who owe no payments at all, and protections for borrowers facing runaway interest when they are making their monthly payments," he added.

Related stories

Here's what borrowers should know about the rulings.

First ruling: No new payment reforms

Student-loan borrowers who have already enrolled in SAVE can continue making the payments the plan calculated for them. However, the new provisions set to go into effect July 1 — including cutting undergraduate borrowers' payments in half and forgiveness credit for period of deferment of forbearance — are halted.

Here's why: Kansas' Crabtree ruled , in part, in favor of the attorneys general, and he explained in his ruling that the SAVE plan's monthly payment cap and shortening of the payment period for forgiveness "overreach any generosity Congress has authorized before."

However, Crabtree ruled to preserve the provisions of SAVE that have already gone into effect because the plaintiffs failed to adequately show how they suffered harm from parts of the plan already in place. For example, the Education Department outlined in June 2023 its intention to cap monthly payments and announced the shorter timeline to forgiveness a month in advance, leaving the attorneys general with time to challenge the plan earlier.

"All of this is to ask why: if these parts of the SAVE Plan promised an irreparable harm to plaintiffs, why didn't they move to enjoin the SAVE Plan before they took effect?" Crabtree wrote.

However, with regards to the new SAVE provisions set to go into effect July 1, Crabtree ruled that the plaintiffs succeeded in showing harm because there was no delay in challenging the plan's unimplemented provisions, and any forthcoming relief would be irreversible.

So rather than reversing or altering any of the provisions through SAVE already implemented, Crabtree decided to halt any new measures that have yet to be implemented until the court makes a final decision.

Second ruling: No student-loan forgiveness

While thousands of borrowers have already received student-loan forgiveness through the SAVE provision, which cancels debt for borrowers with original balances of $12,000 or less, no more borrowers will be able to partake in that relief for now.

Missouri's Ross handed down a different ruling regarding SAVE. He first said that Missouri's argument that the plan would harm student-loan company MOHELA — based in Missouri — due to lost revenue has standing, given it was the same conclusion the Supreme Court reached when it struck down Biden's first attempt at broad debt relief last summer.

With regards to the fate of SAVE, Ross decided that while already implemented provisions of SAVE can remain, any future student-loan forgiveness through the plan is blocked. He wrote that Congress did not account for the scale of loan forgiveness under SAVE, and as a result, the attorneys general have "a 'fair chance' of success on the merits on their claim that the Secretary has overstepped its authority by promulgating a loan forgiveness provision as part of the SAVE program."

He also said that even without allowing student-loan forgiveness, the other provisions, like lower payments and limited interest accrual, will still provide relief to borrowers. Since the attorneys general did not adequately argue why the other provisions should be blocked, Crabtree said he would only place a preliminary injunction on the debt cancellation.

Cardona said on Tuesday that the Justice Department will appeal the rulings.

White House Press Secretary Karine Jean-Pierre said in a statement that the Education Department will "continue to enroll more Americans in SAVE and help more students and borrowers access the benefits of the plan that remain available, including $0 payments for anyone making $16 an hour or less, lower monthly payments for millions more borrowers, and protecting borrowers from runaway interest if they are making their monthly payments."

Watch: Why student loans aren't canceled, and what Biden's going to do about it

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Education | 7 Maryland education laws taking effect July 1

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During a legislative session filled with back-and-forth budget discussions, the collapse of the Francis Scott Key Bridge and lawmakers attempting to push their bills through before midnight on April 8, several education bills passed .

A number of those will take effect on Monday. Here are some to look out for and some that did not survive the busy 90-day session.

The state budget

Although Moore’s $63.1 billion budget passed, negotiations were down to the wire during the last two weeks of the legislative session while the state faced the Key Bridge collapse. The Maryland House of Delegates wanted to pass an extensive revenue package, but the Senate remained steadfast in avoiding tax and fee increases.

Lawmakers reached a compromise with a spending plan that could raise up to $91 million for education this fiscal year. Revenue from new taxes on tobacco products will go toward funding the Blueprint for Maryland’s Future, though with increased taxes, tobacco use is expected to slow.

The Blueprint remains fully funded until fiscal year 2027, when general funds will then be tapped, causing the projected deficit to increase. Lawmakers worry about the Blueprint’s financial picture but have pushed conversations regarding solutions down the road.

The budget for fiscal year 2025 allocates $10.1 billion to K-12 education.

Maryland Higher Education Commission

The Maryland Higher Education Commission’s approval process for proposed new programs at colleges and universities will be updated and streamlined under a new law. House Bill 1244, cross-filed with Senate Bill 1022, was built from a list of recommendations made by the Maryland Program Approval Process Workgroup in January to add more transparency to the process.

The law requires the formation of a State Plan for Higher Education, which includes goals and priorities for postsecondary education and workforce development needs. According to Del. Stephanie Smith, a Baltimore City Democrat and a sponsor of the bill, other notable requirements are a collaborative fund to reduce institutional financial burdens and letters of intent to create collaboration between institutions.

A mandatory review process for newly approved academic programs that were subject to objection by a historically Black college or university is also established under the law.

The mandatory review will look for “any harm to Maryland HBCUs who have historically contended with unnecessarily duplicative academic programs being approved at non-HBCUs,” Smith said in an email.

This came after the state agreed to a $577 million settlement with its HBCUs in 2021 for putting them at a disadvantage when competing for students after authorizing predominately white institutions nearby to duplicate academic programs.

