Temporal data mining for educational applications

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

Volumn 5351 LNAI, Issue , 2008, Pages 66-77

  Beal, Carole R ^a   Cohen, Paul R ^a  

^a UNIVERSITY OF ARIZONA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING COMMUNITIES; DATA MINING TECHNIQUES; DATA SETS; DISTANCE METRICS; EDUCATIONAL APPLICATIONS; HIDDEN STATE VARIABLES; INTELLIGENT TUTORING SYSTEMS; LEARNING OUTCOMES; STUDENT BEHAVIORS; TEMPORAL DATA MININGS; TESTING HYPOTHESIS; UNOBSERVABLE;

ARTIFICIAL INTELLIGENCE; BIONICS; COMPUTER AIDED INSTRUCTION; EDUCATION; INFORMATION MANAGEMENT;

DATA MINING;

EID: 58349093203     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-89197-0_10     Document Type: Conference Paper

References (11)

1. (Retrieved July 8, 2006), http://www.ed.gov/nclb/landing.jhtml
2. Beal, C.R., Qu, L., Lee, H.: Classifying learner engagement through integration of multiple data sources. In: Proceedings of the 21st National Conference on Artificial Intelligence. AAAI Press, Menlo Park (2006)
3. Koedinger, K.R., Corbett, A.T., Ritter, S., Shapiro, L.J.: Carnegie Learnings Cognitive Tutor: Summary of research results. Carnegie Learning, Pittsburgh (2000)
4. Baker, R.S., Corbett, A.T., Koedinger, K.R., Roll, I.: Detecting when students game the system, across tutor subjects and classroom cohorts, In: Ardissono, L., Brna, P., Mitrović, A. (eds.) UM 2005. LNCS (LNAI), vol. 3538, pp. 220-224, Springer, Heidelberg (2005)
5. Beck, J.: Engagement tracing: Using response times to model student disengagement. In: Looi, C., McCalla, C., Bredeweg, B., Breuker, J. (eds.) Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology, pp. 88-95. IOS Press, Amsterdam (2005)
6. Stevens, R., Johnson, D., Soller, A.: Probabilities and prediction: Modeling the development of scientific problem solving skills. Cell Biology Education 4, 42-57 (2005)
7. Sutton, D., Cohen, P.R.: Very predictive Ngrams for space-limited probabilistic models. In: Pfenning, F., et al. (eds.) Advances in intelligent data analysis V, pp. 134-142. Springer, Berlin (2003)
8. Beal, C.R., Mitra, S., Cohen, P.R.: Modeling learning patterns of students with a tutoring system using Hidden Markov Models. In: Proceedings of the 13th International Conference on Artificial Intelligence in Education (AIED). Rev, CA (2006) (July 2007)
9. Ramoni, M., Sebastiani, P., Cohen, P.R.: Bayesian clustering by dynamics. Machine Learning 47, 91-121 (2001)
10. Beal, C.R., Cohen, P.R.: Computational methods for evaluating student and group learning histories in intelligent tutoring systems. In: Looi, C., McCalla, G., Bredeweg, B., Breuker, J. (eds.) Artificial Intelligence in Education: Supporting learning through intelligent and socially-informed technology, pp. 80-87. IOS Press, Amsterdam (2005)
11. Cohen, P.R.: Empirical methods for artificial intelligence. MIT Press, Cambridge (1995)