The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
Effects of Feedback in a Computer-Based Learning Environment on Students’ Learning Outcomes: A Meta-Analysis
Abstract
In this meta-analysis, we investigated the effects of methods for providing item-based feedback in a computer-based environment on students’ learning outcomes. From 40 studies, 70 effect sizes were computed, which ranged from −0.78 to 2.29. A mixed model was used for the data analysis. The results show that elaborated feedback (EF; e.g., providing an explanation) produced larger effect sizes (0.49) than feedback regarding the correctness of the answer (KR; 0.05) or providing the correct answer (KCR; 0.32). EF was particularly more effective than KR and KCR for higher order learning outcomes. Effect sizes were positively affected by EF feedback, and larger effect sizes were found for mathematics compared with social sciences, science, and languages. Effect sizes were negatively affected by delayed feedback timing and by primary and high school. Although the results suggested that immediate feedback was more effective for lower order learning than delayed feedback and vice versa, no significant interaction was found.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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?
In the citing paper
“On the one hand, there is robust evidence that elaborate feedback with correct answers facilitates learning (Heckler and Mikula, 2016; Van der Kleij et al., 2015; Wisniewski et al., 2020).”
From Assessing the Impact and Underlying Pathways of Sequenced AI feedback on Student Learning (2026) · cites this paper as Van der Kleij et al., 2015
In the citing paper
“However, non-layered feedback can be effective without layering (e.g., (Heckler and Mikula, 2016; Van der Kleij et al., 2015; Wisniewski et al., 2020)).”
From Assessing the Impact and Underlying Pathways of Sequenced AI feedback on Student Learning (2026) · cites this paper as Van der Kleij et al., 2015
In the citing paper
“A particularly effective form of feedback provides the correct answer with an explanation of why it is the correct answer (Heckler and Mikula, 2016; Van der Kleij et al., 2015; Butler et al., 2013; Wisniewski et al., 2020).”
From Assessing the Impact and Underlying Pathways of Sequenced AI feedback on Student Learning (2026) · cites this paper as Van der Kleij et al., 2015
In the citing paper
“These conclusions are further supported by meta-analytic evidence: a synthesis of 40 studies comprising 70 effect sizes revealed that elaborated feedback (e.g., providing explanations) produced a substantially larger mean effect size than feedback limited to correctness judgments or feedback providing only the correct answer (Van der Kleij et al., 2015).”
From Assessing the Impact and Underlying Pathways of Sequenced AI feedback on Student Learning (2026) · cites this paper as Van der Kleij et al., 2015
Citations
Cites
Hattie, J., & Timperley, H. (2007) · Review of Educational Research · doi:10.3102/003465430298487 · Cited by 12,905
(2000) · Cited by 9,198
Assessment and Classroom Learning
Black, P., & Wiliam, D. (1998) · Assessment in Education: Principles, Policy & Practice · doi:10.1080/0969595980050102 · Cited by 8,152
Visible learning: a synthesis of over 800 meta‐analyses relating to achievement
Carter, M. (2009) · Educational Psychology · doi:10.1080/01443410903415150 · Cited by 7,696
Kluger, A. N., & DeNisi, A. (1996) · Psychological Bulletin · doi:10.1037/0033-2909.119.2.254 · Cited by 5,882
Distribution Theory for Glass's Estimator of Effect Size and Related Estimators
Hedges, L. V. (1981) · Journal of Educational Statistics · doi:10.2307/1164588 · Cited by 5,695
Shute, V. J. (2008) · Review of Educational Research · doi:10.3102/0034654307313795 · Cited by 4,631
Formative assessment and the design of instructional systems
Sadler, D. R. (1989) · Instructional Science · doi:10.1007/bf00117714 · Cited by 4,285
Inside the Black Box: Raising Standards through Classroom Assessment
Black, P., & Wiliam, D. (2010) · Phi Delta Kappan · doi:10.1177/003172171009200119 · Cited by 4,231
The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring
BLOOM, B. S. (1984) · Educational Researcher · doi:10.3102/0013189x013006004 · Cited by 2,734 · cited 2×
“According to Bloom (1984), one-to-one tutoring is the most effective form of instruction.”
