Deep learning techniques have emerged as valuable tools for video analysis and motion detection. Recent advancements in this field have shown promising results. Our objective is to leverage these video understanding techniques to aid teachers in evaluating their teaching quality and enhancing their effectiveness in the classroom. However, existing research on student behavior analysis primarily focuses on recognizing actions pertaining to classroom management, neglecting the identification of “learning behaviors” exhibited by students. To address this limitation, we introduce a novel video dataset specifically designed to capture the nuances of “learning behaviors” displayed by primary-grade students in the mathematics classroom, along with a dedicated student localization dataset focused on detecting the location of individuals. Our approach introduces a framework that utilizes deep learning-based object detection and action recognition techniques trained on our curated datasets to analyze and comprehend student learning behaviors in the classroom. To assess the performance of our approach, we conduct separate tests on our object detection and action recognition models. Sub-sequently, our framework is applied to a collection of recorded 360-degree classroom videos, enabling a thorough evaluation of its capabilities.

Unified systems for multi-sensor devices, particularly eye-tracking in Virtual Reality (VR), are intricate and often require the listening and streaming of multichannel data. In this project, we propose a visual analysis framework for replicating a participant's viewing involvement by interpreting head movements as rotations and point-of-gaze (POG) as on-screen indicators. Our solution suggests an additional layer of system for near-real-time for processing and analyzing this multi-device data to connect with the data and enable both near-real-time or subsequent offline viewing of the entire VR eye-tracking session. Moreover, our method provides a no-batteries-need solution to create traditional eye-tracking visualization techniques. Finally, we apply three prior education technology analysis metrics: higher density gaze for students, shorter fixation time, and less fixation duration variance for students to determine expertise levels in this system. We systematically establish a ubiquitous, multi-device, eye-tracking solution to incorporate this approach. We evaluate the effectiveness of our system through a user study, using both expertise and non- expertise levels, and selectively surveying to ascertain the quality of the replicated experience and we test the system by running a real-world user study with sixty four different participants. We demonstrate the application's significance and potential to integrate prior analysis metrics using the collected data which this data collection and analysis have been approved by IRB.