Even after the settlement, some institutes have been accused of duplicating HBCU programs . Last year, Towson University withdrew a proposal for a doctoral business program that was similar to one at Morgan State University, which is historically Black. The higher education commission also denied the creation of a physical therapy doctoral program at the Johns Hopkins University and Stevenson University because of their similarity to existing programs at the University of Maryland Eastern Shore, a historically Black university, and the University of Maryland, Baltimore.

Despite a last-minute issue that prompted top negotiators to send a letter to the commission, “This bill was enthusiastically signed by Governor Moore and is being implemented, as passed, by the Maryland Higher Education Commission,” Smith said.

Community schools

A law altering requirements for community schools created by the Blueprint passed, though not without changes. Any school that receives Concentration of Poverty Grants is a community school. Under the Blueprint, a school district can only use grant money for administrative needs if it has at least 40 community schools.

Senate Bill 161 and House Bill 200 would have made adjustments so that districts under a 40-school threshold could use 10% of grant funds for administrative needs and certain other costs. But the language was striken from the law, and instead, it focuses on more technical changes.

Legacy preference in college admissions

After the U.S. Supreme Court ruled affirmative action unconstitutional , Maryland passed a law with bipartisan support to ban legacy and donor preference in admission for institutions that receive state funding.

Del. Jazz Lewis, a Prince George’s County Democrat who sponsored the bill, said it was a great start, especially as the most diverse state on the East Coast.

“Getting a quality higher education can change not just your life but your family,” Lewis said in an interview. “And we should make sure that whatever happens federally, that in Maryland, access is equitable for all those who are qualified and doing what they need to do to prepare themselves for that next step.”

Many colleges and universities across the country have already banned legacy preference, as it disproportionately benefits white students. Similar legislation is also being considered in other states to increase diversity on campuses.

Institutions in Maryland can still ask applicants to provide information about their relationships with alumni for data collection under the law.

Refund for mental health withdrawal from college

Students at Maryland’s public institutions of higher education can receive a refund for tuition and fees paid during a semester if they withdraw due to certain extenuating circumstances, such as mental health.

House Bill 539 and Senate Bill 567, also known as the Cameron Carden Act of 2024, require the institutions to develop a policy to allow students to withdraw if their circumstance interferes with their ability to complete the education and be refunded for the semester.

Cameron Carden was a student at Salisbury University whose mental health became debilitating after graffiti with racist remarks threatening violence appeared on campus, according to his testimony. After seeking help, he decided to leave school, learning he would not be refunded as if he had left for a physical injury. He advocated for the law so others would not have to worry about lost finances while fearing for their safety on campus.

The law makes mental health and wellness a valid circumstance for withdrawing from the institution and receiving a refund, along with illness, injury and hospitalization.

Contraceptive access for community college students

In last year’s session, a bill sponsored by former Democratic Sen. Ariana Kelly passed requiring public four-year colleges and universities to develop a plan to provide students with the full range of reproductive care. This year, Sen. Cheryl Kagan, a Montgomery County Democrat, joined Kelly to sponsor a bill that includes community colleges in the language.

The bill was signed into law in April and goes into effect Monday, mandating community colleges to create and implement a plan to provide students with over-the-counter contraceptives by Aug. 1, 2025.

“It’s about equitable access to contraception,” Kagan said in an interview. “And whether a student is at a four-year college or university or at a program at a community college, she or he should be able to access contraception.”

The law takes effect just a few weeks after the two-year anniversary of the overturning of Roe v. Wade, barring the federal right to abortion. “[Maryland] can do better by helping people avoid pregnancy when possible,” Kagan said.

Because community colleges don’t have residence halls or health centers with wide-ranging hours like a four-year institution, schools can provide access to over-the-counter contraception through vending machines, retail stores on campus, health centers, student centers or other methods. The law is about convenience, Kagan said, as many students are balancing other commitments.

“I think we just have the obligation to make it easy to get contraception,” Kagan said. “And so they’re not faced with the decision about abortion, because they’ve made their choice to try not to get pregnant.”

Trauma-sensitive active shooter drills

Active shooter drills in schools across the state will now be trauma-sensitive under a new law. The legislation bans loud sounds that mimic gunfire or explosions, individuals role-playing as active shooters or victims and other similar activities that may be traumatic for students and staff.

Parents must be notified of the drills or training, which are required to be age-appropriate, at the beginning of each school year. By July 1, 2025, parents will begin to receive model content on the state’s laws for storing firearms as well.

The law fosters collaboration between the Maryland Center for School Safety and an institution of higher education or federal research agency to study active shooter drills’ effectiveness and psychological impact. The Center must find the best practices for drills and training by Oct. 1, 2024. Before the 2025 school year, the State Department of Education will review the Center’s best practices each year and make any necessary updates.

What didn’t pass?

Bills codifying funding for the Broadening Options and Opportunities for Students Today, or “BOOST,” guaranteeing admission for qualifying students to certain universities did not beat the legislative clock.

BOOST, the state’s school choice program, provides scholarships for students who qualify for USDA free and reduced-price lunch to attend private schools. Republican lawmakers pushed for a bill that would mandate funding for the program from the state’s general fund each year starting in fiscal year 2026.

While the bill failed, BOOST is still funded in the budget for this fiscal year, with $9 million allocated to the program.

Sen. Malcolm Augustine sponsored a bill attempting to guarantee admission to schools in the University System of Maryland, Morgan State University and St. Mary’s College of Maryland for students who graduated in the top 10% of their class from a Maryland public or private high school.

The Democrat from Prince George’s County was motivated to write the legislation after the Supreme Court declared affirmative action unconstitutional. The bill was met with opposition from University of Maryland, College Park and Morgan State admissions officers who said it wouldn’t help increase diversity. The bill didn’t make it out of the committee hearing stage.

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