Background
Research works printed but not linked: 15
- Black P., 1998, Phi Delta Kappan, 80, 139
- *Bowles M. A., 2005, Effects of verbalization condition and type of feedback on L2 development in a CALL task
- *Cameron B., 2005, Journal of Interactive Learning Research, 16, 243
- Cohen J., 1988, Statistical power analysis for the behavioural sciences
- 10.3102/10769986006002107 · DOI · Paper page
- *Kim J. Y. L., 1991, Journal of Computer-Based Instruction, 18, 14
- *Merril J., 1987, Journal of Computer-Based Instruction, 14, 18
- Narciss S., 2004, Instructional design for multimedia learning, 181
- Nyquist J. B. (2003). The benefits of reconstruing feedback as a larger system of formative assessment: A meta-analysis (Unpublished master’s thesis). Vanderbilt University, Nashville, TN.
- 10.3102/10769986008002157 · DOI · Paper page
- Resing W. C. M. (1990). Intelligentie en leerpotentieel. Een onderzoek naar het leerpotentieel van jonge leerlingen uit het basis-en speciaal onderwijs [Intelligence and learning potential: A study on the learning potential of young children in regular and special education] (Unpublished doctoral dissertation). Vrije Universiteit Amsterdam, Netherlands.
- Shadish W. R., 1994, The handbook of research synthesis, 261
- Smith P. L., 2005, Instructional design
- *Wager S. U. (1983). The effect of immediacy and type of informative feedback on retention in a computer-assisted task (Unpublished doctoral dissertation). The Florida State University, Tallahassee.
- White H. D., 1994, The handbook of research synthesis, 41
Cited by
Sorted by citations
Items 601–700 of 715
Hu, X., Goh, Y. M., Lin, A., & Liu, Q. (2023) · Lecture notes in civil engineering · doi:10.1007/978-981-19-7331-4_31
(2020) · Odile Jacob eBooks · doi:10.3917/oj.masso.2020.01.0239
Digitale Feedback-Trends im Coaching und zur Selbststeuerung richtig einsetzen
Holtmeier, S., & Mertin, I. (2023) · Organisationsberatung Supervision Coaching · doi:10.1007/s11613-023-00849-9
Hanafi, Y., Anam, F. K., Saefi, M., Ikhsan, M. A., & Diyana, T. N. (2023) · Journal of Community Service and Empowerment · doi:10.22219/jcse.v4i3.29202
“Thus, positive results will be obtained if the feedback provided is informative, specific, goal-related, and not expressed in judgmental language (Van der Kleij et al., 2015), as is done in the recitation program.”
Backgroundعبدالفتاح, د. أ. ع. (2023) · تکنولوجيا التعليم سلسلة دراسات وبحوث · doi:10.21608/tesr.2023.331709
Huebner, B. J., Jackson, B., Danzl, M. M., & Pitt, J. E. (2023) · Journal of Physical Therapy Education · doi:10.1097/jte.0000000000000322
Mengelkamp, C. (2023) · Springer eBooks · doi:10.1007/978-3-662-66608-1_9
Feedback on Multiple Choice Assessments
Sefcik, D. (2024) · IAMSE manuals · doi:10.1007/978-3-031-52921-4_4
Thanyalakmara, T. (2021) · Journal of Education Studies · doi:10.58837/chula.educu.49.3.1
Effects of Feedback Interventions on Letter-String Problem Solving by Analogical Transfer
Rull, M. A. P., Morales, V., Ortega, P., & Villicaña, V. (2023) · Revista Argentina de Ciencias del Comportamiento ( RACC ) · doi:10.32348/1852.4206.v15.n2.30905
Online course feedback comments clustering via document similarity
Alonso, D. R., Cortés, C. Z., Castillo, H., & Carranza, J. L. C. (2024) · Revista de Tecnología y Educación · doi:10.35429/jtae.2024.19.8.1.12
Merrick, M., & Fyfe, E. R. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4848261
Frazee, M., Gipson, K., Lyden, E., & Langel, S. (2024) · Innovations in Health Sciences Education Journal · doi:10.32873/unmc.dc.ihsej.0056
Artificial Intelligence-Based Analysis of Biological Signals and Attention Assessment Methods
Li, Z., Li, H., & Jiang, Y. (2024) · International Symposium on Robotics, Artificial Intelligence and Information Engineering (RAIIE) · doi:10.1145/3689299.3689314
“Van der Kleij et al. conducted a meta-analysis on the effects of feedback on student learning outcomes in computer-assisted learning environments [8].”
