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.
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?
Providing an evaluative response soon after a student's attempt at task or step enhances robust learning efficiency.
“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)
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 · see citing works
Self-citationSelf-explanations: How students study and use examples in learning to solve problems
Chi, M. (1989) · Cognitive Science · doi:10.1016/0364-0213(89)90002-5 · Cited by 2,287 · see citing works
The Atomic Components of Thought
Anderson, J. R., & Lebiere, C. J. (2014) · Psychology Press eBooks · doi:10.4324/9781315805696 · Cited by 2,181 · see citing works
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 · see citing works
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 · see citing works
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 · 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
Feedback in Written Instruction
Kulhavy, R. W. (1977) · Review of Educational Research · doi:10.3102/00346543047002211 · Cited by 571 · cited 1× · see citing works
“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 · see citing works
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 · see citing works
Summary knowledge of results for skill acquisition: Support for the guidance hypothesis.
Schmidt, R. A., Young, D. E., Swinnen, S. P., & Shapiro, D. C. (1989) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037//0278-7393.15.2.352 · Cited by 404 · see citing works
Skill Acquisition and the LISP Tutor
Anderson, J. R., Conrad, F. G., & Corbett, A. T. (1989) · Cognitive Science · doi:10.1207/s15516709cog1304_1 · Cited by 385 · see citing works
Self-citationComputers and classroom culture
(1996) · Choice Reviews Online · doi:10.5860/choice.33-6430 · Cited by 347 · see citing works
Effective Tutoring Techniques: A Comparison of Human Tutors and Intelligent Tutoring Systems
Merrill, D. C., Reiser, B. J., Ranney, M., & Trafton, J. G. (1992) · Journal of the Learning Sciences · doi:10.1207/s15327809jls0203_2 · Cited by 298 · see citing works
Motivational techniques of expert human tutors: Lessons for the design of computer-based tutors.
Lepper, M. R., Woolverton, M., Mumme, D. L., & Gurtner, J. (1993) · Cited by 293 · see citing works
Computers and Classroom Culture
Schofield, J. W. (1995) · Cambridge University Press eBooks · doi:10.1017/cbo9780511571268 · Cited by 264 · see citing works
The atomic components of thought
Taatgen, N. (1999) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(98)01250-9 · Cited by 184 · see citing works
TEACHING KNOWLEDGE AND INTELLIGENT TUTORING
Sussex, R. (1992) · Computer Assisted Language Learning · doi:10.1080/0958822920050309 · Cited by 71 · see citing works
Teaching knowledge and intelligent tutoring
Goodyear, P. (1991) · Cited by 70 · see citing works
Cognitive and interactional aspects of correction in tutoring
Fox, B. A. (1991) · Cited by 57 · see citing works
Instruction intrusiveness in dynamic simulation training
Munro, A., Fehling, M., & Towne, D. M. (1985) · The Journal of Computer Based Instruction · Cited by 41 · see citing works
Instructional interventions in computer-based tutoring
Corbett, A. T., & Trask, H. J. (2000) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/332040.332412 · Cited by 33 · see citing works
Self-citationAnderson, J. R., Corbett, A. T., & Reiser, B. J. (1986) · Cited by 24 · see citing works
Self-citation
Flagged first, then by citations
1 of the 24 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Shute, V. J. (2008) · Review of Educational Research · doi:10.3102/0034654307313795 · Cited by 4,631 · see citing works
VanLEHN, K. (2011) · Educational Psychologist · doi:10.1080/00461520.2011.611369 · Cited by 2,323 · see citing works
Current practice in measuring usability: Challenges to usability studies and research
Hornbæk, K. (2006) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2005.06.002 · Cited by 1,123 · see citing works
Effectiveness of Intelligent Tutoring Systems: A Meta-Analytic Review
Kulik, J. A., & Fletcher, J. D. (2016) · Review of Educational Research · doi:10.3102/0034654315581420 · Cited by 930 · see citing works
Koedinger, K. R., Corbett, A. T., & Perfetti, C. (2012) · Cognitive Science · doi:10.1111/j.1551-6709.2012.01245.x · Cited by 772 · see citing works
“…KCs (e.g., perceptual categories, simple concepts and skills) may imply instructional approaches that emphasize recall (Roediger & Karpicke, 2006), spacing of practice (Cepeda et al., 2006), tutored practice (e.g., Corbett & Anderson, 2001), and optimized scheduling of practice (Pavlik, 2007).”
