“Learning factors analysis-a general method for cognitive model evaluation and improvement,”
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Traditional approaches for cognitive modeling in knowledge tracing include factor analysis methods which estimate student knowledge by learning a function (logistic in most cases) based on various factors related to the students, course materials, learning and forgetting behavior.
“Traditional approaches for cognitive modeling include factor analysis methods which estimate student knowledge by learning a function (logistic in most cases) based on various factors related to the students, course materials, learning and forgetting behavior, etc. (Pavlik and Anderson, 2005; Cen et al., 2006; Pavlik et al., 2009).”
From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as Cen et al., 2006
Cites
Artificial intelligence: a modern approach
Kumar, D. A., Balasubramanian, S., Byeon, D. H., & Manerkar, P. G. V. (1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 22,182 · see citing works
Cognitive Skills and Their Acquisition
Anderson, J. R. (2013) · Psychology Press eBooks · doi:10.4324/9780203728178 · Cited by 2,926 · see citing works
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
Mechanisms of skill acquisition and the law of practice
Newell, A., & Rosenbloom, P. S. (2018) · Figshare · doi:10.1184/r1/6607196 · Cited by 1,501 · see citing works
Intelligent Tutoring Goes To School in the Big City
Koedinger, K. R., Anderson, J. R., Hadley, W. H., & Mark, M. A. (2018) · doi:10.1184/r1/6470153 · Cited by 1,105 · see citing works
Self-citationAll of Statistics: A Concise Course in Statistical Inference
Wasserman, L. (2004) · Springer Texts in Statistics · doi:10.1007/978-0-387-21736-9 · Cited by 1,090 · see citing works
RULE SPACE: AN APPROACH FOR DEALING WITH MISCONCEPTIONS BASED ON ITEM RESPONSE THEORY
Tatsuoka, K. K. (1983) · Journal of Educational Measurement · doi:10.1111/j.1745-3984.1983.tb00212.x · Cited by 935 · see citing works
Anderson, J. R., Boyle, C., & Reiser, B. (1985) · Science · doi:10.1126/science.228.4698.456 · Cited by 699 · see citing works
All Of Statistic A Concise Course In Statistical Inference
Waserman, L. (2003) · Cited by 652 · see citing works
Cognitively Diagnostic Assessment
Nichols, P. D., Chipman, S. F., & Brennan, R. L. (2012) · Routledge eBooks · doi:10.4324/9780203052969 · Cited by 491 · see citing works
The Real Story Behind Story Problems: Effects of Representations on Quantitative Reasoning
Koedinger, K. R., & Nathan, M. J. (2004) · Journal of the Learning Sciences · doi:10.1207/s15327809jls1302_1 · Cited by 431 · see citing works
Self-citationJournal of Artificial Intelligence in Education
(1991) · Intelligent Tutoring Media · doi:10.1080/14626269109408288 · Cited by 223 · see citing works
(1992) · Cited by 154 · see citing works
The Q-matrix Method: Mining Student Response Data for Knowledge
Barnes, T. M. (2005) · Cited by 151 · see citing works
Journal of Artificial Intelligence in Education, 4(2/3)
(1993) · Intelligent Tutoring Media · doi:10.1080/14626269309408329 · Cited by 58 · see citing works
STEPS: a simulated, tutorable physics student
Ur, S., & VanLehn, K. A. (1995) · Cited by 35 · see citing works
Croteau, E. A., Heffernan, N. T., & Koedinger, K. R. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30139-4_23 · Cited by 33 · see citing works
Self-citationA novel application of theory refinement to student modeling
Baffes, P. T., & Mooney, R. J. (1996) · Cited by 29 · see citing works
Malmkjaer, K. (2003) · Routledge eBooks · doi:10.4324/9780203432860-12 · Cited by 19 · see citing works
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New Potentials for Data‐Driven Intelligent Tutoring System Development and Optimization
Koedinger, K. R., Brunskill, E., Baker, R. S. J. D., McLaughlin, E. A., & Stamper, J. (2013) · AI Magazine · doi:10.1609/aimag.v34i3.2484 · Cited by 178 · see citing works
Self-citationProactive and reactive engagement of artificial intelligence methods for education: a review
Mallik, S., & Gangopadhyay, A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1151391 · Cited by 139 · see citing works
Human-Machine Student Model Discovery and Improvement Using DataShop
Stamper, J. C., & Koedinger, K. R. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-21869-9_46 · Cited by 58 · see citing works
Self-citationThe SIETTE Automatic Assessment Environment
Conejo, R., Guzmán, E., & Trella, M. (2016) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-015-0078-4 · Cited by 34 · see citing works
Learning Cognitive Models Using Neural Networks
Chaplot, D. S., MacLellan, C., Salakhutdinov, R., & Koedinger, K. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-93843-1_4 · Cited by 11 · see citing works
Self-citationGálvez, J., Conejo, R., & Guzmán, E. (2013) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-013-0008-2 · Cited by 6 · see citing works
- Authors
- Cen, H., Koedinger, K., & Junker, B.
- Affiliations
- Carnegie Mellon University (US)
- ORCID
- Koedinger, Kenneth
- Venue
- Lecture notes in computer science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2006
- Pages
- 164-175
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1562092080
- Cited by
- 572
- References
- 19
- Citations by year
- 2026: 25 · 2025: 36 · 2024: 48 · 2023: 46 · 2022: 47 · 2021: 39 · 2020: 51 · 2019: 31 · 2018: 40 · 2017: 27 · 2016: 26 · 2015: 29 · 2014: 25 · 2013: 19 · 2012: 19
- FWCI
- 6.95
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Keywords
- intelligent tutoring systems, production rules, model improvement, combinatorial search, statistical models, human expertise
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