More Web Proxy on the site http://driver.im/

Article

Learning factors analysis – a general method for cognitive model evaluation and improvement

Authors:

Kenneth Koedinger,

Brian JunkerAuthors Info & Claims

ITS'06: Proceedings of the 8th international conference on Intelligent Tutoring Systems

Pages 164 - 175

https://doi.org/10.1007/11774303_17

Published: 26 June 2006 Publication History

Abstract

A cognitive model is a set of production rules or skills encoded in intelligent tutors to model how students solve problems. It is usually generated by brainstorming and iterative refinement between subject experts, cognitive scientists and programmers. In this paper we propose a semi-automated method for improving a cognitive model called Learning Factors Analysis that combines a statistical model, human expertise and a combinatorial search. We use this method to evaluate an existing cognitive model and to generate and evaluate alternative models. We present improved cognitive models and make suggestions for improving the intelligent tutor based on those models.

References

[1]

Baffes P., Mooney R J.: A Novel Application of Theory Refinement to Student Modeling. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI- 96). Portland OR (1996) 403-408

Digital Library

[2]

Barnes T.: The Q-matrix Method: Mining Student Response Data for Knowledge. In: Proceedings of AAAI 2005 Educational Data Mining Workshop. (2005)

[3]

Cen, H, Koedinger, K, Junker B.: Automating Cognitive Model Improvement by A*Search and Logistic Regression. In: Proceedings of AAAI 2005 Educational Data Mining Workshop. (2005)

[4]

Corbett A. T., Anderson, J. R., O'Brien A. T.: Student Modelling in the ACT Programming Tutor. In: Cognitively Diagnostic Assessment. Erlbaum, Hillsdale, NJ (1995)

[5]

Corbett, A. T. and Anderson, J. R.: Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4. (1995) 253-278.

[6]

Croteau E. A., Heffernan N. T., Koedinger K. R.: Why are Algebra Word Problem Difficult? Using Tutoring Log Files and the Power Law of Learning to Select the Best Fitting Cognitive Model. In: Proceedings of Intelligent Tutoring Systems. (2004)

[7]

Draney, K., Pirolli, P., & Wilson, M.: A Measurement Model for a Complex Cognitive Skill. In: Cognitively Diagnostic Assessment. Erlbaum, Hillsdale, NJ (1995)

[8]

Junker B. W., Koedinger, K., Trottini M.: Finding Improvements in Student Models for Intelligent Tutoring Systems via Variable Selection for a Linear Logistic Test Model., presented at annual meeting of Psychometric Society Vancouver BC (2000)

[9]

Koedinger K. R.: http://pact.cs.cmu.edu/koedinger/koedingerResearch.html. (2000)

[10]

Koedinger K. R., Mathan S.: Distinguishing Qualitatively Different Kinds of Leaning Using Log Files and Learning Curves. In: Proc. of Intelligent Tutoring Systems (2004)

[11]

Koedinger, K. R., Nathan, M. J.: The Real Story Behind Story Problems: Effects Of Representations On Quantitative Reasoning. The Journal of the Learning Sciences, 13 (2). (2004) 129-164.

[12]

Koedinger K. R., Anderson, J. R., Hadley W. H., Mark M. A.: Intelligent Tutoring Goes to School in the Big City. In: Proceedings of the 7th World Conference on Art, Intelligence and Education, AACE. (1995)

[13]

Newell, A., Rosenbloom, P.: Mechanisms of Skill Acquisition and the Law of Practice. In Anderson J. (ed.): Cognitive Skills and Their Acquisition, Erlbaum Hillsdale NJ (1981)

[14]

Russell S., Norvig P., Artificial Intelligence, 2nd edn. Prentice Hall (2003)

Digital Library

[15]

Tatsuoka, K.: Rule space: An approach for dealing with misconceptions based on item response theory. Journal of Educational Measurement. 20(4) (1983) 345-354.

[16]

Ur, S., VanLehn, K.: STEPS: A Simulated, Tutorable Physics Student. Journal of Artificial Intelligence in Education. 6(4) (1995) 405-437

Digital Library

[17]

Wasserman, L.: All of Statistics: A Concise Course in Statistical Inference. Springer (2004)

Digital Library

Cited By

Sun JDu SZhou JYuan XShen XLiang R(2024)Question Embedding on Weighted Heterogeneous Information Network for Knowledge TracingACM Transactions on Knowledge Discovery from Data10.1145/370315819:1(1-28)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3703158
Qian LLuo X(2024)A Double-layer Stacked Gate Recurrent Unit with Self-Attention Residual Model for Knowledge TracingProceedings of the 2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Education Digitalization and Computer Science10.1145/3686424.3686454(171-176)Online publication date: 26-Jul-2024
https://dl.acm.org/doi/10.1145/3686424.3686454
Shen XYu FLiu YLiang RWan QYang KSun JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Revisiting Knowledge Tracing: A Simple and Powerful ModelProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681205(263-272)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681205
Show More Cited By

