Differential use and effectiveness of practice testing: Who benefits and who engages?
Open accessAbstract
Practice testing is an effective learning strategy, yet low-achieving students often underutilize it despite its benefits. This study investigates who benefits from and who engages in practice testing within an authentic educational setting. We collected data from 325 undergraduates (56 % self-identified as female) in a gateway mathematics course, combining survey responses, practice testing records, and exam performance (points, grades, passing). Employing regressions and prediction rule ensembles, we analyzed a comprehensive set of predictors (prior/current achievement, motivational beliefs, personality, and volitional factors). Increased practice testing was linked to improved exam performance, especially for students with moderate-to-high current achievement. Students with lower prior achievement were less likely to test themselves and required more practice testing attempts to achieve similar exam performance improvements. Predictors of practice testing included prior achievement, intrinsic value, and mastery goals. Therefore, integrating low-stakes testing with motivation-supportive interventions may reduce performance gaps and promote equity in higher education. • Practice testing benefits all students, especially higher-achieving and more motivated ones. • Low-achieving students require more attempts to achieve similar learning gains to higher-achieving peers. • Prior achievement and motivation predict students' use of practice testing and moderates its effect on exam performance. This study reveals a critical disparity in educational practices: students who are most likely to benefit from self-testing strategies are least likely to use them. Our findings reveal that this group tends to have lower intrinsic motivation and weaker mastery-approach goals, which likely hinder their engagement with self-testing. To address this disparity, interventions should focus on cultivating these key motivational prerequisites, thereby empowering low-performing students to adopt effective learning strategies. Integrating self-testing into curricula and promoting self-regulated learning skills may enhance participation among low-achieving students. These findings offer actionable insights for educators, administrators, and policymakers aiming to improve academic outcomes and promote equitable learning opportunities in higher education.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Retrieval practice is most effective when learners engage in effortful and successful retrieval, and when motivational and self-regulatory factors support sustained engagement.
“For instance, retrieval practice is most effective when learners engage in effortful and successful retrieval, and when motivational and self-regulatory factors support sustained engagement (Butler et al., 2017; Schwerter et al., 2025).”
From Testing the testing effect on prolific: when retrieval practice fails to boost learning (2026) · cites this paper as Schwerter et al., 2025
Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,706 · see citing works
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927 · see citing works
ggplot2: Elegant Graphics for Data Analysis
Ginestet, C. E. (2011) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00676_9.x · Cited by 14,484 · see citing works
Becoming a Self-Regulated Learner: An Overview
Zimmerman, B. J. (2002) · Theory Into Practice · doi:10.1207/s15430421tip4102_2 · Cited by 7,305 · see citing works
Time Discounting and Time Preference: A Critical Review
Frederick, S., Loewenstein, G., & O’Donoghue, T. (2002) · Journal of Economic Literature · doi:10.1257/002205102320161311 · Cited by 6,106 · see citing works
The Role of Goal Orientation in Self-Regulated Learning
Pintrich, P. R. (2000) · Handbook of Self-Regulation · doi:10.1016/b978-012109890-2/50043-3 · Cited by 5,173 · see citing works
The structure of phenotypic personality traits.
Goldberg, L. R. (1993) · American Psychologist · doi:10.1037/0003-066x.48.1.26 · Cited by 4,816 · see citing works
The structure of phenotypic personality traits.
Goldberg, L. R. (1993) · American Psychologist · doi:10.1037//0003-066x.48.1.26 · Cited by 4,816 · see citing works
(2000) · Elsevier eBooks · doi:10.1016/b978-0-12-109890-2.x5027-6 · Cited by 4,439 · see citing works
Time Discounting and Time Preference: A Critical Review
Frederick, S. W., Loewenstein, G. F., & O’Donoghue, T. (2002) · Journal of Economic Literature · doi:10.1257/jel.40.2.351 · Cited by 4,116 · see citing works
A 2 × 2 achievement goal framework.
