Abstract
One of the common expectations of ITS designers is that students efficiently learn from every practice opportunity. However, when students are using an Intelligent Tutoring System, they can exhibit a variety of behaviors, such as “gaming,” which can strongly reduce learning. In this paper, we present a new approach to infer the impact of gaming on learning at the fine-grained level. We integrated a knowledge tracing model of the student’s knowledge with the student’s gaming state (as identified by our gaming detector). We found that when gaming, students learn almost nothing (on the order of one-twelfth to one-fiftieth as efficiently). A student’s gaming amount is associated with aggregate effects on his knowledge and learning, leading to less learning even in the practice opportunities where no gaming occurs. In addition, we found that students tend to game in those skills on which they have relatively low knowledge. Furthermore, we found that knowing the identity of the studentis more important than knowing the skill for predicting whether gaming will occur.
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References
Aleven, V., McLaren, B.M., Roll, I., Koedinger, K.R.: Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 227–239. Springer, Heidelberg (2004)
Arroyo, I., Woolf, B.: Inferring learning and attitudes from a Bayesian Network of log file data. In: Proceedings of the 12th International Conference on Artificial Intelligence in Education, pp. 33–40 (2005)
Baker, R.S., Corbett, A.T., Koedinger, K.R., Wagner, A.Z.: Off-Task Behavior in the Cognitive Tutor Classroom: When Students “Game The System”. In: Proceedings of ACM CHI 2004: Computer-Human Interaction, pp. 383–390 (2004)
Stevens, R., Soller, A., Cooper, M., Sprang, M.: Modeling the Development of Problem-Solving Skills in Chemistry with a Web-Based Tutor. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 580–591. Springer, Heidelberg (2004)
Walonoski, J., Heffernan, N.: Detection and analysis of off-task gaming behavior in intelligent tutoring systems. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 382–391. Springer, Heidelberg (2006)
Cocea, M., Hershkovitz, A., Baker, R.S.J.d.: The Impact of Off-task and Gaming Behaviors on Learning: Immediate or Aggregate? In: Proceedings of the 14th International Conference on Artificial Intelligence in Education, pp. 507–514 (2009)
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)
Corbett, A., Anderson, J.: Knowledge tracing: Modeling the acquisition of procedural knowledge. In: User Modeling and User-Adapted Interaction, pp. 253–278 (1995)
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)
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)
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)
Baker, R.S.J.d., de Carvalho, A.M.J.A.: Labeling Student Behavior Faster and More Precisely with Text Replays. In: Proceedings of the 1st International Conference on Educational Data Mining, pp. 38–47 (2008)
Baker, R.S.J.d.: Differences between Intelligent Tutor Lessons, and the Choice to Go Off-Task. In: Proceedings of the 2nd International Conference on Educational Data Mining, pp. 11–20 (2009)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. (2003)
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Gong, Y., Beck, J.E., Heffernan, N.T., Forbes-Summers, E. (2010). The Fine-Grained Impact of Gaming (?) on Learning. In: Aleven, V., Kay, J., Mostow, J. (eds) Intelligent Tutoring Systems. ITS 2010. Lecture Notes in Computer Science, vol 6094. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13388-6_24
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DOI: https://doi.org/10.1007/978-3-642-13388-6_24
Publisher Name: Springer, Berlin, Heidelberg
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