ManyClasses 1: Assessing the Generalizable Effect of Immediate Feedback Versus Delayed Feedback Across Many College Classes
Open accessAbstract
Psychology researchers have long attempted to identify educational practices that improve student learning. However, experimental research on these practices is often conducted in laboratory contexts or in a single course, which threatens the external validity of the results. In this article, we establish an experimental paradigm for evaluating the benefits of recommended practices across a variety of authentic educational contexts—a model we call ManyClasses. The core feature is that researchers examine the same research question and measure the same experimental effect across many classes spanning a range of topics, institutions, teacher implementations, and student populations. We report the first ManyClasses study, in which we examined how the timing of feedback on class assignments, either immediate or delayed by a few days, affected subsequent performance on class assessments. Across 38 classes, the overall estimate for the effect of feedback timing was 0.002 (95% highest density interval = [−0.05, 0.05]), which indicates that there was no effect of immediate feedback compared with delayed feedback on student learning that generalizes across classes. Furthermore, there were no credibly nonzero effects for 40 preregistered moderators related to class-level and student-level characteristics. Yet our results provide hints that in certain kinds of classes, which were undersampled in the current study, there may be modest advantages for delayed feedback. More broadly, these findings provide insights regarding the feasibility of conducting within-class randomized experiments across a range of naturally occurring learning environments.
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?
The initial ManyClasses study manipulated the timing of feedback on student assessments in 38 college classes and found no evidence of any global effect of delaying feedback on routine measures of student learning.
“In contrast, the initial ManyClasses study (Fyfe et al., 2021) manipulated the timing of feedback on student assessments in 38 college classes and found no evidence of any global effect of delaying feedback on routine measures of student learning.”
From ManyClasses 2: The effects of prequestions on media interactions and learning. (2026) · cites this paper as Fyfe et al., 2021
The optimal timing of feedback depends on the complexity and nature of the task.
“The optimal timing of feedback depends on the complexity and nature of the task [75].”
From Formative assessment and feedback in medical education: A practical guide: AMEE Guide No. 189 (2026) · cites this paper as [75]
Immediate feedback is not always a requirement; some tasks benefit from delayed feedback in terms of learner outcomes.
“However, studies in general education [75] and industrial-organizational contexts [76] suggest that immediate feedback is not always a requirement; in fact, some tasks benefit from delayed feedback in terms of the impact on learner outcomes, as it provides learners with the opportunity for self-reflection and deeper processing of their performance.”
From Formative assessment and feedback in medical education: A practical guide: AMEE Guide No. 189 (2026) · cites this paper as [75]
For more complex tasks, such as clinical reasoning, delayed feedback may be more effective.
“In contrast, for more complex tasks, such as clinical reasoning, delayed feedback may be more effective.”
From Formative assessment and feedback in medical education: A practical guide: AMEE Guide No. 189 (2026) · cites this paper as [75]
For simpler, skill-based tasks, immediate feedback often enhances learning efficiency.
“For simpler, skill-based tasks, immediate feedback often enhances learning efficiency.”
From Formative assessment and feedback in medical education: A practical guide: AMEE Guide No. 189 (2026) · cites this paper as [75]
Cites
Inference from iterative simulation using multiple sequences
Gelman, A., & Rubin, D. B. (1992) · Statistical Science · doi:10.1214/ss/1177011136 · Cited by 16,627 · see citing works
Hattie, J., & Timperley, H. (2007) · Review of Educational Research · doi:10.3102/003465430298487 · Cited by 12,905 · see citing works
Active learning increases student performance in science, engineering, and mathematics
Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1319030111 · Cited by 9,787 · see citing works
Kluger, A. N., & DeNisi, A. (1996) · Psychological Bulletin · doi:10.1037/0033-2909.119.2.254 · Cited by 5,882 · see citing works
Moving to a World Beyond “ <i>p</i> < 0.05”
Wasserstein, R. L., Schirm, A. L., & Lazar, N. A. (2019) · The American Statistician · doi:10.1080/00031305.2019.1583913 · Cited by 2,976 · see citing works
Beyond the two disciplines of scientific psychology.
