Abstract
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.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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)
VanLehn, K.: The Behavior of Tutoring Systems. International Journal of Artificial Intelligence in Education 16, 227–265 (2006)
Shute, V.J., Ventura, M., Kim, Y.J.: Assessment and Learning of Qualitative Physics in Newton’s Playground. The Journal of Educational Research 106, 423–430 (2013)
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)
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)
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)
Roll, I., Aleven, V., McLaren, B.M., Koedinger, K.R.: Improving students’ help-seeking skills using metacognitive feedback in an intelligent tutoring system. Learning and Instruction 21, 267–280 (2011)
Gong, Y., Beck, J.E., Ruiz, C.: Modeling Multiple Distributions of Student Performances to Improve Predictive Accuracy. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds.) UMAP 2012. LNCS, vol. 7379, pp. 102–113. Springer, Heidelberg (2012)
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)
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)
Wieman, C.E., Adams, W.K., Perkins, K.K.: PhET: Simulations That Enhance Learning. Science 322, 682–683 (2008)
De Jong, T., Van Joolingen, W.R.: Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research 68, 179–201 (1998)
Kardan, S.: Data mining for adding adaptive interventions to exploratory and open-ended environments. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds.) UMAP 2012. LNCS, vol. 7379, pp. 365–368. Springer, Heidelberg (2012)
Zhang, C., Zhang, S.: Association rule mining: Models and algorithms. Springer, Heidelberg (2002)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: An update. ACM SIGKDD Explorations Newsletter 11, 10–18 (2009)
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)
Milligan, G.W., Cooper, M.C.: An examination of procedures for determining the number of clusters in a data set. Psychometrika 50, 159–179 (1985)
Rousseeuw, P.J.: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics 20, 53–65 (1987)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Kardan, S., Roll, I., Conati, C. (2014). The Usefulness of Log Based Clustering in a Complex Simulation Environment. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds) Intelligent Tutoring Systems. ITS 2014. Lecture Notes in Computer Science, vol 8474. Springer, Cham. https://doi.org/10.1007/978-3-319-07221-0_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-07221-0_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07220-3
Online ISBN: 978-3-319-07221-0
eBook Packages: Computer ScienceComputer Science (R0)