Learning to Identify Students' Off-Task Behavior in Intelligent Tutoring Systems

Authors:
Suleyman Cetintas;Luo Si;Yan Ping Xin;Casey Hord;Dake Zhang
Affiliations:
Department of Computer Science;Department of Computer Science;Department of Educational Studies, Purdue University, West Lafayette, IN, 47906, USA;Department of Educational Studies, Purdue University, West Lafayette, IN, 47906, USA;Department of Educational Studies, Purdue University, West Lafayette, IN, 47906, USA
Venue:
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Year:
2009

Citing 4
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Abstract

The paper proposes a machine learning model that can automatically identify off-task behaviors of students while using an intelligent tutoring system. Only log files that record students' actions with the system are used for the development of the model. The model utilizes a set of time features, performance features and mouse movement features and is compared to i) a model that only utilizes time features, ii) a model that uses time and performance features. In order to address data sparseness problem, a robust Ridge Regression algorithm is designed to estimate model parameters. An extensive set of experiment results demonstrate the power of using multiple types of evidence as well as the robust Ridge Regression algorithm.