Cronbach, L. J. (1975) · American Psychologist · doi:10.1037/h0076829 · Cited by 2,097 · see citing works
Basics of meta‐analysis: <i>I</i> <sup>2</sup> is not an absolute measure of heterogeneity
Borenstein, M., Higgins, J. P. T., Hedges, L. V., & Rothstein, H. R. (2017) · Research Synthesis Methods · doi:10.1002/jrsm.1230 · Cited by 1,848 · see citing works
(2014) · Thammasat University Digital Collections · Cited by 1,732 · see citing works
Kruschke, J. K., & Liddell, T. M. (2018) · Psychonomic Bulletin & Review · doi:10.3758/s13423-016-1221-4 · Cited by 1,288 · see citing works
Undue reliance on I 2 in assessing heterogeneity may mislead
Rücker, G., Schwarzer, G., Carpenter, J. R., & Schumacher, M. (2008) · BMC Medical Research Methodology · doi:10.1186/1471-2288-8-79 · Cited by 1,287 · see citing works
Investigating Variation in Replicability: A “Many Labs” Replication Project
Klein, R. A., Ratliff, K. A., Vianello, M., Adams, R. B., Bahník, Š., Bernstein, M. J., Bocian, K., Brandt, M. J., Brooks, B., Brumbaugh, C. C., et al. (2014) · Social Psychology · doi:10.1027/1864-9335/a000178 · Cited by 1,208 · see citing works
Many Labs 2: Investigating variation in replicability across samples and settings
Klein, R. A., Vianello, M., Hasselman, F., Adams, B. G., Adams, R. B., Alper, S., Aveyard, M., Axt, J. R., Babalola, M. T., Bahník, Š., et al. (2018) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245918810225 · Cited by 944 · see citing works
R-squared for Bayesian Regression Models
Gelman, A., Goodrich, B., Gabry, J., & Vehtari, A. (2018) · The American Statistician · doi:10.1080/00031305.2018.1549100 · Cited by 924 · see citing works
Teaching More by Lecturing Less
Knight, J., & Wood, W. B. (2005) · Cell Biology Education · doi:10.1187/05-06-0082 · Cited by 882 · see citing works
Denwood, M. J. (2016) · Journal of Statistical Software · doi:10.18637/jss.v071.i09 · Cited by 730 · see citing works
Timing of feedback and verbal learning.
Kulik, J. A., & Kulik, C.-L. C. (1988) · Review of Educational Research · doi:10.3102/00346543058001079 · Cited by 703 · see citing works
Yarkoni, T. (2022) · Behavioral and Brain Sciences · doi:10.1017/s0140525x20001685 · Cited by 701 · see citing works
Bayesian data analysis for newcomers
Kruschke, J. K., & Liddell, T. M. (2017) · Psychonomic Bulletin & Review · doi:10.3758/s13423-017-1272-1 · Cited by 409 · see citing works
Frank, M. C., Bergelson, E., Bergmann, C., Cristià, A., Floccia, C., Gervain, J., Hamlin, J. K., Hannon, E. E., Struhl, M. K., Levelt, C. C., et al. (2017) · Infancy · doi:10.1111/infa.12182 · Cited by 333 · see citing works
Board on Behavioral, C., Board on Science Education, Education, D. O. B. A. S. S. A., & National Academies of Sciences, E. (2018) · National Academies Press eBooks · doi:10.17226/24783 · Cited by 330 · see citing works
Moving From the Lab to the Field: The Role of Fidelity and Achieved Relative Intervention Strength
Hulleman, C. S., & Cordray, D. S. (2009) · Journal of Research on Educational Effectiveness · doi:10.1080/19345740802539325 · Cited by 291 · see citing works
(2014) · University of New Hampshire Scholars Repository (University of New Hampshire at Manchester) · Cited by 241 · see citing works
Instructional Complexity and the Science to Constrain It
Koedinger, K. R., Booth, J. L., & Klahr, D. (2013) · Science · doi:10.1126/science.1238056 · Cited by 209 · see citing works
Andrews, T. C., & Lemons, P. P. (2015) · CBE—Life Sciences Education · doi:10.1187/cbe.14-05-0084 · Cited by 159 · see citing works
Metastudies for robust tests of theory
Baribault, B., Donkin, C., Little, D. R., Trueblood, J., Oravecz, Z., van Ravenzwaaij, D., White, C., Boeck, P. D., & Vandekerckhove, J. (2018) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.1708285114 · Cited by 115 · see citing works
Falsifiability is not optional.
