Self-assessment of motivation: explicit and implicit indicators in L2 vocabulary learning
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
WTF? detecting students who are conducting inquiry without thinking fastidiously
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
User Modeling and User-Adapted Interaction
Review: Student modeling approaches: A literature review for the last decade
Expert Systems with Applications: An International Journal
International Journal of Artificial Intelligence in Education
Self-Assessment in the REAP Tutor: Knowledge, Interest, Motivation, & Learning
International Journal of Artificial Intelligence in Education
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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.