The Usefulness of Log Based Clustering in a Complex Simulation Environment
Data mining techniques have been successfully employed on user interaction data in exploratory learning environments. In this paper we investigate using data mining techniques for analyzing student behaviors in an especially-complex exploratory environment, with over one hundred possible actions at any given point. Furthermore, the outcomes of these actions depend on their context. We propose a multi-layer action-events structure to deal with the complexity of the data and employ clustering and rule mining to examine student behaviors in terms of learning performance and effects of different degrees of scaffolding. Our findings show that using the proposed multi-layer structure for describing action-events enables the clustering algorithm to effectively identify the successful and unsuccessful students in terms of learning performance across activities in the presence or absence of external scaffolding. We also report and discuss the prominent behavior patterns of each group and investigate short term effects of scaffolding.
KeywordsEducational Data Mining Clustering Scaffolding
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- 1.Koedinger, K.R., Corbett, A.T.: Cognitive tutors: Technology bringing learning science to the classroom. In: The Cambridge Handbook of the Learning Sciences, pp. 61–78 (2006)Google Scholar
- 2.VanLehn, K.: The Behavior of Tutoring Systems. International Journal of Artificial Intelligence in Education 16, 227–265 (2006)Google Scholar
- 4.Gobert, J.D., Pedro, M.A.S., Baker, R.S.J.d., Toto, E., Montalvo, O.: Leveraging Educational Data Mining for Real-time Performance Assessment of Scientific Inquiry Skills within Microworlds. JEDM - Journal of Educational Data Mining 4, 111–143 (2012)Google Scholar
- 5.Leelawong, K., Biswas, G.: Designing Learning by Teaching Agents: The Betty’s Brain System. International Journal of Artificial Intelligence in Education 18, 181–208 (2008)Google Scholar
- 6.Roll, I., Aleven, V., Koedinger, K.R.: The Invention Lab: Using a Hybrid of Model Tracing and Constraint-Based Modeling to Offer Intelligent Support in Inquiry Environments. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010, Part I. LNCS, vol. 6094, pp. 115–124. Springer, Heidelberg (2010)CrossRefGoogle Scholar
- 9.Shih, B., Koedinger, K.R., Scheines, R.: Unsupervised Discovery of Student Strategies. In: Proceedings of the 3rd Intl. Conf. on Educational Data Mining, pp. 201–210 (2010)Google Scholar
- 10.Kardan, S., Conati, C.: A Framework for Capturing Distinguishing User Interaction Behaviours in Novel Interfaces. In: Proc. of the 4th Int. Conf. on Educational Data Mining, Eindhoven, The Netherlands, pp. 159–168 (2011)Google Scholar
- 16.Kardan, S., Conati, C.: Comparing and Combining Eye Gaze and Interface Actions for Determining User Learning with an Interactive Simulation. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds.) UMAP 2013. LNCS, vol. 7899, pp. 215–227. Springer, Heidelberg (2013)CrossRefGoogle Scholar
- 19.Roll, I., Yee, N., Briseno, A.: Students’ Adaptation and Transfer of Strategies Across Levels of Scaffolding in an Exploratory Environment. In: Proc. of the 12th Intl. Conf. on Intelligent Tutoring Systems (2014)Google Scholar