LeBel, E. P., Berger, D., Campbell, L., & Loving, T. J. (2017) · Journal of Personality and Social Psychology · doi:10.1037/pspi0000106 · Cited by 112 · see citing works
Mullet, H. G., Butler, A. C., Verdin, B., von Borries, R. F., & Marsh, E. J. (2014) · Journal of Applied Research in Memory and Cognition · doi:10.1016/j.jarmac.2014.05.001 · Cited by 90 · see citing works
A randomized controlled trial of interleaved mathematics practice
Rohrer, D., Dedrick, R. F., Hartwig, M. K., & Cheung, C.-N. (2020) · Journal of Educational Psychology · doi:10.1037/edu0000367 · Cited by 90 · cited 1× · see citing works
“There are existing examples of such studies; for instance, Rohrer et al. (2020) investigated the sequencing of study materials in 57 middle school classrooms, and Booth et al. (2015) tested the role of worked examples with students in 28 different algebra classrooms.”
BackgroundLearning Algebra by Example in Real-World Classrooms
Booth, J. L., Oyer, M. H., Paré-Blagoev, J., Elliot, A. J., Barbieri, C., Augustine, A., & Koedinger, K. R. (2015) · Journal of Research on Educational Effectiveness · doi:10.1080/19345747.2015.1055636 · Cited by 58 · see citing works
Evidence for Cognitive Science Principles that Impact Learning in Mathematics
Booth, J. L., McGinn, K. M., Barbieri, C., Begolli, K. N., Chang, B. L., Miller‐Cotto, D., Young, L. K., & Davenport, J. L. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-805086-6.00013-8 · Cited by 52 · see citing works
Mullaney, K. M., Carpenter, S. K., Grotenhuis, C., & Burianek, S. (2014) · Memory & Cognition · doi:10.3758/s13421-014-0441-y · Cited by 46 · see citing works
Gurung, R. A. R., & Burns, K. (2019) · Applied Cognitive Psychology · doi:10.1002/acp.3507 · Cited by 42 · see citing works
Toward consilience in the use of task-level feedback to promote learning
Butler, A. C., & Woodward, N. R. (2018) · The Psychology of learning and motivation/The psychology of learning and motivation · doi:10.1016/bs.plm.2018.09.001 · Cited by 42 · see citing works
Feedback influences children's reasoning about math equivalence: A meta-analytic review
Fyfe, E. R., & Brown, S. A. (2018) · Thinking & Reasoning · doi:10.1080/13546783.2017.1359208 · Cited by 39 · see citing works
Self-citationThe Clicker Technique: Cultivating Efficient Teaching and Successful Learning
Anderson, L. S., Healy, A. F., Kole, J. A., & Bourne, L. E. (2012) · Applied Cognitive Psychology · doi:10.1002/acp.2899 · Cited by 33 · see citing works
Bloom, H. S., & Spybrook, J. K. (2017) · Journal of Research on Educational Effectiveness · doi:10.1080/19345747.2016.1271069 · Cited by 33 · see citing works
An In Vivo Study of Self-Regulated Study Sequencing in Introductory Psychology Courses
Carvalho, P. F., Braithwaite, D. W., de Leeuw, J. R., Motz, B. A., & Goldstone, R. L. (2016) · PLoS ONE · doi:10.1371/journal.pone.0152115 · Cited by 32 · see citing works
Self-citationEmbedding Experiments: Staking Causal Inference in Authentic Educational Contexts
Motz, B. A., Carvalho, P. F., de Leeuw, J. R., & Goldstone, R. L. (2018) · Journal of Learning Analytics · doi:10.18608/jla.2018.52.4 · Cited by 31 · see citing works
Self-citationA Path to Greater Credibility: Large-Scale Collaborative Education Research
Makel, M. C., Smith, K. N., McBee, M. T., Peters, S. J., & Miller, E. M. (2019) · AERA Open · doi:10.1177/2332858419891963 · Cited by 25 · see citing works
The Role of the Sample in Estimating and Explaining Treatment Effect Heterogeneity
Tipton, E., & Hedges, L. V. (2017) · Journal of Research on Educational Effectiveness · doi:10.1080/19345747.2017.1364563 · Cited by 9 · see citing works
