Combining Unsupervised and Supervised Classification to Build User Models for Exploratory Learning Environments

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Published Oct 1, 2009
Saleema Amershi Cristina Conati

Abstract

In this paper, we present a data-based user modeling framework that uses both unsupervised and supervised classification to build student models for exploratory learning environments. We apply the framework to build student models for two different learning environments and using two different data sources (logged interface and eye-tracking data). Despite limitations due to the size of our datasets, we provide initial evidence that the framework can automatically identify meaningful student interaction behaviors and can be used to build user models for the online classification of new student behaviors online. We also show framework transferability across applications and data types.

How to Cite

Amershi, S., & Conati, C. (2009). Combining Unsupervised and Supervised Classification to Build User Models for Exploratory Learning Environments. JEDM | Journal of Educational Data Mining, 1(1), 18-71. Retrieved from https://jedm.educationaldatamining.org/index.php/JEDM/article/view/9
Abstract 642 | PDF Downloads 487

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Keywords

data mining, unsupervised and supervised classification, user modeling, intelligent learning environments, exploratory learning environments

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