Elliot, A. J., & McGregor, H. A. (2001) · Journal of Personality and Social Psychology · doi:10.1037//0022-3514.80.3.501 · Cited by 3,953 · see citing works
Expectancies, values and academic behaviors
Eccles, J. S. (1983) · Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt) · Cited by 3,901 · see citing works
psych: Procedures for Personality and Psychological Research
Revelle, W. (2017) · Cited by 3,321 · see citing works
A Conceptual Framework for Assessing Motivation and Self-Regulated Learning in College Students
Pintrich, P. R. (2004) · Educational Psychology Review · doi:10.1007/s10648-004-0006-x · Cited by 3,269 · see citing works
Test-Enhanced Learning: Taking Memory Tests Improves Long-Term Retention
Roediger, H. L., & Karpicke, J. D. (2006) · Psychological Science · doi:10.1111/j.1467-9280.2006.01693.x · Cited by 2,867 · see citing works
Handbook of Self-Regulation of Learning and Performance
Schunk, D. H. (2011) · Routledge eBooks · doi:10.4324/9780203839010 · Cited by 2,191 · see citing works
Hulleman, C. S., Schrager, S. M., Bodmann, S. M., & Harackiewicz, J. M. (2010) · Psychological Bulletin · doi:10.1037/a0018947 · Cited by 1,308 · see citing works
On the measurement of achievement goals: Critique, illustration, and application.
Elliot, A. J., & Murayama, K. (2008) · Journal of Educational Psychology · doi:10.1037/0022-0663.100.3.613 · Cited by 1,281 · see citing works
Self-citationA meta-analytic examination of the goal orientation nomological net.
Payne, S. C., Youngcourt, S. S., & Beaubien, J. M. (2007) · Journal of Applied Psychology · doi:10.1037/0021-9010.92.1.128 · Cited by 1,242 · see citing works
Variables associated with achievement in higher education: A systematic review of meta-analyses.
Schneider, M., & Preckel, F. (2017) · Psychological Bulletin · doi:10.1037/bul0000098 · Cited by 1,162 · see citing works
Memory and Metamemory Considerations in the Training of Human Beings
Bjork, R. A. (1994) · Metacognition · doi:10.7551/mitpress/4561.003.0011 · Cited by 1,084 · see citing works
Retrieval Practice Produces More Learning than Elaborative Studying with Concept Mapping
Karpicke, J. D., & Blunt, J. R. (2011) · Science · doi:10.1126/science.1199327 · Cited by 1,019 · see citing works
Harackiewicz, J. M., Barron, K. E., Tauer, J. M., & Elliot, A. J. (2002) · Journal of Educational Psychology · doi:10.1037/0022-0663.94.3.562 · Cited by 921 · see citing works
Metacognition: Knowing about Knowing
Allen, B. (1995) · Information Processing & Management · doi:10.1016/0306-4573(95)80050-4 · Cited by 877 · see citing works
The Big Five personality traits, learning styles, and academic achievement
Komarraju, M., Karau, S. J., Schmeck, R. R., & Avdic, A. (2011) · Personality and Individual Differences · doi:10.1016/j.paid.2011.04.019 · Cited by 733 · see citing works
Multiple Imputation for Nonresponse in Surveys.
Sugden, R. A., & Rubin, D. B. (1988) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.2307/2983027 · Cited by 710 · see citing works
Measuring cost: The forgotten component of expectancy-value theory
Flake, J. K., Barron, K. E., Hulleman, C., McCoach, B. D., & Welsh, M. E. (2015) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2015.03.002 · Cited by 554 · see citing works
Cue strength as a moderator of the testing effect: The benefits of elaborative retrieval.
Carpenter, S. K. (2009) · Journal of Experimental Psychology: Learning, Memory, and Cognition · doi:10.1037/a0017021 · Cited by 503 · see citing works
Unsuccessful retrieval attempts enhance subsequent learning.
Kornell, N., Hays, M. J., & Bjork, R. A. (2009) · Journal of Experimental Psychology: Learning, Memory, and Cognition · doi:10.1037/a0015729 · Cited by 484 · see citing works
Study strategies of college students: Are self-testing and scheduling related to achievement?
Hartwig, M. K., & Dunlosky, J. (2012) · Psychonomic Bulletin & Review · doi:10.3758/s13423-011-0181-y · Cited by 477 · see citing works
The Relationship Between Economic Preferences and Psychological Personality Measures
Becker, A., Deckers, T., Dohmen, T., Falk, A., & Kosse, F. (2012) · Annual Review of Economics · doi:10.1146/annurev-economics-080511-110922 · Cited by 477 · see citing works
True grit and genetics: Predicting academic achievement from personality.
