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| Content Provider | ACM Digital Library |
|---|---|
| Author | Raca, Mirko Dillenbourg, Pierre |
| Abstract | Should we judge the quality of the class by the grades the students and teacher get at the end of the semester or how the group collaborated during the semester towards acquiring new knowledge? Up until recently, the later approach was all too inaccessible due to complexity and time needed to evaluate every class. With the development of new technologies in different branches of video processing, gaze tracking and audio analysis we are getting the opportunity to go further with our analysis and go around the potential problem substitution into which we were previously forced. We present our efforts to record student-student and student-teacher interactions within a classroom eco-system. For this purpose, we developed a multi-camera system for observing teacher actions and students reactions throughout the class. We complemented the data with a mobile eye-tracker worn by the teacher, quantitative questionnaire data collection, as well as in-depth interviews with students about their impressions of the classes they took, and about our intervention. The seven-part experiment was conducted during the autumn semester of 2013, in two classes with over 60 participants. We present the conclusions we reached about the experiment format, visualize the preliminary results of our processing and discuss other options we are considering for our further experiments. We aim to explore further possibilities for analysing classroom life in order to create a more responsive environment to the needs of the students. |
| Starting Page | 13 |
| Ending Page | 20 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450304887 |
| DOI | 10.1145/2666633.2666636 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-11-12 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Computer vision Tracking Video analysis Classroom Intrusiveness Head pose |
| Content Type | Text |
| Resource Type | Article |
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