Wakeling, V., & Robertson, P. R. (2017) · American Journal of Educational Research · doi:10.12691/education-5-8-5 · Cited by 6 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Almaatouq, A., Griffiths, T. L., Suchow, J. W., Whiting, M. E., Evans, J., & Watts, D. J. (2022) · Behavioral and Brain Sciences · doi:10.1017/s0140525x22002874 · Cited by 126 · see citing works
Toward AI grading of student problem solutions in introductory physics: A feasibility study
Kortemeyer, G. (2023) · Physical Review Physics Education Research · doi:10.1103/physrevphyseducres.19.020163 · Cited by 59 · see citing works
Almaatouq, A., Griffiths, T. L., Suchow, J. W., Whiting, M. E., Evans, J. A., & Watts, D. J. (2022) · doi:10.31234/osf.io/anjkm · Cited by 57 · see citing works
Lachner, A., Backfisch, I., & Franke, U. (2024) · Frontline Learning Research · doi:10.14786/flr.v12i1.1179 · Cited by 38 · see citing works
Formative assessment and feedback in medical education: A practical guide: AMEE Guide No. 189
Lipnevich, A. A., Mattern, K., & Feddock, C. (2026) · Medical Teacher · doi:10.1080/0142159x.2025.2569623 · Cited by 37 · see citing works
Identifying active ingredients in cognitive-behavioral therapies: What if we didn't?Preprint
Lorenzo‐Luaces, L. (2022) · Behaviour Research and Therapy · doi:10.1016/j.brat.2023.104365 · Cited by 33 · see citing works
Feasibility of adaptive teaching with technology: Which implementation conditions matter?
Sibley, L., Lachner, A., Plicht, C., Fabian, A., Backfisch, I., Scheiter, K., & Bohl, T. (2024) · Computers & Education · doi:10.1016/j.compedu.2024.105108 · Cited by 30 · see citing works
Adaptive teaching with technology enhances lasting learning
Sibley, L., Fabian, A., Plicht, C., Pagano, L., Ehrhardt, N., Wellert, L., Bohl, T., & Lachner, A. (2025) · Learning and Instruction · doi:10.1016/j.learninstruc.2025.102141 · Cited by 24 · see citing works
What the Science of Learning Teaches Us About Arithmetic Fluency
McNeil, N. M., Jordan, N. C., Viegut, A. A., & Ansari, D. (2025) · Psychological Science in the Public Interest · doi:10.1177/15291006241287726 · Cited by 30 · see citing works
A Collaboratively-Derived Research Agenda for E-assessment in Undergraduate Mathematics
Kinnear, G., Jones, I., Sangwin, C., Alarfaj, M. O., Davies, B., Fearn, S., Foster, C. F., Heck, A., Henderson, K. L., Hunt, T., et al. (2022) · International Journal of Research in Undergraduate Mathematics Education · doi:10.1007/s40753-022-00189-6 · Cited by 22 · see citing works
Backfisch, I., Franke, U., Ohla, K., Scholtz, N., & Lachner, A. (2023) · Unterrichtswissenschaft · doi:10.1007/s42010-023-00192-z · Cited by 23 · see citing works
Prompting Better Feedback: A Study of Custom GPT for Formative Assessment in Undergraduate Physics
Mills, E., Mizouri, A., & Peach, A. (2025) · Education Sciences · doi:10.3390/educsci15081058 · Cited by 20 · see citing works
Toward a Holistic Approach to Reducing Academic Procrastination With Classroom Interventions
Miyake, A., & Kane, M. J. (2022) · Current Directions in Psychological Science · doi:10.1177/09637214211070814 · Cited by 17 · see citing works
Poquet, O. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13435 · Cited by 17 · see citing works
Welhaf, M. S., Phillips, N. E., Smeekens, B. A., Miyake, A., & Kane, M. J. (2022) · Cognitive Research Principles and Implications · doi:10.1186/s41235-022-00372-y · Cited by 20 · see citing works
Controlled outputs, full data: A privacy‐protecting infrastructure for MOOC data
Hutt, S. J., Baker, R. S., Mogessie, M., Andres-Bray, J. M. L., & Brooks, C. A. (2022) · British Journal of Educational Technology · doi:10.1111/bjet.13231 · Cited by 20 · see citing works
Self-citationYan, V. X., Sana, F., & Carvalho, P. F. (2023) · Policy Insights from the Behavioral and Brain Sciences · doi:10.1177/23727322231218906 · Cited by 21 · see citing works