Rimfeld, K., Kovas, Y. V., Dale, P. S., & Plomin, R. J. (2016) · Journal of Personality and Social Psychology · doi:10.1037/pspp0000089 · Cited by 461 · see citing works
Technology Enhanced Learning in higher education; motivations, engagement and academic achievement
Dunn, T. J., & Kennedy, M. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.04.004 · Cited by 399 · see citing works
The Scholarship of Teaching and Learning in Higher Education: An Evidence-Based Perspective
Perry, R. P., & Smart, J. C. (2007) · Springer eBooks · doi:10.1007/1-4020-5742-3 · Cited by 389 · see citing works
Developing Self-Regulation Skills: The Important Role of Homework
Ramdass, D., & Zimmerman, B. J. (2011) · Journal of Advanced Academics · doi:10.1177/1932202x1102200202 · Cited by 368 · see citing works
Bailey, T. H., & Phillips, L. J. (2016) · Higher Education Research & Development · doi:10.1080/07294360.2015.1087474 · Cited by 356 · see citing works
Using qualitative methods to develop a survey measure of math and science engagement
Fredricks, J. A., Wang, M., Schall, J. D., Hofkens, T. L., Sung, H. C., Parr, A. K., & Allerton, J. (2016) · Learning and Instruction · doi:10.1016/j.learninstruc.2016.01.009 · Cited by 349 · see citing works
Wigfield, A., & Eccles, J. S. (2019) · Advances in motivation science · doi:10.1016/bs.adms.2019.05.002 · Cited by 333 · see citing works
Testing (quizzing) boosts classroom learning: a systematic and meta-analytic review
Yang, C., Luo, L., Vadillo, M. A., Yu, R., & Shanks, D. R. (2021) · Psychological Bulletin · doi:10.1037/bul0000309 · Cited by 333 · see citing works
Bembenutty, H., & Karabenick, S. A. (2004) · Educational Psychology Review · doi:10.1023/b:edpr.0000012344.34008.5c · Cited by 331 · see citing works
Azevedo, R. (2009) · Metacognition and Learning · doi:10.1007/s11409-009-9035-7 · Cited by 322 · see citing works
What grades and achievement tests measure
Borghans, L., Golsteyn, B. H. H., Heckman, J. J., & Humphries, J. E. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1601135113 · Cited by 288 · see citing works
Transfer of test- enhanced learning: meta-analytic review and synthesis
Pan, S. C., & Rickard, T. C. (2018) · Psychological Bulletin · doi:10.1037/bul0000151 · Cited by 286 · see citing works
Yperen, N. W. V., Blaga, M., & Postmes, T. (2014) · PLoS ONE · doi:10.1371/journal.pone.0093594 · Cited by 282 · see citing works
Gaspard, H., Häfner, I., Parrisius, C., Trautwein, U., & Nagengast, B. (2016) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2016.09.003 · Cited by 239 · see citing works
The Cambridge Handbook of Motivation and Learning
Renninger, K. A., & Hidi, S. E. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316823279 · Cited by 222 · see citing works
texreg : Conversion of Statistical Model Output in R to L A T E X and HTML Tables
Leifeld, P. (2013) · Journal of Statistical Software · doi:10.18637/jss.v055.i08 · Cited by 221 · see citing works
Agarwal, P. K., Nunes, L. D., & Blunt, J. R. (2021) · Educational Psychology Review · doi:10.1007/s10648-021-09595-9 · Cited by 211 · see citing works
Trautwein, U., & Lüdtke, O. (2007) · Journal of Educational Psychology · doi:10.1037/0022-0663.99.2.432 · Cited by 169 · see citing works
Byrnes, J. P., & Miller, D. C. (2006) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2006.09.002 · Cited by 166 · see citing works
Expectancy-Value Theory and Its Relevance for Student Motivation and Learning
Rosenzweig, E. Q., Wigfield, A., & Eccles, J. S. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316823279.026 · Cited by 154 · see citing works
Hopkins, R. F., Lyle, K. B., Hieb, J. L., & Ralston, P. A. S. (2016) · Educational Psychology Review · doi:10.1007/s10648-015-9349-8 · Cited by 136 · see citing works
