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Automatic Detection of Student Off-Task Behavior while Using an Intelligent Tutor for Algebra
| Content Provider | Semantic Scholar |
|---|---|
| Author | Bate, Allan Edgar C. |
| Copyright Year | 2010 |
| Abstract | As more and more modern classrooms use Intelligent Tutoring Systems, it becomes imperative for our educators to determine whether these systems are being used properly, or whether students engage in off-task behavior. Off-task behavior is defined as disengagement from a learning experience. It can range from resting one's eyes to talking to one's seatmate. It can also take the form of "gaming the system" defined as attempting to advance through the curriculum by abusing regularities in the system. Gaming is operationalized as systematic guessing or trial and error. These behaviors constitute time away from the learning task and are therefore considered detrimental to learning. In this study, we recorded student interactions with Aplusix, an intelligent tutor for algebra. We then asked two experts to label excerpts or clips of these interactions. Finally, we used machine learning techniques to create detectors of off-task behavior. KeywordsAffective Computing, Intelligent Tutoring Systems, Machine-learning, Aplusix, Off-task behavior |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://penoy.admu.edu.ph/~didith/2011AutomaticDetection.pdf |
| Alternate Webpage(s) | http://curry.ateneo.net/~didith/2010AutomaticDetection.pdf |
| Alternate Webpage(s) | http://penoy.admu.edu.ph/~didith/2010AutomaticDetection.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