Self-citationPennington, C. R., Jones, A., Tzavella, L., Chambers, C., & Button, K. S. (2022) · Aston Publications Explorer (Aston University) · Cited by 15 · see citing works
Hübner, N., Winstone, N., Merk, S., & Hattie, J. (2025) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2025.102365 · Cited by 15 · see citing works
Generalizability, transferability, and the practice-to-practice gap
de Leeuw, J. R., Motz, B. A., Fyfe, E. R., Carvalho, P. F., & Goldstone, R. L. (2022) · Behavioral and Brain Sciences · doi:10.1017/s0140525x21000406 · Cited by 13 · see citing works
Self-citationAsher, M. W., Hartman, J. D., Blaser, M., Eichler, J. F., & Carvalho, P. F. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105387 · Cited by 15 · see citing works
Self-citationBego, C. R., Lyle, K. B., Ralston, P. A. S., Immekus, J. C., Chastain, R. J., Haynes, L. D., Hoyt, L. K., Pigg, R. M., Rabin, S. D., Scobee, M. W., et al. (2024) · International Journal of STEM Education · doi:10.1186/s40594-024-00468-5 · Cited by 12 · see citing works
Lu, X., Wang, W., Motz, B. A., Ye, W., & Heffernan, N. T. (2023) · Computers and Education Open · doi:10.1016/j.caeo.2023.100148 · Cited by 17 · see citing works
Self-citationCharting the Future of Assessments
Kyllonen, P. C., Sevak, A., Ober, T. M., Choi, I., Sparks, J. R., & Fishtein, D. (2024) · ETS Research Report Series · doi:10.1002/ets2.12388 · Cited by 16 · see citing works
Unheard and unused: why students reject teacher and peer feedback
Lipnevich, A. A., Lopera-Oquendo, C., Tomazin, L., Gutterman, J., & Florentin, C. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1567704 · Cited by 8 · see citing works
Scalable Science Education via Online Cooperative Questioning
Hilton, C. B., Goldwater, M. B., Hancock, D., Clemson, M., Huang, A., & Denyer, G. S. (2021) · CBE—Life Sciences Education · doi:10.1187/cbe.19-11-0249 · Cited by 7 · see citing works
Timing of feedback and retrieval practice: a laboratory study with EFL students
Aljabri, S. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03983-6 · Cited by 6 · see citing works
Does technology-based non-interactive teaching enhance students’ learning in the classroom?
Sibley, L., Russ, H., Ahmad, G., Baumgärtner, B., Bräutigam, D., Brümmer, S., Bussmann, H., Erb, N., Evans, L., Fischer, S., et al. (2024) · Computers and Education Open · doi:10.1016/j.caeo.2024.100233 · Cited by 10 · see citing works
The effect of immediate feedback on mathematics learning achievement
Hadijah, H., Isnarto, I., & Walid, W. (2022) · JURNAL PIJAR MIPA · doi:10.29303/jpm.v17i6.4172 · Cited by 6 · see citing works
Impact of different practice testing methods on learning outcomes
Akbulut, Y. (2024) · European Journal of Education · doi:10.1111/ejed.12626 · Cited by 6 · see citing works
Lachner, A., Sibley, L., & Wagner, S. (2024) · Educational Psychology Review · doi:10.1007/s10648-024-09876-z · Cited by 6 · see citing works
Beyond Repetition: The Role of Varied Questioning and Feedback in Knowledge Generalization
Yadav, G., Carvalho, P. F., McLaughlin, E. A., & Koedinger, K. R. (2024) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3657604.3664688 · Cited by 4 · see citing works
Self-citationLachner, A., Russ, H., Hübner, N., Sibley, L., & Scheiter, K. (2025) · Educational Psychology Review · doi:10.1007/s10648-025-10060-0 · Cited by 3 · see citing works
Wild brooms and learning analytics
Motz, B. A., & Morrone, A. S. (2023) · Journal of Computing in Higher Education · doi:10.1007/s12528-023-09353-6 · Cited by 3 · see citing works
Self-citationEstimating Student Grades through Peer Assessment as a Crowdsourcing Calibration Problem