BackgroundSelf-citationBuilding Intelligent Interactive Tutors : student-centered strategies for revolutionizing e-learning
Woolf, B. P. (2009) · HAL · Cited by 716 · see citing works
Schwartz, D. L., & Martin, T. (2004) · Cognition and Instruction · doi:10.1207/s1532690xci2202_1 · Cited by 763 · see citing works
Shute, V. J. (2007) · ETS Research Report Series · doi:10.1002/j.2333-8504.2007.tb02053.x · Cited by 467 · see citing works
Teaching Johnny not to fall for phish
Kumaraguru, P., Sheng, S., Acquisti, A., Cranor, L. F., & Hong, J. (2010) · ACM Transactions on Internet Technology · doi:10.1145/1754393.1754396 · Cited by 399 · see citing works
Roll, I., Aleven, V., McLaren, B. M., & Koedinger, K. R. (2010) · Learning and Instruction · doi:10.1016/j.learninstruc.2010.07.004 · Cited by 386 · see citing works
A systematic literature review of automated feedback generation for programming exercises
Keuning, H., Jeuring, J., & Heeren, B. (2019) · ACM Transactions on Computing Education · doi:10.1145/3231711 · Cited by 370 · see citing works
Data-Driven Hint Generation in Vast Solution Spaces: a Self-Improving Python Programming Tutor
Rivers, K., & Koedinger, K. R. (2015) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-015-0070-z · Cited by 258 · see citing works
Cognitive Computer Tutors: Solving the Two-Sigma Problem
Corbett, A. T. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44566-8_14 · Cited by 241 · see citing works
Self-citationOnline Mathematics Homework Increases Student Achievement
Roschelle, J., Feng, M., Murphy, R. F., & Mason, C. A. (2016) · AERA Open · doi:10.1177/2332858416673968 · Cited by 267 · see citing works
Blended Learning: How Teachers Balance the Blend of Online and Classroom Components
Jeffrey, L. M., Milne, J., Suddaby, G., & Higgins, A. (2014) · Journal of Information Technology Education Research · doi:10.28945/1968 · Cited by 237 · see citing works
Zhu, M., Liu, O. L., & Lee, H.-S. (2020) · Computers & Education · doi:10.1016/j.compedu.2019.103668 · Cited by 229 · see citing works
Garcia, R., Falkner, K. E. K., & Vivian, R. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.05.006 · Cited by 221 · see citing works
Booth, J. L., Lange, K. E., Koedinger, K. R., & Newton, K. J. (2012) · Learning and Instruction · doi:10.1016/j.learninstruc.2012.11.002 · Cited by 205 · see citing works
Safety implications of providing real-time feedback to distracted drivers
Donmez, B., Boyle, L. N., & Lee, J. D. (2006) · Accident Analysis & Prevention · doi:10.1016/j.aap.2006.10.003 · Cited by 203 · see citing works
Fostering the Intelligent Novice: Learning From Errors With Metacognitive Tutoring
Mathan, S. A., & Koedinger, K. R. (2005) · Educational Psychologist · doi:10.1207/s15326985ep4004_7 · Cited by 197 · see citing works
Teaching Introductory Programming
Koulouri, T., Lauria, S., & Macredie, R. D. (2014) · ACM Transactions on Computing Education · doi:10.1145/2662412 · Cited by 182 · see citing works
Motivation and performance in a game-based intelligent tutoring system.
