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Abstract Title: Machine Learning Predicts Responses to Conceptual Questions Using Eye Movements
Abstract: Research has shown that students' responses to conceptual questions correlate with their eye movements.  However, to what extent is it possible to predict whether a particular learner might answer a question correctly by monitoring their eye movements in real time?  To answer this question, we used spatial-temporal eye-movement data from about 400 participants, as well as their responses to four conceptual physics questions with diagrams.  Half of these data were used as a training set for a machine learning algorithm (MLA) that would predict the correctness of students' responses to these questions.  The other half of the data were used as a test set to determine the performance of the MLA in terms of the accuracy of the prediction.  We will discuss the results of our study with specific attention to the prediction accuracy of the MLA under different conditions.
Abstract Type: Contributed Poster Presentation
Session Time: Poster Session II
Poster Number: B69
Contributed Paper Record: Contributed Paper Information
Contributed Paper Download: Download Contributed Paper

Author/Organizer Information

Primary Contact: N. Sanjay Rebello
Purdue University
525 Northwestern Ave.
Physics Building
West Lafayette, IN 47907
Phone: 765-464-3207
Co-Author(s)
and Co-Presenter(s)
Yang Wang
Tianlong Zu
John Hutson
Minh Hoai Nguyen
Lester C. Loschky

Contributed Poster

Contributed Poster: Download the Contributed Poster