Classroom Video Assessment and Retrieval via Multiple Instance Learning
This work addresses the problem of improving assessment efficiency for educators and researchers using classroom video, but it appears incremental as it applies existing techniques to a specific domain.
The paper tackled the problem of content-based retrieval of classroom video to support human assessment of learning environments, using a multiple instance learning approach to map semantic concepts to video features, and reported a formative experiment suggesting potential application to productivity enhancement in broader retrieval tasks.
We propose a multiple instance learning approach to content-based retrieval of classroom video for the purpose of supporting human assessing the learning environment. The key element of our approach is a mapping between the semantic concepts of the assessment system and features of the video that can be measured using techniques from the fields of computer vision and speech analysis. We report on a formative experiment in content-based video retrieval involving trained experts in the Classroom Assessment Scoring System, a widely used framework for assessment and improvement of learning environments. The results of this experiment suggest that our approach has potential application to productivity enhancement in assessment and to broader retrieval tasks.