The DOI matches its Crossref record; 22 of the 24 references it cites were found in a registry or an index.
Locus of feedback control in computer-based tutoring: impact on learning rate, achievement and attitudes
Abstract
The advent of second-generation intelligent computer tutors raises an important instructional design question: when should tutorial advice be presented in problem solving? This paper examines four feedback conditions in the ACT Programming Tutor. Three versions offer the student different levels of control over error feedback and correction: (a) immediate feedback and immediate error correction; (b) immediate error flagging and student control of error correction; (c) feedback on demand and student control of error correction. A fourth, No-tutor condition offers no stepby-step problem solving support. The immediate feedback group with greatest tutor control of problem solving yielded the most efficient learning. These students completed the tutor problems fastest, and the three tutor-supported groups performed equivalently on tests. Questionnaires revealed little student preference among the four conditions. These results suggest that students will need explicit guidance to benefit from learning opportunities that arise when they have greater control over tutorial assistance.
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
“Providing an evaluative response (e.g., correct or incorrect) soon after a students' attempt at task or step”
From The Knowledge‐Learning‐Instruction Framework: Bridging the Science‐Practice Chasm to Enhance Robust Student Learning (2012) · cites this paper as Corbett & Anderson (2001)
Citations
Cites
Knowledge tracing: Modeling the acquisition of procedural knowledge
Corbett, A. T., & Anderson, J. R. (1995) · User Modelling and User-Adapted Interaction · doi:10.1007/bf01099821 · Cited by 2,324
Self-citationThe Atomic Components of Thought
Anderson, J. R., & Lebiere, C. J. (2014) · Psychology Press eBooks · doi:10.4324/9781315805696 · Cited by 2,181
Self-citationCognitive Tutors: Lessons Learned
Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995) · Journal of the Learning Sciences · doi:10.1207/s15327809jls0402_2 · Cited by 1,982
Self-citationSelf‐Explanations: How Students Study and Use Examples in Learning to Solve Problems
Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989) · Cognitive Science · doi:10.1207/s15516709cog1302_1 · Cited by 1,714
The Atomic Components of Thought
Taatgen, N. (1999) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(98)01250-9 · Cited by 1,482
The instructional effect of feedback in test-like events.
Bangert-Drowns, R. L., Kulik, C.-L. C., Kulik, J. A., & Morgan, M. (1991) · Review of Educational Research · doi:10.3102/00346543061002213 · Cited by 1,313
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
Feedback in Written Instruction
Kulhavy, R. W. (1977) · Review of Educational Research · doi:10.3102/00346543047002211 · Cited by 571 · cited 1×
“Overall, feedback timing has little consistent impact on learning, although delayed feedback can facilitate retention [5,11,12].”
BackgroundBielaczyc, K., Pirolli, P. L., & Brown, A. L. (1995) · Cognition and Instruction · doi:10.1207/s1532690xci1302_3 · Cited by 422
Summary knowledge of results for skill acquisition: Support for the guidance hypothesis.
Schmidt, R. A., Young, D. E., Swinnen, S., & Shapiro, D. C. (1989) · Journal of Experimental Psychology: Learning, Memory, and Cognition · doi:10.1037/0278-7393.15.2.352 · Cited by 407
Flagged first, then by citations
2 of the 24 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Pressey , S.L. ( 1926 ). A simple apparatus which gives tests and scores - and teaches . School and Society , 23 ( 586 ), 373 - 376 . Pressey, S.L. (1926). A simple apparatus which gives tests and scores - and teaches. School and Society, 23 (586), 373-376.
- Schooler , L.J. and Anderson , J.R . ( 1990 ). The disruptive potential of immediate feedback . The Proceedings of the Twelfth Annual Conference of the Cognitive Science Society , Cambridge, MA. Schooler, L.J. and Anderson, J.R. (1990). The disruptive potential of immediate feedback. The Proceedings of the Twelfth Annual Conference of the Cognitive Science Society, Cambridge, MA.