Jackson, G. T., & McNamara, D. S. (2013) · Journal of Educational Psychology · doi:10.1037/a0032580 · Cited by 186 · see citing works
Spanning seven orders of magnitude: a challenge for cognitive modeling
Anderson, J. R. (2002) · Cognitive Science · doi:10.1207/s15516709cog2601_3 · Cited by 175 · see citing works
Self-citationPatchan, M. M., Schunn, C. D., & Correnti, R. J. (2016) · Journal of Educational Psychology · doi:10.1037/edu0000103 · Cited by 165 · see citing works
Instructional Scaffolding in STEM Education
Belland, B. R. (2017) · doi:10.1007/978-3-319-02565-0 · Cited by 161 · see citing works
CogSketch: Sketch Understanding for Cognitive Science Research and for Education
Forbus, K. D., Usher, J., Lovett, A., Lockwood, K., & Wetzel, J. (2011) · Topics in Cognitive Science · doi:10.1111/j.1756-8765.2011.01149.x · Cited by 157 · see citing works
Modeling self-efficacy in intelligent tutoring systems: An inductive approach
McQuiggan, S. W., Mott, B., & Lester, J. C. (2007) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-007-9040-y · Cited by 149 · see citing works
Price, T., Dong, Y., & Lipovac, D. (2017) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3017680.3017762 · Cited by 140 · see citing works
Interactive electronic storybooks for kindergartners to promote vocabulary growth.
Smeets, D., & Bus, A. (2012) · Journal of Experimental Child Psychology · doi:10.1016/j.jecp.2011.12.003 · Cited by 150 · see citing works
Chi, M., VanLehn, K. A., Litman, D. J., & Jordan, P. (2011) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-010-9093-1 · Cited by 135 · see citing works
Active Learning is About More Than Hands-On: A Mixed-Reality AI System to Support STEM Education
Yannier, N., Hudson, S. E., & Koedinger, K. (2020) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-020-00194-3 · Cited by 128 · see citing works
Student – Feedback Interaction Model: Revised
Lipnevich, A. A., & Smith, J. K. (2022) · Studies in Educational Evaluation · doi:10.1016/j.stueduc.2022.101208 · Cited by 125 · see citing works
Marwan, S., Gao, G., Fisk, S. R., Price, T., & Barnes, T. (2020) · International Computing Education Research Workshop · doi:10.1145/3372782.3406264 · Cited by 131 · see citing works
Designing for metacognition—applying cognitive tutor principles to the tutoring of help seeking
Roll, I., Aleven, V., McLaren, B. M., & Koedinger, K. R. (2007) · Metacognition and Learning · doi:10.1007/s11409-007-9010-0 · Cited by 118 · see citing works
A Quasi-Experimental Evaluation of An On-Line Formative Assessment and Tutoring System
Koedinger, K. R., McLaughlin, E. A., & Heffernan, N. T. (2010) · Journal of Educational Computing Research · doi:10.2190/ec.43.4.d · Cited by 115 · see citing works
Improving Students' Evaluation of Informal Arguments
Larson, A. A., Britt, M. A., & Kurby, C. A. (2009) · The Journal of Experimental Education · doi:10.3200/jexe.77.4.339-366 · Cited by 112 · see citing works
Translating Principles of Effective Feedback for Students into the CS1 Context
Ott, C., Robins, A. V., & Shephard, K. L. (2016) · ACM Transactions on Computing Education · doi:10.1145/2737596 · Cited by 106 · see citing works
Shute, V. J., Hansen, E. G., & Almond, R. G. (2008) · doi:10.5555/1497124.1497126 · Cited by 100 · see citing works
Intelligent Tutoring and Games (ITaG)
McNamara, D. S., Jackson, G. T., & Graesser, A. C. (2010) · Advances in game-based learning book series · doi:10.4018/978-1-61520-713-8.ch003 · Cited by 100 · see citing works
L2 Writing Practice: Game Enjoyment as a Key to Engagement
Allen, L. K., Crossley, S. A., Snow, E. L., & McNamara, D. S. (2014) · Language learning & technology · doi:10.64152/10125/44373 · Cited by 102 · see citing works
Muis, K. R., Ranellucci, J., Trevors, G., & Duffy, M. C. (2015) · Learning and Instruction · doi:10.1016/j.learninstruc.2015.02.001 · Cited by 93 · see citing works
A Cognitive Tutor for Genetics Problem Solving: Learning Gains and Student Modeling
Corbett, A. T., Kauffman, L. R., MacLaren, B., Wagner, A. Z., & Jones, E. (2010) · Journal of Educational Computing Research · doi:10.2190/ec.42.2.e · Cited by 83 · see citing works
Self-citationArguel, A., Lockyer, L., Kennedy, G. E., Lodge, J. M., & Pachman, M. (2018) · Interactive Learning Environments · doi:10.1080/10494820.2018.1457544 · Cited by 83 · see citing works
Building a Comprehensive Automated Programming Assessment System
