PERC 2018 Abstract Detail Page
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| Abstract Title: | Machine Learning Predicts Responses to Conceptual Questions Using Eye Movements |
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| 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 |
