Comparing knowledge tracing and performance factor analysis by using multiple model fitting procedures

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6094 LNCS, Issue PART 1, 2010, Pages 35-44

  Gong, Yue ^a   Beck, Joseph E ^a   Heffernan, Neil T ^a  

^a Worcester Polytechnic Institute   (United States)

Author keywords

Expectation maximization; Knowledge tracing; Machine learning; Model fitting approaches; Performance factors analysis; Student modeling

Indexed keywords

EXPECTATION MAXIMIZATION; KNOWLEDGE TRACING; MACHINE LEARNING; MODEL FITTING APPROACHES; PERFORMANCE FACTORS ANALYSIS; STUDENT MODELING;

ALGORITHMS; COMPUTER AIDED INSTRUCTION; MAXIMUM PRINCIPLE; STUDENTS;

PARAMETER ESTIMATION;

EID: 79957446210     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13388-6_8     Document Type: Conference Paper

References (12)

1. Beck, J.E.: Difficulties in inferring student knowledge from observations (and why you should care). In: Proceedings of the AIED 2007 Workshop on Educational Data Mining, Marina del Rey, CA, pp. 21-30 (2007).
2. Corbett, A., Anderson, J.: Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction 4, 253-278 (1995).
3. Beck, J.E., Chang, K.-m.: Identifiability: A Fundamental Problem of Student Modeling. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 137- 146. Springer, Heidelberg (2007). (Pubitemid 350270976)
4. Rai, D., Gong, Y., Beck, J.E.: Using Dirichlet priors to improve model parameter plausibility. In: Proceedings of the 2nd International Conference on Educational Data Mining, Cordoba, Spain, pp. 141-148 (2009).
5. Pavlik, P.I., Cen, H., Koedinger, K.: Performance Factors Analysis - A New Alternative to Knowledge. In: Proceedings the 14th International Conference on Artificial Intelligence in Education, Brighton, UK, pp. 531-538 (2009).
6. Beck, J.E., Mostow, J.: How who should practice: Using learning decomposition to evaluate the efficacy of different types of practice for different types of students. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 353-362. Springer, Heidelberg (2008).
7. Cen, H., Koedinger, K., Junker, B.: Learning Factors Analysis - A General Method for Cognitive Model Evaluation and Improvement. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 164-175. Springer, Heidelberg (2006). (Pubitemid 44117589)
8. Chang, K.-m., Beck, J., Mostow, J., Corbett, A.: A Bayes Net Toolkit for Student Modeling in Intelligent Tutoring Systems. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 104-113. Springer, Heidelberg (2006). (Pubitemid 44117583)
9. Pardos, Z.A., Beck, J., Ruiz, C., Heffernan, N.T.: The Composition Effect: Conjunctive or Compensatory? An Analysis of Multi-Skill Math Questions in ITS. In: Baker, Beck (eds.) Proceedings of the First International Conference on Educational Data Mining, Montreal, Canada, pp. 147-156 (2008).
10. Heathcote, A., Brown, S., Mewhort, D.J.K.: The Power Law repealed: The case for an Exponential Law of Practice. Psychonomic Bulletin & Review (2002).
11. Baker, R.: Personal communication (2010).
12. Ioannidis, J.P.A.: Why Most Published Research Findings Are False. PLoS Med. 2(8), e124 (2005).