Batita, M. S. R., Setyosarı, P., Kuswandi, D., & Ulfa, S. U. (2024) · International Conference on Education and Multimedia Technology (ICEMT) · doi:10.1145/3678726.3678734
Effects of Instruction–Problem Arrangements in Video Tutorials for Software Training
van der Meij, H., & Voerman, T. E. (2024) · IEEE Transactions on Professional Communication · doi:10.1109/tpc.2024.3449508
“Given the large amount of literature on the positive effects of feedback on learning [35], [54], [55], [56], [57], our study allowed users to review task videos during the final problem-solving phase (i.e., in I-PS and PS-I-PS).”
20 Years of Interteaching Research and Practice: A Tutorial for its Use in the Classroom
Rosales, R., Gayman, C. M., Jimenez, S. T., & Soldner, J. L. (2024) · Behavior Analysis in Practice · doi:10.1007/s40617-024-00986-2
“On the other hand, research suggests that immediate feedback facilitates knowledge retention (e.g., Van der Kleij et al., 2015); however, given that only one study has evaluated the timing of the lecture delivery, future research on this component of interteaching is warranted.”
Metaverse and Its Underlying Challenges in Higher Education
Mukherjee, S. (2024) · Advances in educational marketing, administration, and leadership book series · doi:10.4018/979-8-3693-5443-8.ch011
Förderlicher Entwurf cyber-physischer Produktionssysteme
Urbas, L., Pelzer, F., Lorenz, S., & Herlitzius, T. (2024) · Springer eBooks · doi:10.1007/978-3-662-58528-3_132
Povratne informacije učenicima u početnoj nastavi matematike
Horvat, S. M. (2024) · Metodički ogledi · doi:10.21464/mo.30.2.1
“Jedno-stavne povratne informacije usmjerene su na ispravnost odgovora (Van der Kleij et al ., 2015).”
Carstensen, B., Asbury, K., Benckwitz, L., Hilz, A., & Guill, K. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5092847 · Cited by 1
Guess Your Health, un progetto pilota di promozione della salute in contesto scolastico
Picci, I., Frau, S., Moi, A., Gabbrielli, M., Caredda, S., Casula, F., Cau, A., Deidda, M., & Guicciardi, M. (2025) · PSICOLOGIA DELLA SALUTE · doi:10.3280/pds2025-001007
Liu, Q., Prakash, H. M., & Heckler, A. F. (2025) · Physical Review Physics Education Research · doi:10.1103/physrevphyseducres.21.010121
Use of Real-Time Sensors to Teach Statistical Graphing to Home-Schooled Children
Chueh, H.-F., & Zhao, Y. (2025) · Advances in early childhood and K-12 education · doi:10.4018/979-8-3693-1894-2.ch002
S.C.I.C.E.V Feedback System: Advancing Psychomotor Development, Skills, and Technique in Volleyball
Pârvu, C., Szabo, D. A., Rosculet, R.-T., & Mocanu, G. D. (2025) · Journal of Information Technology Education Research · doi:10.28945/5487 · Cited by 3
Xero Software in Accounting Education: Enhancing Digital Acumen and Employability
Charles, M., & Julia, S. (2025) · Springer proceedings in business and economics · doi:10.1007/978-3-031-84885-8_14
Ai Tutors vs. Tenacious Myths: Evidence from Personalised Dialogue Interventions in EducationPreprint
Corbett, B. J., & Tangen, J. M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5342794
Pattemore, M. (2025) · Springer eBooks · doi:10.1007/978-3-031-51447-0_120-1
Transforming Assessment Practices
Kaddouri, M., Jmad, S., Azzimani, T., Mhamdi, K., & Abbadi, Z. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-6013-3.ch006
Can the Effect of Automated Elaborated Feedback Be Increased with Affective Feedback?
