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| Content Provider | ACM Digital Library |
|---|---|
| Author | Kaul, Purnendu Hammond, Tracy Rajanna, Vijay |
| Abstract | Intelligent tutoring systems (ITS) empower instructors to make teaching more engaging by providing a platform to tutor, deliver learning material, and to assess students' progress. Despite the advantages, existing ITS do not automatically assess how students engage in problem solving? How do they perceive various activities? and How much time they spend on each activity leading to the solution? In this research, we present an eye tracking framework that, based on eye movement data, can assess students' perceived activities and overall engagement in a sketch based Intelligent tutoring system, "Mechanix" [Valentine et al. 2012]. Based on an evaluation involving 21 participants, we present the key eye movement features, and demonstrate the potential of leveraging eye movement data to recognize students' perceived activities, "reading, gazing at an image, and problem solving," with an accuracy of 97.12%. |
| Starting Page | 134 |
| Ending Page | 134 |
| Page Count | 1 |
| File Format | |
| ISBN | 9781450343831 |
| DOI | 10.1145/2931002.2948727 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-07-22 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Intelligent tutoring systems Perception Eye tracking |
| Content Type | Text |
| Resource Type | Article |
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