Cited by
Sorted by citations
Items 201–280 of 280
Ekwam, E. O., Emmanuella, H. S., Okebukola, P. A., Nkrumah, F., & Oladejo, A. I. (2025) · Discover Education · doi:10.1007/s44217-025-01037-w · Cited by 5
Pagliano, O., Brown, W. E., Rule, G. S., & Bajzek, D. (2007) · E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education · Cited by 3
Using a Non-player Character to Improve Training Outcomes for Submarine Electronic Warfare Operators
Schroeder, B. L., Fraulini, N. W., Buskirk, W. L. V., & Johnson, C. I. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50788-6_39 · Cited by 3
Clemente, V. Á., Gil, L., Gilabert, R., Mañá, A., & Gámez, E. V.-A. (2016) · Universitas Psychologica · doi:10.11144/javeriana.upsy15-1.meda · Cited by 3
Buskirk, W. L. V. (2011) · Journal of International Crisis and Risk Communication Research · Cited by 3
i-Prolog: A Web-Based Intelligent Tutoring System for Learning Prolog
Stathaki, A., Kondylakis, H., Marakakis, E., & Kalogerakis, M. A. (2018) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-76908-0_32 · Cited by 4
Marwan, S., Shi, Y., Menezes, I., Chi, M., Barnes, T. M., & Price, T. (2021) · Educational Data Mining · Cited by 3
Generation of Demand Feedback in Intelligent Tutors for Programming
Kumar, A. N. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24840-8_35 · Cited by 3
Hepburn, C. Y. (2025) · British Journal of Clinical Pharmacology · doi:10.1002/bcp.70302 · Cited by 2
Sunday, O. J., Adesope, O., Dai, S., & Carbonneau, K. J. (2025) · Learning and Motivation · doi:10.1016/j.lmot.2025.102201 · Cited by 2
Yannier, N., Li, J., Chang, H., Pavlinek, S., Yim, M., Hudson, S. E., & Koedinger, K. R. (2025) · Journal of Science Education and Technology · doi:10.1007/s10956-025-10282-5 · Cited by 2
Александрович, С. О. (2022) · IEEE/ACM International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET) · doi:10.1109/icse-seet55299.2022.9794157 · Cited by 2
Error-Flagging Support and Higher Test Scores
Kumar, A. N. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21869-9_21 · Cited by 2
Goldberg, B. (2013) · Journal of International Crisis and Risk Communication Research · doi:10.13140/rg.2.1.4911.7609 · Cited by 2
Multimedia Learning with Media
(2021) · Cambridge University Press eBooks · doi:10.1017/9781108894333.045 · Cited by 2
Александрович, С. О. (2022) · IEEE/ACM International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET) · doi:10.1145/3510456.3514159 · Cited by 2
Cost-Effectiveness of Algebraic Technological Applications
Finster, M., Decker, L. E., Booker, B., Mason, C. A., Tu, S., & Lee, J. (2023) · Journal of Research on Educational Effectiveness · doi:10.1080/19345747.2023.2269918 · Cited by 3
Knowledge Spillover, Trust, Effort, and Error Exposure in Peer‐Assisted Learning
Juvina, I., Carson, J., Menke, P., Crowe, P., Wong, C. H., & McNett, H. (2025) · Topics in Cognitive Science · doi:10.1111/tops.70009 · Cited by 3
The Effect of Providing Error-Flagging Support During Testing
Kumar, A. N. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69132-7_112 · Cited by 2
An Experimental Study of the Impact of Feedback Self-Selection on Novice Programming
Hundhausen, C. D., & Brown, J. L. (2006) · Cited by 2
Developing affect-aware robot tutors
Spaulding, S. (2015) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 2
An Infrastructure to Support Usability Problem Data Analysis
Howarth, J. R. (2004) · VTechWorks (Virginia Tech) · Cited by 2
Using Automatic Item Generation to Create Content for Computerized Formative Assessment
Xinxin, Z. (2019) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r3-vbt3-6598 · Cited by 2
The Effects of Error-Flagging in a Tutor on Expression Evaluation
Kumar, A. N., & Rutigliano, P. (2007) · Cited by 2
ALPS: Bringing Active Inquiry into Active Problem Solving.