Deni̇z, Y., & Bayrak, F. (2025) · Open Praxis · doi:10.55982/openpraxis.17.3.857 · Cited by 1
Durak, H. Y. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2528087
“Feed-back is the information given to learners about their learning performance in learning environments (Van der Kleij et al., 2015).”
Use of Generative AI for Assessment Creation by High School Mathematics Teachers
Grover, S., Grunge, B., Lynch, R., & Clarkson, C. (2025) · Lecture notes in educational technology · doi:10.1007/978-981-96-5761-2_27
Balbi, A., Bonilla, M., Curione, K., Berrutti, S., & Hoffmann, A. F. (2025) · Psychological Test Adaptation and Development · doi:10.1027/2698-1866/a000107
Zink, T., Hahnel, C., Kroehne, U., Deribo, T., Artelt, C., Goldhammer, F., Naumann, J., & Schoor, C. (2025) · European Journal of Psychology of Education · doi:10.1007/s10212-025-01033-6
“In particular, computer-based feedback has been shown to significantly foster students’ motivation and learning outcomes (Shute, 2008; Van der Kleij et al., 2015).”
Sun, J. C., Lin, T.-Y., Hsieh, P.-H., & Chang, W.-L. (2025) · Journal of Educational Computing Research · doi:10.1177/07356331251399685
Evaluating Feedback in an Augmented Reality Learning Environment
Peters, N. C., Thierfelder, B., Ivanov, A., Altmeyer, K., & Malone, S. (2025) · IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) · doi:10.1109/ismar-adjunct68609.2025.00139
“While Elaborated Feedback (EF), which includes explanations and conceptual guidance, has been shown to be more effective for higher-order thinking [25], it also demands more sophisticated design and greater learner cognitive processing.”
Assessment for Distance Education
(2024) · Emerald Group Publishing Limited eBooks · doi:10.1108/979-8-88730-513-420251012
Homayounzadeh, Z., Bavali, M., & Behjat, F. (2025) · Cogent Education · doi:10.1080/2331186x.2025.2590860 · Cited by 1
“This is reinforced by Van der Kleij et al. (2015), who highlighted the role of dynamic interaction in cognitive development, and further enriched by Teng and Yang (2023), which shows that multimodal feedback fosters metacognitive awareness among graduate learners, a process likely driving our…”
Study of Technology-Based Inquiry Learning
Harel, R., & Yerushalmy, M. (2024) · Emerald Group Publishing Limited eBooks · doi:10.1108/979-8-88730-711-420251017
Focus on Data Analysis and Continuous Improvement
(2025) · Emerald Group Publishing Limited eBooks · doi:10.1108/979-8-88730-867-820251013
Rethinking Retrieval Practice with AI: A GPT-Based Approach for Introductory Programming
Elhayany, M., & Meinel, C. (2025) · IEEE Digital Education and MOOCS Conference (DEMOcon) · doi:10.1109/democon65705.2025.11282675
Jiang, T., Gong, S., Wang, J., Zhang, Y., Wang, Z., & Zhao, K. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5531060
The Impact of Self-Regulated Learning on Achieving Intended Learning Outcomes
Nguyen, P. H., Tangworakitthaworn, P., & Gilbert, L. D. (2025) · International Computer Science and Engineering Conference · doi:10.1109/icsec67360.2025.11298049
“Large-scale online courses often lack direct interaction, which can impact learner satisfaction (Van der Kleij et al., 2015).”