McDaniel, M. A., & Einstein, G. O. (2020) · Perspectives on Psychological Science · doi:10.1177/1745691620920723 · Cited by 134 · see citing works
Plante, I., O’Keefe, P. A., & Théorêt, M. (2012) · Motivation and Emotion · doi:10.1007/s11031-012-9282-9 · Cited by 123 · see citing works
Sustaining motivation and academic goals: The role of academic delay of gratification
Bembenutty, H. (1999) · Learning and Individual Differences · doi:10.1016/s1041-6080(99)80002-8 · Cited by 120 · see citing works
Benden, D. K., & Lauermann, F. (2021) · Journal of Educational Psychology · doi:10.1037/edu0000708 · Cited by 111 · see citing works
Self-citationRichardson, J. T. E., Mittelmeier, J., & Rienties, B. (2020) · Oxford Review of Education · doi:10.1080/03054985.2019.1702012 · Cited by 102 · see citing works
Motivation through goal setting
Gómez‐Miñambres, J. (2012) · Journal of Economic Psychology · doi:10.1016/j.joep.2012.08.010 · Cited by 95 · see citing works
Enhancing learning by retrieval: Enriching free recall with elaborative prompting
Endres, T., Carpenter, S. K., Martin, A., & Renkl, A. (2016) · Learning and Instruction · doi:10.1016/j.learninstruc.2016.11.010 · Cited by 91 · see citing works
Predicting achievement and providing support before STEM majors begin to fail
Bernacki, M. L., Chavez, M., & Uesbeck, P. M. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.103999 · Cited by 88 · see citing works
The test trial potentiating model
Izawa, C. (1971) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(71)90012-5 · Cited by 86 · see citing works
Investigating students' use of self‐assessments in higher education using learning analytics
Ifenthaler, D., Schumacher, C., & Kužílek, J. (2022) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12744 · Cited by 86 · see citing works
Du, J., Hew, K. F., & Liu, L. (2023) · Computers & Education · doi:10.1016/j.compedu.2023.104828 · Cited by 79 · see citing works
Present-bias, procrastination and deadlines in a field experiment
Bisin, A., & Hyndman, K. (2019) · Games and Economic Behavior · doi:10.1016/j.geb.2019.11.010 · Cited by 77 · see citing works
Retrieval Practice in Classroom Settings: A Review of Applied Research
Moreira, B. F. T., Pinto, T. S. S., Starling, D. S. V., & Jaeger, A. (2019) · Frontiers in Education · doi:10.3389/feduc.2019.00005 · Cited by 76 · see citing works
Förster, M., Weiser, C., & Maur, A. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.02.012 · Cited by 69 · see citing works
Choice of majors: are women really different from men?
Kugler, A. D., Tinsley, C. H., & Ukhaneva, O. (2021) · Economics of Education Review · doi:10.1016/j.econedurev.2021.102079 · Cited by 68 · see citing works
de la Fuente, J., Martínez-Vicente, J. M., Santos, F. H., Sander, P., Fadda, S., Karagiannopoulou, E., Boruchovitch, E., & Kauffman, D. F. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.861493 · Cited by 66 · see citing works
Rodriguez, F., Rivas, M. J., Matsumura, L. H., Warschauer, M., & Sato, B. K. (2018) · PLOS ONE · doi:10.1371/journal.pone.0200767 · Cited by 64 · see citing works
missRanger: Fast Imputation of Missing Values
Mayer, M. (2017) · doi:10.32614/cran.package.missranger · Cited by 59 · see citing works
Using Web-Based Quizzing to Improve Exam Performance: Lessons Learned
Daniel, D. B., & Broida, J. P. (2004) · Teaching of Psychology · doi:10.1207/s15328023top3103_6 · Cited by 56 · see citing works
Yeo, D. J. S., & Fazio, L. K. (2018) · Journal of Educational Psychology · doi:10.1037/edu0000268 · Cited by 55 · see citing works
Disentangling the predictive validity of high school grades for academic success in university
Vulperhorst, J. P., Lutz, C., de Kleijn, R. A. M., & van Tartwijk, J. (2017) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2017.1353586 · Cited by 54 · see citing works
Requiring a Math Skills Unit: Results of a Randomized Experiment.