Mekterović, I., Brkić, L., Milašinović, B., & Baranović, M. (2020) · IEEE Access · doi:10.1109/access.2020.2990980 · Cited by 74 · see citing works
Kegel, C. A. T., Bus, A. G., & van IJzendoorn, M. H. (2011) · Mind Brain and Education · doi:10.1111/j.1751-228x.2011.01112.x · Cited by 72 · see citing works
Impact of interruption style on end-user debugging
Robertson, T. J., Prabhakararao, S., Burnett, M., Cook, C. R., Ruthruff, J. R., Beckwith, L., & Phalgune, A. (2004) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/985692.985729 · Cited by 73 · see citing works
Moreno, R., Martin Reisslein, B., & Özoğul, G. (2009) · Journal of Engineering Education · doi:10.1002/j.2168-9830.2009.tb01007.x · Cited by 71 · see citing works
Attali, Y. (2015) · Computers & Education · doi:10.1016/j.compedu.2015.08.011 · Cited by 70 · see citing works
Correia, A., Koehler, N. A., Thompson, A. D., & Phye, G. D. (2018) · Research in Science & Technological Education · doi:10.1080/02635143.2018.1487834 · Cited by 76 · see citing works
Factors Influencing Students' Help-Seeking Behavior while Programming with Human and Computer Tutors
Price, T. W., Liu, Z., Cateté, V., & Barnes, T. (2017) · Proceedings of the 2017 ACM Conference on International Computing Education Research · doi:10.1145/3105726.3106179 · Cited by 61 · see citing works
Feedback adaptation in web-based learning systems
Vasil'yeva, E., Puuronen, S., Pechenizkiy, M., & Räsänen, P. (2007) · International Journal of Continuing Engineering Education and Life-Long Learning · doi:10.1504/ijceell.2007.015046 · Cited by 65 · see citing works
Pardos, Z. A., Tang, M., Anastasopoulos, I., Sheel, S. K., & Zhang, E. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581574 · Cited by 62 · see citing works
(1992) · Nursing Standard · doi:10.7748/ns.6.42.18.s39 · Cited by 56 · see citing works
Confounding parameters on program comprehension: a literature survey
Siegmund, J., & Schumann, J. (2014) · Empirical Software Engineering · doi:10.1007/s10664-014-9318-8 · Cited by 55 · see citing works
Saadawi, G. M. E., Azevedo, R., Castine, M., Payne, V. L., Medvedeva, O., Tseytlin, E., Legowski, E. A., Jukic, D. M., & Crowley, R. S. (2009) · Advances in Health Sciences Education · doi:10.1007/s10459-009-9162-6 · Cited by 57 · see citing works
An experimental study of the impact of visual semantic feedback on novice programming
Hundhausen, C. D., & Brown, J. L. (2006) · Journal of Visual Languages & Computing · doi:10.1016/j.jvlc.2006.09.001 · Cited by 54 · see citing works
Recasting the feedback debate: benefits of tutoring error detection and correction skills
Mathan, S. A., & Koedinger, K. R. (2003) · Cited by 54 · see citing works
Hint Generation Under Uncertainty: The Effect of Hint Quality on Help-Seeking Behavior
Price, T., Zhi, R., & Barnes, T. M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-61425-0_26 · Cited by 53 · see citing works
Koedinger, K. R., Aleven, V., Roll, I., & Baker, R. S. (2009) · doi:10.4324/9780203876428.ch20 · Cited by 53 · see citing works
An Empirical Assessment of Comprehension Fostering Features in an Intelligent Tutoring System
Mathan, S. A., & Koedinger, K. R. (2002) · Lecture notes in computer science · doi:10.1007/3-540-47987-2_37 · Cited by 53 · see citing works
An Evaluation of the Impact of Automated Programming Hints on Performance and Learning
Marwan, S., Williams, J. J., & Price, T. (2019) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3291279.3339420 · Cited by 52 · see citing works
Posterlet: A Game-Based Assessment of Children's Choices to Seek Feedback and to Revise
Cutumisu, M., Blair, K. P., Chin, D. B., & Schwartz, D. L. (2015) · Journal of Learning Analytics · doi:10.18608/jla.2015.21.4 · Cited by 50 · see citing works
Dimova, S. (2017) · English for Specific Purposes · doi:10.1016/j.esp.2016.12.004 · Cited by 49 · see citing works
Gusukuma, L., Bart, A. C., Kafura, D. G., & Ernst, J. V. (2018) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3230977.3231002 · Cited by 47 · see citing works
Kumar, H., Леонидович, М. И., Reza, M., Shi, J., Wang, X., Williams, J. J., Kuzminykh, A., & Liut, M. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3687038 · Cited by 84 · see citing works
Perikos, I., Grivokostopoulou, F., & Hatzilygeroudis, I. (2017) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-017-0139-y · Cited by 44 · see citing works
Online tutoring as a pivotal quality of web-based early literacy programs.