Prediction of Performance in Standardised Assessments from Computer-Based Formative Assessment DataPreprint
Garzón, B., Berger, S., Driver, C., & Tomasik, M. J. (2026) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/hqf3s_v2
Wenzel, A., Geiger, J.-M., & Liening, A. (2026) · Springer eBooks · doi:10.1007/978-3-658-50476-2_12
Barkela, V., & Leuchter, M. (2026) · Journal of Computer Assisted Learning · doi:10.1002/jcal.70193
Hu, Y., Wang, Y., Watanabe, B., Gao, M., Qin, Z., Shi, K., Zhang, Z., Shambanna, S., Xue, Z., Zou, T., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-8588902/v1
Theile, Y., & Rott, B. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1736919
Enhancing Mobile-Based Instant Feedback for Soft Skills Development: A User Experience Analysis
Heikkinen, S., & Myyryläinen, H. (2025) · International Conference on Education Technology and Computer · doi:10.1109/icetc66579.2025.11387370
Prediction of Performance in Standardised Assessments from Computer-Based Formative Assessment Data
Garzón, B., Berger, S., Driver, C., & Tomasik, M. J. (2026) · Technology Knowledge and Learning · doi:10.1007/s10758-025-09947-2
“Information about students’ progress allows teachers to modify goals timely, adjust daily activities and give feedback (Van der Kleij et al., 2015 ).”
Jin, S.-H., Hoffman, D. L., Paek, S., & Kim, T.-H. (2026) · Educational Technology Research and Development · doi:10.1007/s11423-026-10603-4
“Still other researchers have focused on improving the quality of feedback provided by instructors to maximize its impact on online learning (Van der Kleij et al., 2015).”
Guo, Y., Zhang, Y., Yu, L., Chen, D., & Wei, Z. (2025) · International Conference on Educational Technology Management (ICETM) · doi:10.1109/icetm67477.2025.11413440
Zhao, Y., & Zheng, Y. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2638487 · Cited by 1
“Among these support strategies, feedback is widely regarded as one of the most critical support mechanisms within the SBIL environment (Van der Kleij et al., 2015).”
Designing conversational Agents for adaptive instructional support in business simulation gaming
Wenzel, A., Geiger, J.-M., & Liening, A. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100576 · Cited by 3
Research challenges and future perspectives for e-assessment technologies in higher education
Striewe, M., Strickroth, S., & Ullrich, M. (2026) · i-com · doi:10.1515/icom-2026-0008 · Cited by 1
The effects of peer tutoring on the acquisition of new operants for tutors, tutees and observers
Sun, Y., & Chang, H. (2026) · Tizard Learning Disability Review · doi:10.1108/tldr-10-2025-0054
GPT-4 feedback increases student activation and learning outcomes in higher education
Geschwind, S., Lambsdorff, J. G., Voss, D., & Hackl, V. (2026) · International Journal of Artificial Intelligence in Education · doi:10.1016/j.ijaied.2026.100014 · Cited by 4
Impacts de la fatigue visuelle sur l’apprentissage via serious game en réalité virtuelle
Souchet, A. D. (2020) · theses.fr (ABES) · doi:10.70675/f91891adzfa55z4eb6za629z42d6e542de58 · Cited by 3
Apprentissage de scènes tactiques par imagination : Impacts de l'expertise et du moment du test
Mguidich, H. (2024) · theses.fr (ABES) · doi:10.70675/8134c7b9z0155z4b05z8d3dz3fb980301100
Zambrano, O. A. C., Fernández, N. A. V., & Pérez, O. M. (2026) · Revista Científica Multidisciplinar G-nerando · doi:10.66473/rcmg.v7i1.1062
AI-Assisted Feedback in Online Teacher Training Courses
Capdehourat, G., Castelo, D., Curi, M. E., & García, S. (2026) · Lecture notes in educational technology · doi:10.1007/978-981-95-7580-0_3
Empowering Student Agency Through Iterative, Feedback-Driven, and Learner-Centered Design Practices
Kaur, R. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-7102-3.ch008
Basa, R. A., & Salazar, D. A. (2026) · Indonesian Journal of Education Research (IJoER) · doi:10.37251/ijoer.v7i2.2400
“In mathematics, elaborated feedback produces greater learning effects compared to simple correctness feedback or the act of simply giving the correct answer [11].”
