Automatic Detection of Off-Task Behaviors in Intelligent Tutoring Systems with Machine Learning Techniques

Abstract

Identifying off-task behaviors in intelligent tutoring systems is a practical and challenging research topic. This paper proposes a machine learning model that can automatically detect students' off-task behaviors. The proposed model only utilizes the data available from the log files that record students' actions within the system. The model utilizes a set of time features, performance features, and mouse movement features, and is compared to 1) a model that only utilizes time features and 2) a model that uses time and performance features. Different students have different types of behaviors; therefore, personalized version of the proposed model is constructed and compared to the corresponding nonpersonalized version. In order to address data sparseness problem, a robust Ridge Regression algorithm is utilized to estimate model parameters. An extensive set of experiment results demonstrates the power of using multiple types of evidence, the personalized model, and the robust Ridge Regression algorithm.