Using hidden markov models to characterize student behaviors in learning-by-teaching environments

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

Volumn 5091 LNCS, Issue , 2008, Pages 614-625

  Jeong, Hogyeong ^a   Gupta, Amit ^a   Roscoe, Rod ^a   Wagster, John ^a   Biswas, Gautam ^a   Schwartz, Daniel ^b  

^a VANDERBILT UNIVERSITY   (United States)

^b STANFORD UNIVERSITY   (United States)

Author keywords

Behavior Analysis; Hidden Markov modeling; Learning by Teaching environments; Metacognition

Indexed keywords

COMPUTER AIDED ANALYSIS; COMPUTER AIDED INSTRUCTION; E-LEARNING; INTELLIGENT VEHICLE HIGHWAY SYSTEMS; LEARNING SYSTEMS; STUDENTS;

ANALYSIS TECHNIQUES; BEHAVIOR ANALYSIS; COMPUTER-BASED LEARNING; HIDDEN MARKOV MODELS (HMMS); LEARNING BY TEACHING; METACOGNITION; STUDENT BEHAVIOR; STUDENTS LEARNING;

HIDDEN MARKOV MODELS;

EID: 67449109631     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-69132-7_64     Document Type: Conference Paper

References (14)

1. Biswas, G., Leelawong, K., Schwartz, D., Vye, N.: TAG-V: Learning By Teaching: A New Agent Paradigm for Educational Software. In: Applied Artificial Intelligence. AAI, vol.19, pp. 363-392 (2005)
2. Wagster, J., Tan, J., Wu, Y., Biswas, G., Schwartz, D.: Do Learning by Teaching Environments with Metacognitive Support Help Students Develop Better Learning Behaviors? In: The 29th Annual Meeting of the Cognitive Science Society, Nashville, pp. 695-700 (2007)
3. Novak, J.D.: Learning, Creating, and Using Knowledge: Concept Maps as Facilitative Tools in Schools and Corporations, LEA, Mahwah, NJ (1998)
4. Roscoe, R.D., Chi, M.: Understanding tutor learning: Knowledge-building and knowledgetelling in peer tutors' explanations and questions. Review of Educational Research. 4, 534-574 (2007)
5. Biswas, G., Schwartz, D., Bransford, J.: TAG-V: Technology support for complex problem solving: From SAD environments to AI. In: Forbus, K.D., Feltovich, P.J. (eds.) Smart Machines in Education, pp. 71-98. AAAI Press, Menlo Park (2001)
6. Roscoe, R.D., Chi, M.: Tutor learning: The role of instructional explaining and responding to questions. In: Instructional Science (in press)
7. Chi, M.T.H., Siler, S.A., Jeong, H., Yamauchi, T., Hausmann, R.G.: Learning from Human Tutoring. Cognitive Science 25(4), 471-533 (2001)
8. Lin, X., Schwartz, D.L., Hatano, G.: Toward Teachers' Adaptive Metacognition. Educational Psychologist 40(4), 245-255 (2005)
9. Leelawong, K., Biswas, G.: Designing Learning by Teaching Systems: The Betty's Brain System. International Journal of Artificial Intelligence in Education (2008)
10. Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proc. IEEE, 77 (1989)
11. Azevedo, R.: Using Hypermedia as a Metacognitive Tool for Enhancing Student Learning? The Role of Self-Regulated Learning. Educational Psychologist 40(4), 199-209 (2005)
12. Zimmerman, B.J.: A Social Cognitive View of Self-Regulated Academic Learning. Journal of Educational Psychology 81(3), 329-339 (1989)
13. Li, C., Biswas, G.: A Bayesian Approach for Learning Hidden Markov Models from Data. Special issue on Markov Chain and Hidden Markov Models. Scientific Programming 10, 201-219 (2002)
14. Fisher, C., Sanderson, P.: Exploratory sequential data analysis: exploring continuous observational data. Interactions 3(2), 25-34 (1996)