Zhao, D., Alotaibi, K. A., Rehman, S., & Li, X. (2026) · BMC Sports Science Medicine and Rehabilitation · doi:10.1186/s13102-026-01710-z
“More broadly, work in educational technology has reported that AI-enhanced instructional systems tend to be associated with improvements in learners’ strategic shifting and openness to alternative solutions, both central components of cognitive adaptability [53, 54].”
Cai, L., & Wu, H. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1858248
Nguon, S. O., Hart, B., & Meak, C. (2026) · Journal of Social Knowledge Education (JSKE) · doi:10.37251/jske.v7i3.3008
“Additionally, Kleij, et al. [70] found that feedback interventions were most effective when they provided elaborated information rather than simple correctness judgement.”
WikiCoach: Scaffolding Learning for Novices in Online Apprenticeship Communities
Zhou, M., Zhao, M., & Terveen, L. (2026) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3774935.3806185 · Cited by 1
“In education and learning sciences, feedback is most effective when it fosters reflection and agency rather than prescribing solutions [58, 73].”
Literature Review: Analysing the Factors Affecting College Students’ Digital Language Tests
Zhang, J., & Razak, R. A. (2026) · Africa Education Review · doi:10.1080/18146627.2026.2659587
Real-time cognitive-affective dynamics of failure feedback in a technology-based learning task
Ackermann, H., Lange, A. L., Dumont, H., Hafner, V. V., & Lazarides, R. (2026) · Communications Psychology · doi:10.1038/s44271-026-00487-8
Wang, J., Gong, S., Guo, J., Wang, Z., & Wang, Y. (2026) · Journal of Computer Assisted Learning · doi:10.1002/jcal.70273
Wu, L., Xu, S., Liao, W., & Fujimura, S. (2026) · Behaviour and Information Technology · doi:10.1080/0144929x.2026.2692099
Araya, D. H., & Henríquez, V. V. (2026) · Improving Schools · doi:10.1177/13654802261458493
Offline Reinforcement Learning for Adaptive Feedback in Online Programming Education
Kirouchenassamy, B., Yessad, A., Jolivet, S., & Luengo, V. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29755-6_6
Yang, K., Wei, J., Liang, Z., Raković, M., Oliveira, E., Gašević, D., & Chen, G. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29755-6_49
Xin, Y., Weina, H., Deng, S., Yan, D., & Liuheng, L. (2026) · Studies In Educational Evaluation · doi:10.1016/j.stueduc.2026.101639
The Use of Virtual Manipulative Environments in Assessment
Hurme, J., Laakkonen, M.-P., Huttunen, H.-L., & Harmoinen, S. (2026) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon67543.2026.11574124
Fujia, Y., & See, B. H. (2026) · Review of Education · doi:10.1002/rev3.70189 · Cited by 1
Audience response systems for formative assessment purposes:
Pérez-Segura, J. J., Cirugeda, I. L., Campillo, R. M. L., & Ruiz, R. S. (2026) · International Journal of English Studies · doi:10.6018/ijes.608181
“Van feedback.”