Recommendations

Intelligent Improvement in Throughput of Cognitive Radio

Under the energy detection scheme based cognitive radio (CR) system, the process of spectrum sensing is of high importance. The sensing performance of CR primarily depends on two important parameters namely, the sensing time ź and the reference ...
Analysis of Cognitive Radio Networks with Imperfect Sensing and Backup Channels
IMIS '13: Proceedings of the 2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing

We study the performance of cognitive radio networks with imperfect spectrum sensing and backup channels (BCs). The cognitive radio network is an opportunistic spectrum access (OSA) network which consists of a primary system serving primary users (PUs) ...
Analysis of QoS Provisioning in Cognitive Radio Networks: A Case Study

Dynamic Spectrum Access (DSA) is a promising solution to the problem of spectrum inefficiency. Based on Cognitive Radio (CR) technology, DSA allows a CR device to opportunistically access unused or less crowded spectrum while ensuring protection for the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Guide Proceedings

ITS'06: Proceedings of the 8th international conference on Intelligent Tutoring Systems

June 2006

817 pages

ISBN:3540351590

Editors:
Mitsuru Ikeda
JAIST, 1-1, Asahi-dai, Nomi, Ishikawa, Japan
,
Kevin D. Ashley
Learning Research and Development Center, University of Pittsburgh, 1-1, Asahi-dai, Nomi, Pittsburgh, PA, Japan
,
Tak-Wai Chan
Graduate Institute of Network Learning Technology, National Central University, 300, Jhongda Rd., Jhongli City,Taoyuan County, PA, Taiwan

Sponsors

Ministry of Education, China: Ministry of Education of Republic of China
TAAI: Taiwanese Association for Artificial Intelligence
National Program of Science and Technology for e-Learning, Taiwan
National Science Council: National Science Council (Taiwan)
Taipei City Government, Taiwan

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 26 June 2006

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

111
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Sun JDu SZhou JYuan XShen XLiang R(2024)Question Embedding on Weighted Heterogeneous Information Network for Knowledge TracingACM Transactions on Knowledge Discovery from Data10.1145/370315819:1(1-28)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3703158
Qian LLuo X(2024)A Double-layer Stacked Gate Recurrent Unit with Self-Attention Residual Model for Knowledge TracingProceedings of the 2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Education Digitalization and Computer Science10.1145/3686424.3686454(171-176)Online publication date: 26-Jul-2024
https://dl.acm.org/doi/10.1145/3686424.3686454
Shen XYu FLiu YLiang RWan QYang KSun JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Revisiting Knowledge Tracing: A Simple and Powerful ModelProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681205(263-272)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681205
Moore SSchmucker RMitchell TStamper JJoyner DKim MWang XXia M(2024)Automated Generation and Tagging of Knowledge Components from Multiple-Choice QuestionsProceedings of the Eleventh ACM Conference on Learning @ Scale10.1145/3657604.3662030(122-133)Online publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1145/3657604.3662030
Linhao ZShenghua ZZhijiao XGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)Discovering Multi-Relational Integration for Knowledge Tracing with Retentive NetworksProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658030(960-968)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3658030
Lee MSiedahmed AHeffernan N(2024)Expert Features for a Student Support Recommendation Contextual Bandit AlgorithmProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636909(864-870)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3636555.3636909
Demirtaş MFowler MHu NCunningham K(2024)Validating, Refining, and Identifying Programming Plans Using Learning Curve Analysis on Code Writing DataProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671120(263-279)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3632620.3671120
Zhao SSahebi SBoratto LGena CMarras MGermanakos PPopescus E(2024)Towards Multi-Objective Behavior and Knowledge Modeling in StudentsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664880(183-188)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664880
Zhao SSahebi SSerra ESpezzano F(2024)Multi-Task Modeling of Student Knowledge and BehaviorProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679823(3363-3373)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679823
Guo YShen SLiu QHuang ZZhu LSu YChen ESerra ESpezzano F(2024)Mitigating Cold-Start Problems in Knowledge Tracing with Large Language Models: An Attribute-aware ApproachProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679664(727-736)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679664
Show More Cited By

View Options

View options

Media

Figures

Other

Tables

View Table of Contents