Stevens, S., Corbett, A. T., Koedinger, K. R., Wagner, A. Z., Chao, C.-Y., Ulrich, H., & Lesgold, S. (2004) · Cited by 2
Self-citationChen, L., & Dubrawski, A. W. (2018) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3231644.3231697 · Cited by 1
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
YILMAZ, N., & Birişçi, S. (2026) · Education and Information Technologies · doi:10.1007/s10639-025-13825-w · Cited by 1
Feedback Timing: The Impact on Undergraduates’ Statistics Anxiety, Self-Efficacy and Perceptions
Jones, A., Wood, J. S., McDonald, E., & Abbott, K. (2026) · Psychology Learning & Teaching · doi:10.1177/14757257261427206 · Cited by 1
How to utilize a classroom network to support teacher feedback in statistics education
Tolboom, J. (2013) · Utrecht University Repository (Utrecht University) · doi:10.13140/rg.2.1.3480.9364 · Cited by 1
de Abreu, F. M., & Barbosa, Á. M. (2022) · International Journal of Creative Interfaces and Computer Graphics · doi:10.4018/ijcicg.308808 · Cited by 1
Corrias, A., Choy, J. L. F., Soong, S. K. A., & Gan, M. J. S. (2016) · ASCILITE Publications · doi:10.14742/apubs.2016.868 · Cited by 1
ALBAYRAK, H. B., & Güzel, E. B. (2024) · Pamukkale University Journal of Education · doi:10.9779/pauefd.1290603 · Cited by 1
The Effect of Repetition Feedback on Success Rate and Uptake
Lagatie, R., & Causmaecker, P. D. (2010) · Lirias · Cited by 1
Steif, P. S., Eicholtz, M., & Kara, L. B. (2014) · Journal of Visual Languages & Computing · doi:10.1016/j.jvlc.2014.10.012 · Cited by 1
Context of Use of Computer-Based Scaffolding
Belland, B. R. (2016) · Springer eBooks · doi:10.1007/978-3-319-02565-0_3 · Cited by 1
Kim, K. G., Oertel, C., & Dillenbourg, P. (2021) · International Journal for Research in Vocational Education and Training · doi:10.13152/ijrvet.8.1.4 · Cited by 1
Marwan, S. (2021) · NCSU Libraries Repository (North Carolina State University Libraries) · Cited by 1
Automatic item generation for online measurement and evaluation: Turkish literature items
SAYIN, A., & Gierl, M. J. (2023) · International Journal of Assessment Tools in Education · doi:10.21449/ijate.1249297 · Cited by 1
News Presenter skills evaluation using multi-modality and machine learning
Emam, A. M., Elgarh, M. A., Fahmy, A., Moniem, A. A., & Atia, A. (2023) · Intelligent Methods, Systems, and Applications (IMSA) · doi:10.1109/imsa58542.2023.10217597 · Cited by 1
An Adaptive and Customizable Feedback System for VR- Based Training Simulators (Short Paper)
Lopez-Garate, M., Lardizabal, M., Lozano-Rodero, A., & Matey, L. (2008) · Cited by 1
The Computational and Educational Viability of Deploying Intelligent Tutoring Systems
Kildare, R. (2003) · UTAS Research Repository · doi:10.25959/23212352 · Cited by 1
Doing more than teaching students: opportunities for CALL in the learning sciences
Wylie, R., Mitamura, T., Koedinger, K. R., & Rankin, J. (2007) · ISCA Workshop on Speech and Language Technology in Education (SLaTE) · doi:10.21437/slate.2007-4 · Cited by 1
Roll, I., Aleven, V. A. W. M. M., & Koedinger, K. R. (2010) · International Conference of Learning Sciences
Scaffolding Problem Solving with Embedded Examples to Promote Deep Learning
Ringenberg, M. A. (2007) · D-Scholarship@Pitt (University of Pittsburgh)
Brown, J. (2005) · Australasian Journal of Paramedicine
Orchestrating Biology Instruction
Daniel, E. G. S. (2012) · SensePublishers eBooks · doi:10.1007/978-94-6091-927-5_32
A Learning Environment for Synchronous Tutorial Advising
Aymerich, F. M., Dessì, N., Saba, A. F., & Cifra, C. (2002)
Intelligent Educational Systems for Teaching Programming
Qiu, L. (2004)
The case for error detection support during online testing
Kumar, A. N. (2010) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/1822090.1822188
Kelley, C. M. (2014) · NCSU Libraries Repository (North Carolina State University Libraries)
Zeinolabedini, M., & Gholami, J. (2014) · Mediterranean Journal of Humanities · doi:10.13114/mjh.201428450
AC 2012-3478: RELATING USAGE OF WEB-BASED LEARNING MATE- RIALS TO LEARNING PROGRESS
Steif, P. S., & Dollár, A. (2012)
Kalvelage, K. J., & Dorneich, M. C. (2014)
Leveraging Help Requests In Pomdp Intelligent Tutors
Folsom-Kovarik, J. T. (2012) · Journal of International Crisis and Risk Communication Research