Kegel, C. A. T., & Bus, A. G. (2011) · Journal of Educational Psychology · doi:10.1037/a0025849 · Cited by 44 · see citing works
Adding Physical Objects to an Interactive Game Improves Learning and Enjoyment
Yannier, N., Hudson, S. E., Wiese, E. S., & Koedinger, K. R. (2016) · ACM Transactions on Computer-Human Interaction · doi:10.1145/2934668 · Cited by 42 · see citing works
A Comparison of the Quality of Data-Driven Programming Hint Generation Algorithms
Price, T., Dong, Y., Zhi, R., Paaßen, B., Lytle, N., Cateté, V., & Barnes, T. M. (2019) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-019-00177-z · Cited by 42 · see citing works
Unproductive Help-seeking in Programming: What it is and How to Address it
Marwan, S., Dombe, A., & Price, T. (2020) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3341525.3387394 · Cited by 41 · see citing works
Generating Data-Driven Hints for Open-Ended Programming.
Price, T., Dong, Y., & Barnes, T. M. (2016) · Educational Data Mining · Cited by 41 · see citing works
Adaptive Immediate Feedback for Block-Based Programming: Design and Evaluation
Marwan, S., Akram, B., Barnes, T. M., & Price, T. (2022) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2022.3180984 · Cited by 44 · see citing works
Efficacy of Adaptive Feedback Strategies in Simulation-Based Training
Billings, D. R. (2012) · Military Psychology · doi:10.1080/08995605.2012.672905 · Cited by 40 · see citing works
Collaboration in cognitive tutor use in latin America
Ogan, A., Walker, E., Baker, R. S., Rebolledo‐Mendez, G., Jiménez-Castro, M., Laurentino, T., & de Carvalho, A. (2012) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2207676.2208597 · Cited by 40 · see citing works
Using GPT-4 to Provide Tiered, Formative Code Feedback
Nguyen, H., & Allan, V. H. (2024) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3626252.3630960 · Cited by 39 · see citing works
Goal‐setting, Feedback, and Assessment Practices Reported by Australian Clinical Supervisors
Gonsalvez, C. J., Wahnon, T., & Deane, F. P. (2016) · Australian Psychologist · doi:10.1111/ap.12175 · Cited by 39 · see citing works
Shute, V. J., Hansen, E. G., & Almond, R. G. (2007) · ETS Research Report Series · doi:10.1002/j.2333-8504.2007.tb02068.x · Cited by 39 · see citing works
Am I Wrong, or Is the Autograder Wrong? Effects of AI Grading Mistakes on Learning
Li, T. W., Hsu, S., Fowler, M., Zhang, Z., Zilles, C. B., & Karahalios, K. G. (2023) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3568813.3600124 · Cited by 38 · see citing works
The Impact of Adding Textual Explanations to Next-step Hints in a Novice Programming Environment
Marwan, S., Lytle, N., Williams, J. J., & Price, T. (2019) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3304221.3319759 · Cited by 38 · see citing works
Pol, H. J., Harskamp, E. G., Suhre, C. J. M., & Goedhart, M. J. (2008) · Journal of Science Education and Technology · doi:10.1007/s10956-008-9110-x · Cited by 38 · see citing works
Automated tutoring for a database skills training environment
Kenny, C., & Pahl, C. (2005) · ACM SIGCSE Bulletin · doi:10.1145/1047124.1047377 · Cited by 38 · see citing works
Abdullah, Z. B. (2014) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2014.04.082 · Cited by 41 · see citing works
Design of Visualizations for Human-Information Interaction: A Pattern-Based Framework
Sedig, K., & Parsons, P. (2016) · Synthesis lectures on visualization · doi:10.2200/s00685ed1v01y201512vis005 · Cited by 35 · see citing works
