Instructional Activity Detection Using Deep Neural Networks

Analyzing instructional videos via computer vision and machine learning holds promise for several tasks, such as assessing teacher performance and classroom climate, evaluating student engagement, and identifying biases in instruction. The traditional way of evaluating instructional videos depends on manual observation with human raters, which is time-consuming and requires a trained labor force. This paper tests several deep network architectures in the automation of instructional video analysis, where the networks are tailored to recognize classroom activity. Our experimental setup includes a set of 250 hours of primary and middle school videos that are annotated by expert human raters. We present several strategies to handle varying lengths of instructional activities, a major challenge in the detection of instructional activity. Based on the proposed strategies, we enhance and compare different deep networks for detecting instructional activity.