Pozo, S., & Stull, C. A. (2006) · American Economic Review · doi:10.1257/000282806777212486 · Cited by 48 · see citing works
Variable Selection with Multiply-Imputed Datasets: Choosing Between Stacked and Grouped Methods
Du, J., Boss, J., Han, P., Beesley, L. J., Kleinsasser, M. J., Goutman, S. A., Batterman, S., Feldman, E. L., & Mukherjee, B. (2022) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2022.2035739 · Cited by 48 · see citing works
Lechuga, M. T., Tudela, J. M. O., & Gómez‐Ariza, C. J. (2015) · Learning and Instruction · doi:10.1016/j.learninstruc.2015.08.002 · Cited by 47 · see citing works
Dunlosky, J. T., & Rawson, K. A. (2015) · Learning and Instruction · doi:10.1016/j.learninstruc.2015.05.003 · Cited by 45 · see citing works
Goal setting and raising the bar: A field experiment
van Lent, M., & Souverijn, M. (2020) · Journal of Behavioral and Experimental Economics · doi:10.1016/j.socec.2020.101570 · Cited by 45 · see citing works
Entry and Degree Attainment in STEM: The Intersection of Gender and Race/Ethnicity
Ma, Y., & Liu, Y. (2017) · Social Sciences · doi:10.3390/socsci6030089 · Cited by 45 · see citing works
Fitting Prediction Rule Ensembles with R Package pre
Fokkema, M. (2020) · Journal of Statistical Software · doi:10.18637/jss.v092.i12 · Cited by 44 · see citing works
Ziegler, M. D., Schroeter, T. A., Lüdtke, O., & Roemer, L. (2018) · Journal of Intelligence · doi:10.3390/jintelligence6030035 · Cited by 41 · see citing works
Cogliano, M., Bernacki, M. L., Hilpert, J. C., & Strong, C. L. (2022) · Journal of Educational Psychology · doi:10.1037/edu0000745 · Cited by 39 · see citing works
Benefits of additional online practice opportunities in higher education
Schwerter, J., Dimpfl, T., Bleher, J., & Murayama, K. (2021) · The Internet and Higher Education · doi:10.1016/j.iheduc.2021.100834 · Cited by 36 · see citing works
Self-citationArgyriou, P., Benamar, K., & Nikolajeva, M. (2022) · Psychology Learning & Teaching · doi:10.1177/14757257221091512 · Cited by 36 · see citing works
Spaced Retrieval Practice Imposes Desirable Difficulty in Calculus Learning
Lyle, K. B., Bego, C. R., Ralston, P. A. S., & Immekus, J. C. (2022) · Educational Psychology Review · doi:10.1007/s10648-022-09677-2 · Cited by 31 · see citing works
Fitting prediction rule ensembles to psychological research data: An introduction and tutorial.
Fokkema, M., & Strobl, C. (2019) · Psychological methods · doi:10.1037/met0000256 · Cited by 29 · see citing works
Wang, T., Li, S., Huang, X., Pan, Z., & Lajoie, S. P. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11357-1 · Cited by 23 · see citing works
‘Making it Count’: Incentives, Student Effort and Performance
Chevalier, A., Dolton, P., & Lührmann, M. (2017) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/rssa.12278 · Cited by 22 · see citing works
Retrieval Practice Enhances Analogical Problem Solving
Wong, S. S. H., Ng, G. J. P., Tempel, T., & Lim, S. W. H. (2017) · The Journal of Experimental Education · doi:10.1080/00220973.2017.1409185 · Cited by 19 · see citing works
Horn, D., & Kiss, H. J. (2018) · PLoS ONE · doi:10.1371/journal.pone.0190163 · Cited by 19 · see citing works
A distributional analysis of upper secondary school performance
Cullinan, J., Denny, K., & Flannery, D. J. (2019) · Empirical Economics · doi:10.1007/s00181-019-01756-8 · Cited by 15 · see citing works
The Testing Effect in the Lecture Hall: Does it Depend on Learner Prerequisites?
