Abstract
Discovering dependencies between students’ responses and their level of mastering of a particular skill is very important in the process of developing intelligent tutoring systems. This work is an approach to attain a higher level of certainty while following students’ learning progress. Rough sets approximations are applied for assessing students understanding of a concept. Consecutive responses from each individual learner to automated tests are placed in corresponding rough sets approximations. The resulting path provides strong indication about the current level of learning outcomes.
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References
Aleven, V., Koedinger, K.R.: Limitations of Student Control: Do Student Know when they need help? In: Gauthier, G., Van Lehn, K., Frasson, C. (eds.) ITS 2000. LNCS, vol. 1839, pp. 292–303. Springer, Heidelberg (2000)
Baker, R.S., Corbett, A.T., Koedinger, K.R.: Detecting student misuse of intelligent tutoring systems. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 531–540. Springer, Heidelberg (2004)
Ferreira, U.: A Five-valued Logic and a System. Journal of Computer Science and Technology 4(3), 134–140 (2004)
Goodstein, R.L.: Boolean Algebra. Dover Publications, Mineola (2007)
Gradel, E., Otto, M., Rosen, E.: Undecidability results on two-variable logics. Archive of Mathematical Logic 38, 313–354 (1999)
Guzmà n, E., Conejo, R.: A model for student knowledge diagnosis through adaptive testing. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 12–21. Springer, Heidelberg (2004)
Harper, R.: Correcting computer-based assessments for guessing. Journal of Computer Assisted Learning 19, 2–8 (2003)
Immerman, N., Rabinovich, A., Reps, T., Sagiv, M., Yorsh, G.: The boundary between decidability and undecidability of transitive closure logics. In: Marcinkowski, J., Tarlecki, A. (eds.) CSL 2004. LNCS, vol. 3210. Springer, Heidelberg (2004)
Koedinger, K.R., McLaren, B.M., Roll, I.: A help-seeking tutor agent. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 227–239. Springer, Heidelberg (2004)
Marek, V.W., Truszczynski, M.: Contributions to the theory of rough sets. Fundamenta Informaticae 39(4), 389–409 (1999)
Mayo, M., Mitrovic, A.: Optimising ITS behaviour with Bayesian networks and decision theory. International Journal of Artificial Intelligence in Education 12, 124–153 (2001)
Park, C., Kim, M.: Development of a Level-Based Instruction Model in Web-Based Education. In: Luo, Y. (ed.) CDVE 2004. LNCS (LNAI), vol. 3190, pp. 215–221. Springer, Heidelberg (2004)
Pecheanu, E., Segal, C., Stefanescu, D.: Content modeling in Intelligent Instructional Environment. LNCS (LNAI), vol. 3190, pp. 1229–1234. Springer-, Heidelberg (2003)
Pawlak, Z.: Rough Sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishing, Dordrecht (1991)
Renkl, A.: Learning from worked-out examples: Instructional explanations supplement self-explanations. Learning and Instruction 12, 529–556 (2002)
Santos, C.T., Osòrio, F.S.: Integrating intelligent agents, user models, and automatic content categorization in virtual environment. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 128–139. Springer, Heidelberg (2004)
Schworm, S., Renkl, A.: Learning by solved example problems: Instructional explanations reduce self-explanation activity. In: Gray, W.D., Schunn, C.D. (eds.) Proceeding of the 24th Annual Conference of the Cognitive Science Society, pp. 816–821. Erlbaum, Mahwah (2002)
Whitesitt, J.E.: Boolean Algebra and Its Applications. Dover Publications, NewYork (1995)
Wood, D.: Scaffolding, contingent tutoring, and computer-supported learning. International Journal of Artificial Intelligence in Education 12, 280–292 (2001)
Yao, Y.Y.: Interval-set algebra for qualitative knowledge representation. In: Proceedings of the Fifth International Conference on Computing and information, pp. 370–374 (1993)
Apache HTTP Server Project, http://httpd.apache.org/
Python Programming Language, http://www.python.org/
SQLite, http://www.sqlite.org/
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Encheva, S., Tumin, S. (2010). Rough Sets Approximations for Learning Outcomes. In: Tomar, G.S., Grosky, W.I., Kim, Th., Mohammed, S., Saha, S.K. (eds) Ubiquitous Computing and Multimedia Applications. UCMA 2010. Communications in Computer and Information Science, vol 75. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13467-8_7
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DOI: https://doi.org/10.1007/978-3-642-13467-8_7
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