Exploring Differential Levels of Feedback in Digital Learning Objects
Sumner, T. M. (2017) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r3k35ms19
A Model for Competence E-Assessment and Feedback in Higher Education
González‐Marcos, A., Nalda, F. N., Ordieres-Meré, J. B., & Alba‐Elías, F. (2018) · Advances in higher education and professional development book series · doi:10.4018/978-1-5225-5936-8.ch012
Reaching More Students: A Web-based Intelligent Tutoring System with support for Offline Access
Kehrer, P. H. (2012) · Digital WPI
Sugatan, C. (2020) · Deep Blue (University of Michigan)
Eken, D. T. (2021) · i-manager’s Journal on English Language Teaching · doi:10.26634/jelt.11.2.17593
Powerful Learning Conversations
Rienzo, C., Rolfe, H., & Wilkinson, D. (2016) · University of Brighton Repository (University of Brighton) · doi:10.13140/rg.2.1.4393.6886
Kim, K.-H., & Lee, M.-J. (2021) · Journal of Curriculum and Evaluation · doi:10.29221/jce.2021.24.3.27
Multimedia Learning with Cognitive Tutors
Koedinger, K. R., & Aleven, V. A. W. M. M. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108894333.046
CLP: A Platform for Competitive Learning
Vats, A., Guzun, G., & Anastasiu, D. C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16290-9_58
CareerCraft: Crafting Your Future, One Code at a Time
Patil, S., Dugad, R., Gile, A., Patil, G., & Hatyalikar, S. (2025) · OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry (OTCON) · doi:10.1109/otcon65728.2025.11070607
(A)I Can Play Gomoku: An Intelligent Tutoring System for Strategic Games
Zhang, Q., Wang, C., & MacLellan, C. J. (2025) · Annual Symposium on Computer-Human Interaction in Play · doi:10.1145/3744736.3749191
Sun, J. C., Lin, T.-Y., Hsieh, P.-H., & Chang, W.-L. (2025) · Journal of Educational Computing Research · doi:10.1177/07356331251399685
Formative Feedback for Students in Problem-Based Learning
Lorenzo, S. (2025) · Springer eBooks · doi:10.1007/978-3-032-05405-0_7
Thomas, R., John, M., Pinder, A. R., Staros, M., Brandon, L., Sofia, P., Marcia, M., & JAMES, F. (2025) · i-manager’s Journal on Educational Psychology · doi:10.26634/jet.22.2.21713
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
Diagnostic comportemental et cognitif des erreurs dans la résolution de problèmes arithmétiques
Martin, B. (2016) · theses.fr (ABES) · doi:10.70675/411833edz01d6z4036z9ddfzb844a7ea3baf
Learning to read : new remediation strategies and underlying cognitive mechanisms
Andreola, C. (2023) · theses.fr (ABES) · doi:10.70675/5c762d33zbc28z4a74zb9daz2e6c21738de6
Asher, M. W., Wei, Y., Reynolds, A., Ogan, A., & Carvalho, P. F. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791885 · Cited by 1
Co-designing AI-mediated Paired Reading for Children in Swedish Classrooms
Viberg, O., Larsson, P., Hedlin, E., & Engwall, O. (2026) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3773077.3812154 · Cited by 1
Artificial Intelligence in Special Education and Inclusive Learning
Omar, K., Fakhouri, H. N., & Hamad, F. F. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-9330-8.ch005
Paired Reading in Swedish with AI: Co-Designing for Enhanced Reading Engagement
Viberg, O., Engwall, O., & Larsson, P. (2026) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3829807.3829857
Importance of Lecture Feedback in H2 Biology Lectures in a Junior College
Foo-Lam, W. K., Poh, M. L., & Soh, Y. P. (2012) · SensePublishers eBooks · doi:10.1007/978-94-6091-927-5_31
Metadata
- Authors
- Corbett, A. T., & Anderson, J. R.
- Affiliations
- Carnegie Mellon University (US)
- ORCID
- Anderson, John R.
- Venue
- Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2001-03
- Pages
- 245-252
- OpenAlex
- W2105543722
- Cited by
- 280
- References
- 24
- Citations by year
- 2026: 7 · 2025: 13 · 2024: 5 · 2023: 12 · 2022: 15 · 2021: 14 · 2020: 13 · 2019: 15 · 2018: 14 · 2017: 14 · 2016: 20 · 2015: 10 · 2014: 15 · 2013: 10 · 2012: 17
- FWCI
- 2.67
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Keywords
- computer-based tutoring, intelligent tutoring systems, feedback control, error feedback, error correction, programming education, student control, immediate feedback, learning efficiency, student preferences
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