The Role of Rubrics in Learning and Implementation of Authentic Assessment: A Literature Review
Nkhoma, C., Nkhoma, M. Z., Thomas, S., & Le, N. Q. (2020) · Informing Science and IT Education Conference · doi:10.28945/4606 · Cited by 37 · see citing works
The Impacts of Three Educational Technologies on Algebraic Understanding in the Context of COVID-19
Decker, L. E., Mason, C. A., Lee, J.-E., Chan, J. Y., Sales, A., Liu, A. S., & Tu, S. (2023) · AERA Open · doi:10.1177/23328584231165919 · Cited by 39 · see citing works
Using Tutors to Improve Educational Games
Easterday, M. W., Aleven, V., Scheines, R., & Carver, S. M. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21869-9_11 · Cited by 34 · see citing works
CogSketch: Sketch Understanding for Cognitive Science Research and for Education
Forbus, K. D. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14749-4_2 · Cited by 33 · see citing works
AI Adaptivity in a Mixed-Reality System Improves Learning
Yannier, N., Hudson, S. E., Chang, H., & Koedinger, K. R. (2024) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00388-5 · Cited by 33 · see citing works
Ahmed, U. Z., Srivastava, N., Sindhgatta, R., & Karkare, A. (2020) · IEEE/ACM International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET) · doi:10.1145/3377814.3381703 · Cited by 33 · see citing works
Fowler, M., Smith, D. H., Hassan, M. R., Poulsen, S., West, M., & Zilles, C. B. (2022) · Computer Science Education · doi:10.1080/08993408.2022.2079866 · Cited by 32 · see citing works
Tan, J., & Biswas, G. (2006) · Lecture notes in computer science · doi:10.1007/11774303_37 · Cited by 32 · see citing works
Babaali, P., & Gonzalez, L. (2015) · International Journal of Mathematical Education in Science and Technology · doi:10.1080/0020739x.2014.997318 · Cited by 32 · see citing works
Providing immediate feedback improves recycling and composting accuracy
Luo, Y., Zelenika, I., & Zhao, J. (2018) · Journal of Environmental Management · doi:10.1016/j.jenvman.2018.11.061 · Cited by 32 · see citing works
Aleven, V. A. W. M. M. (2013) · Springer international handbooks of education · doi:10.1007/978-1-4419-5546-3_21 · Cited by 31 · see citing works
Payne, V. L., Medvedeva, O., Legowski, E. A., Castine, M., Tseytlin, E., Jukic, D. M., & Crowley, R. S. (2009) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2009.07.002 · Cited by 32 · see citing works
Supporting Effective Interaction with Tabletop Groupware
Morris, M. R. (2006) · IEEE International Workshop on Horizontal Interactive Human-Computer Systems · doi:10.1109/tabletop.2006.29 · Cited by 30 · see citing works
Automated tutoring for a database skills training environment
Kenny, C., & Pahl, C. (2005) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/1047344.1047377 · Cited by 30 · see citing works
Cutumisu, M., Blair, K. P., Chin, D. B., & Schwartz, D. L. (2016) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-016-0137-5 · Cited by 29 · see citing works
Intelligent science exhibits: Transforming hands-on exhibits into mixed-reality learning experiences
Yannier, N., Crowley, K., Do, Y., Hudson, S. E., & Koedinger, K. (2022) · The Journal of the Learning Sciences · doi:10.1080/10508406.2022.2032071 · Cited by 24 · see citing works
TraceDiff: Debugging unexpected code behavior using trace divergences
Suzuki, R., Soares, G., Head, A., Glassman, E. L., Reis, R., Mongiovi, M., D’Antoni, L., & Hartmann, B. (2017) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vlhcc.2017.8103457 · Cited by 28 · see citing works
- 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