InfluentialAlmayez, M., Al-khresheh, M. H., Alruwaili, S. F., Al-Ali, M. N., & Alenezy, F. S. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.107322
Automated Assessment in Network Security Education by Means of Unit Testing
Haskins, B. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-3-032-20235-2_3
Schaaf, J., Rolfes, T., Nagy, G., & Heinze, A. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1842708
Hu, Y., Wang, Y., Watanabe, B., Gao, M., Qin, Z., Shi, K., Zhang, Z., Shambanna, S., Xue, Z., Zou, T., et al. (2026) · doi:10.2196/preprints.106505
Pi, Z., Cao, Y., Zhang, Y., Ma, Y., Cao, W., & Li, X. (2026) · Computers & Education · doi:10.1016/j.compedu.2026.105722
Çiğdem, H., & Öztürk, G. (2026) · Kuramsal Eğitimbilim · doi:10.30831/akukeg.1814155
Feedback in STEM Higher Education: A Systematic Literature Review
Pirzada, P., Chen, Y., & Chai, K. K. (2026) · Education Sciences · doi:10.3390/educsci16081205
Quality Assurance of Learning Assessments in Large Information Systems and Decision Analysis Courses
Ugray, Z., & Dunn, B. (2022) · Journal of Information Systems Education · doi:10.62273/bgwc4456
Zhang, T. (2026) · Learning and Motivation · doi:10.1016/j.lmot.2026.102322 · Cited by 1
Gatea, H. E., & Yapıcıoğlu, D. K. (2026) · Bartın University Journal of Faculty of Education · doi:10.14686/buefad.1799056
A Comparative Study of Active Learning Components in Working Angle Feedback-Based Scaling Practice
Park, S.-A., & Lee, J.-G. (2026) · Journal of Digital Contents Society · doi:10.9728/dcs.2026.27.7.2047
Türkmen, E., Türkmen, G., & Günaydın, Y. (2026) · Sustainability · doi:10.3390/su18168142
Automated Feedback in Grading: An SQL Clause-Driven Approach
Wanjiru, B., van Bommel, P., & Hiemstra, D. (2026) · Studies in systems, decision and control · doi:10.1007/978-3-032-23499-5_14
Ma, X., Wang, D., & Chen, G. (2026) · The Journal of Educational Research · doi:10.1080/00220671.2026.2710208
Azad, H. G., Guedidi, A., Poëllhuber, B., & Hurtut, T. (2026) · Applied Sciences · doi:10.3390/app16168128
Sun, J., Cai, H., Xie, G., & Han, B. (2026) · International Conference on Computer Science and Technologies in Education (CSTE) · doi:10.1109/cste69562.2026.11649709
Plante, P. (2026) · Springer eBooks · doi:10.1007/978-3-031-52643-5_95-1
Jiménez, C. P. T., Garay, J. P. P., Ordoñez, A. J. D. L. C., Huaco, M. J. A., & Sánchez, K. R. (2026) · Revista Vive · doi:10.33996/revistavive.v9i26.512
Lu, X., Lu, Y., Ren, S., & Zhang, D. (2026) · International Journal of Science Education · doi:10.1080/09500693.2026.2701381
Metadata
- Authors
- Van der Kleij, F. M., Feskens, R. C. W., & Eggen, T. J. H. M.
- Affiliations
- Cito Institute for Educational Measurement and University of Twente
- Cito Institute for Educational Measurement
- University of Twente (NL)
- Venue
- Review of Educational Research
- Published
- 2015-12
- Volume
- 85
- Issue
- 4
- Pages
- 475-511
- ISSN
- 0034-6543 (ISSN-L)
- 1935-1046
- OpenAlex
- W2110661236
- Cited by
- 714
- References
- 76
- Citations by year
- 2026: 91 · 2025: 96 · 2024: 89 · 2023: 73 · 2022: 71 · 2021: 74 · 2020: 64 · 2019: 61 · 2018: 39 · 2017: 31 · 2016: 19 · 2015: 4
- FWCI
- 155.08
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Review of Educational Research (publishedVersion)
- University of Twente Research Information (publishedVersion)
- University of Twente Research Information (submittedVersion)
- Topics
- Social Sciences › Education › Student Assessment and Feedback
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Keywords
- elaborated feedback, computer-assisted instruction, learning outcomes, feedback timing, immediate feedback, delayed feedback, higher-order learning, mathematics learning
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