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  1. Proceedings of the 2014 ACM workshop on Multimodal Learning Analytics Workshop and Grand Challenge (MLA '14)
  2. Holistic Analysis of the Classroom
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Multimodal Learning Analytics as a Tool for Bridging Learning Theory and Complex Learning Behaviors
Combining empirical and machine learning techniques to predict math expertise using pen signal features
Estimation of Presentations Skills Based on Slides and Audio Features
Acoustic-Prosodic Entrainment and Rapport in Collaborative Learning Dialogues
Using Multimodal Cues to Analyze MLA'14 Oral Presentation Quality Corpus: Presentation Delivery and Slides Quality
Holistic Analysis of the Classroom
Presentation Skills Estimation Based on Video and Kinect Data Analysis
Deciphering the Practices and Affordances of Different Reasoning Strategies through Multimodal Learning Analytics

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Holistic Analysis of the Classroom

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 PDF
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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