Xiao, Y., Gao, Y., Yue, C., & Gehringer, E. F. (2022) · International Conference on Information Technology Based Higher Education and Training (ITHET) · doi:10.1109/ithet56107.2022.10031993 · Cited by 3 · see citing works
Time Scarcity and Student Performance: Instructional Strategies for Busy Adult Online Students
Holmes, M. (2023) · Canadian Journal of Learning and Technology · doi:10.21432/cjlt28357 · Cited by 2 · see citing works
Lipnevich, A. A., Lopera-Oquendo, C., Çalık, B., Shu, L., & Tay, H. Y. (2025) · Asia Pacific Education Review · doi:10.1007/s12564-025-10086-7 · Cited by 2 · see citing works
Assessing student perspectives on STACK implementation in university mathematics courses
Chelleri, E., Doz, D., Juma, Z. O., Dogbalou, M. W. D., & Lewański, D. (2025) · International Journal of Mathematical Education in Science and Technology · doi:10.1080/0020739x.2025.2563115 · Cited by 2 · see citing works
The impact of online feedback on student learning outcomes: a meta-analysis study
Adıgüzel, O. C., Esen, E., & Karagöl, İ. (2025) · Asia Pacific Education Review · doi:10.1007/s12564-025-10099-2 · Cited by 3 · see citing works
Piloting a Flexible Deadline Policy for a First-Year Computer Programming Course
Bhatt, I., Alford, L. K., Begley, L., Hosseini, R., & Lichti, D. A. (2024) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--43892 · Cited by 2 · see citing works
Self-citationMegaSoTL: Supporting pedagogical research across multiple institutions
Mojarad, S. N., & Cruz, L. E. (2021) · To improve the academy · doi:10.3998/tia.470 · Cited by 2 · see citing works
Self-citationToward a Holistic Approach to Reducing Academic Procrastination with Classroom InterventionsPreprint
Miyake, A., & Kane, M. J. (2021) · doi:10.31234/osf.io/9murb · Cited by 2 · see citing works
ManyClasses 2: The effects of prequestions on media interactions and learning.
Motz, B. A., Chinni, A., de Leeuw, J. R., Jankowski, H. E., Aggarwal, A., Amato, M., Berlin, K., Britten, K., Brown, A., Cerchiaro, M. L., et al. (2026) · Journal of Educational Psychology · doi:10.1037/edu0000978 · Cited by 1 · see citing works
Self-citationChrist, A., Birnbaum, L., & Kröner, S. (2025) · Zeitschrift für Erziehungswissenschaft · doi:10.1007/s11618-025-01301-7 · Cited by 1 · see citing works
Smith, H., Ottmar, E., Ngo, V., Closser, A. H., Chan, J. Y., Vanacore, K., & Sales, A. (2025) · Journal of Research on Educational Effectiveness · doi:10.1080/19345747.2025.2534377 · Cited by 3 · see citing works
Welhaf, M., Phillips, N. E., Smeekens, B. A., Miyake, A., & Kane, M. J. (2021) · doi:10.31234/osf.io/hu6wn · Cited by 1 · see citing works
Learning by Teaching in the Wild: Domain, Grades, and School Track MatterPreprint
Sibley, L., Russ, H., & Lachner, A. (2023) · doi:10.31234/osf.io/d293m · Cited by 1 · see citing works
Williamson, M. J., & Garbin, J. (2022) · Teaching of Psychology · doi:10.1177/00986283221081225
MacKay, J. R. D., & Wu, S. C. (2023) · Wiley eBooks · doi:10.1002/9781119852865.ch17 · Cited by 3 · see citing works
Schuetze, B. A., Yan, V. X., & Carvalho, P. F. (2024) · doi:10.31234/osf.io/yu8tr
Self-citationManyClasses 2: The Effects of Prequestions on Media Interactions and LearningPreprint
Motz, B. A., Chinni, A., de Leeuw, J. R., Jankowski, H. E., Aggarwal, A., Amato, M., Berlin, K. L., Britten, K., Brown, A., Cerchiaro, M. L., et al. (2024) · doi:10.31234/osf.io/3xbma
Self-citationFully Latent Principal Stratification With Measurement Models
Lee, S., Sales, A., Kang, H., & Whittaker, T. A. (2025) · Journal of Educational and Behavioral Statistics · doi:10.3102/10769986251321428
Schuetze, B. A., Yan, V. X., & Carvalho, P. F. (2025) · doi:10.31234/osf.io/yu8tr_v2
Self-citationPerez, M. J. (2025) · Journal of interdisciplinary perspectives · doi:10.69569/jip.2025.599
Generative Processing Strengthens Learning Across Errorful and Non-Errorful Contexts
