Abstract
In this paper, we present the application of unsupervised learning techniques to automatically recognize behaviors that may be detrimental to learning during interaction with an Exploratory Learning Environment (ELE). First, we describe how we use the k-means clustering algorithm for off-line identification of learner groups with distinguishing interaction patterns who also show similar learning improvements with an ELE. We then discuss how a k-means on-line classifier, trained with the learner groups detected off-line, can be used for adaptive support in ELEs. We aim to show the value of a data-based approach for recognizing learners as an alternative to knowledge-based approaches that tend to be complex and time-consuming even for domain experts, especially in highly unstructured ELEs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Amershi, S., Arksey, N., Carenini, G., Conati, C., Mackworth, A., Maclaren, H., Poole, D.: Designing CIspace: Pedagogy and Usability in a Learning Environment for AI. Innovation and Technology in CS Education, 178–182 (2005)
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)
Beck, J.E. (ed.): Educational Data Mining: AAAI Workshop. WS-05-02 (2005)
Beck, J.E.: Engagement Tracing: Using Response Times to Model Student Disengagement. AI in Education, 88–95 (2005)
Bunt, A., Conati, C., Huggett, M., Muldner, K.: On Improving the Effectiveness of OLEs Through Tailored Support for Exploration. AI in Education (2001)
Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley-Interscience, NY (2001)
Gorniak, P.J., Poole, D.: Building a Stochastic Dynamic Model of Application Use. Uncertainty in Artificial Intelligence, 230–237 (2000)
Hundhausen, C.D., Douglas, S.A., Stasko, J.T.: A Meta-Study of Algorithm Visualization Effectiveness. J. Visual Languages and Computing 13(3), 259–290 (2002)
Hunt, E., Madhyastha, T.: Data Mining Patterns of Thought. In: Beck, J.E. (ed.) AAAI Workshop on Educational Data Mining (2005)
Poole, D., Mackworth, A., Goebel, R.: Computational Intelligence: A Logical Approach. Oxford University Press, New York (1998)
Stern, L., Markham, S., Hanewald, R.: You Can Lead a Horse to Water: How Students Really Use Pedagogical Software. Innovation and Technology in CS Ed., 246–250 (2005)
Talavera, L., Gaudioso, E.: Mining Student Data to Characterize Similar Behavior Groups In Unstructured Collaboration Spaces. In: Workshop on AI in CSCL, European Conference on Artificial Intelligence, pp. 17–23 (2004)
Vicente, K.J., Torenvliet, G.L.: The Earth is Spherical (p<0.05): Alternative Methods of Statistical Inference. Theoretical Issues in Ergonomics Science 1(3), 248–271 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Amershi, S., Conati, C. (2006). Automatic Recognition of Learner Groups in Exploratory Learning Environments. In: Ikeda, M., Ashley, K.D., Chan, TW. (eds) Intelligent Tutoring Systems. ITS 2006. Lecture Notes in Computer Science, vol 4053. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11774303_46
Download citation
DOI: https://doi.org/10.1007/11774303_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35159-7
Online ISBN: 978-3-540-35160-3
eBook Packages: Computer ScienceComputer Science (R0)