Glaser, J., & Richter, T. (2022) · Psychology Learning & Teaching · doi:10.1177/14757257221136660 · Cited by 15 · see citing works
Janson, M. P., & Janke, S. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00488-9 · Cited by 15 · see citing works
Wenzel, K., & Reinhard, M. (2021) · Learning and Motivation · doi:10.1016/j.lmot.2020.101706 · Cited by 14 · see citing works
Endres, T., & Eitel, A. (2023) · Applied Cognitive Psychology · doi:10.1002/acp.4160 · Cited by 14 · see citing works
Self-assessing a test with a possible bonus enhances low performers’ academic performance
Yuan, J., Savadatti, S., & Zheng, G. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.104036 · Cited by 13 · see citing works
Tracing Undergraduate Science Learners’ Digital Cognitive Strategy Use and Relation to Performance
Mefferd, K., & Bernacki, M. L. (2023) · Journal of Science Education and Technology · doi:10.1007/s10956-022-10018-9 · Cited by 11 · see citing works
Voluntary E-Learning Exercises Support Students in Mastering Statistics
Schwerter, J., & Brahm, T. (2024) · Technology Knowledge and Learning · doi:10.1007/s10758-023-09714-1 · Cited by 10 · see citing works
Self-citationA scoping review of research on individual differences in the testing effect paradigm
Smith-Peirce, R. N., & Butler, A. C. (2024) · Learning and Individual Differences · doi:10.1016/j.lindif.2024.102602 · Cited by 10 · see citing works
Self-Testing and Follow-Through of Learning Strategies Supports Student Success
Hazlett, Z. S., Jimenez, P. C., & Knight, J. (2025) · CBE—Life Sciences Education · doi:10.1187/cbe.24-04-0128 · Cited by 8 · see citing works
Spontaneous use of retrieval and rereading: Relation to achievement goals and exam performance.
Wallace, A. S., Elliot, A. J., & Rogge, R. D. (2022) · Journal of Educational Psychology · doi:10.1037/edu0000757 · Cited by 6 · see citing works
Exploring the use and impact of online digital resources in a mathematics module
Morari, V., Palmer, C., Carroll, C., Manning, D., & O’Rourke, S. (2024) · European Journal of Science and Mathematics Education · doi:10.30935/scimath/14605 · Cited by 4 · see citing works
Bertilsson, F., Stenlund, T., Sundström, A. E., & Jonsson, B. (2024) · European Journal of Psychology of Education · doi:10.1007/s10212-024-00845-2 · Cited by 4 · see citing works
Interpretable Prediction Rule Ensembles in the Presence of Missing DataPreprint
Schroeder, V., Schwerter, J., Fokkema, M., & Doebler, P. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2410.16187 · Cited by 2 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
von Keyserlingk, L., Schwerter, J., Kunina‐Habenicht, O., & Lauermann, F. (2025) · Educational Psychology · doi:10.1080/01443410.2025.2564891 · Cited by 18 · see citing works
Self-citationSchwerter, J., Lauermann, F., Dimpfl, T., & Bernacki, M. L. (2026) · Journal of Educational Psychology · doi:10.1037/edu0001042 · Cited by 1 · see citing works
Self-citationTesting the testing effect on prolific: when retrieval practice fails to boost learning
Sigayret, K., Parmentier, J.-F., & Silvestre, F. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1727423
Are you using AI to study? Here’s how to do it well
Rowe, L. I. (2026) · doi:10.64628/aa.5j955ry9x
- Authors
- Schwerter, J., Lauermann, F., Brahm, T., & Murayama, K.
- Affiliations
- TU Dortmund University (DE)
- University of Bonn (DE)
- University of Tübingen (DE)
- Corresponding author
- Schwerter, Jakob
- Venue
- Learning and Individual Differences
- Publisher
- Elsevier BV
- Published
- 2025-10
- Volume
- 123
- Pages
- 102761
- ISSN
- 1041-6080 (ISSN-L)
- OpenAlex
- W4412938220
- Cited by
- 3
- References
- 104
- Citations by year
- 2026: 2 · 2025: 1
- FWCI
- 3.17
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Learning and Individual Differences (publishedVersion, cc-by)
- Funders
- Ministry of Culture and Science of the State of North Rhine-Westphalia (PROFILNRW-2020-068)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Education, Achievement, and Giftedness
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Education › Student Assessment and Feedback
- Keywords
- practice tests, self-testing, low-achieving students, exam performance, prior achievement, intrinsic motivation, mastery goals, low-stakes assessment, higher education equity, student self-regulation, student engagement, learning strategies
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review