Khin, T. H. K., & Pan, S. C. (2026) · The Journal of Experimental Education · doi:10.1080/00220973.2026.2636103
Students Who Choose to Challenge Themselves Perform Better
Niu, J. L., & Gurung, A. (2026) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3774398.3811615
Quixal, M., Eder, T., Gazar, F., & Lachner, A. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1768177
Juma, Z. O., Doz, D., Dogbalou, M. W. D., & Lewański, D. (2026) · International Journal of Mathematical Education in Science and Technology · doi:10.1080/0020739x.2026.2723184
Open Data in educational research: Possibilities, challenges, and reflections
Svecnik, E., & Krammer, G. (2026) · Zeitschrift für Bildungsforschung · doi:10.1007/s35834-026-00565-1
Martin, N. S., Vest, N. A., Closser, A. H., & Botelho, A. F. (2026) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2026.102505
- Authors
- Fyfe, E. R., de Leeuw, J. R., Carvalho, P. F., Goldstone, R. L., Sherman, J., Admiraal, D., Alford, L. K., Bonner, A., Brassil, C. E., Brooks, C. A., Carbonetto, T., Chang, S. H., Cruz, L., Czymoniewicz-Klippel, M., Daniel, F., Driessen, M., Habashy, N., Hanson-Bradley, C. L., Hirt, E. R., Carbonell, V. H., Jackson, D. K., Jones, S., Keagy, J. L., Keith, B., Malmquist, S. J., McQuarrie, B., Metzger, K. J., Min, M. K., Patil, S., Patrick, R. S., Pelaprat, E., Petrunich-Rutherford, M. L., Porter, M. R., Prescott, K., Reck, C., Renner, T., Robbins, E., Smith, A. R., Stuczynski, P., Thompson, J., Tsotakos, N., Turk, J. K., Unruh, K., Webb, J. D., Whitehead, S. N., Wisniewski, E. C., Zhang, K. A., & Motz, B. A.
- Affiliations
- Indiana University Bloomington (US)
- Department of Cognitive Science, Vassar College, Poughkeepsie, New York, USA
- Carnegie Mellon University (US)
- University of Nebraska–Lincoln (US)
- University of Michigan (US)
- Pennsylvania State University (US)
- Indiana University Southeast (US)
- Department of Biobehavioral Health, Penn State University, University Park, Pennsylvania, USA
- Indiana University Northwest (US)
- University of Minnesota (US)
- Indiana University Kokomo (US)
- University of Minnesota Morris (US)
- University of Minnesota Rochester (US)
- Unizin, Austin, Texas, USA
- University of Minnesota, Duluth (US)
- Indiana University East (US)
- Corresponding author
- Fyfe, Emily R.
- ORCID
- Fyfe, Emily R.
- de Leeuw, Joshua R.
- Carvalho, Paulo F.
- Goldstone, Robert L.
- Sherman, Janelle
- Admiraal, David
- Alford, Laura K.
- Brassil, Chad E.
- Brooks, Christopher A.
- Chang, Sau Hou
- Cruz, Laura
- Daniel, Frances
- Habashy, Noel
- Hirt, Edward R.
- Metzger, Kelsey J.
- Min, Maung K.
- Patil, Sameer
- Petrunich-Rutherford, Maureen L.
- Tsotakos, Nikolaos
- Turk, Judith K.
- Zhang, Ke Anne
- Motz, Benjamin A.
- Venue
- Advances in Methods and Practices in Psychological Science
- Publisher
- SAGE Publications
- Published
- 2021-07
- Volume
- 4
- Issue
- 3
- ISSN
- 2515-2459 (ISSN-L)
- 2515-2467
- OpenAlex
- W3162001182
- Cited by
- 59
- References
- 42
- Citations by year
- 2026: 6 · 2025: 16 · 2024: 13 · 2023: 8 · 2022: 12 · 2021: 4
- FWCI
- 16.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Advances in Methods and Practices in Psychological Science (publishedVersion, cc-by-nc)
- Lincoln (University of Nebraska) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Education › Student Assessment and Feedback
- Social Sciences › Education › Evaluation of Teaching Practices
- Social Sciences › Education › Educational Research and Methods
- Keywords
- immediate feedback, delayed feedback, feedback timing, student learning, generalizability, university courses, class assignment, assessment performance, teaching practice
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
