Developing a Multimodal Classroom Engagement Analysis Dashboard for Higher-Education

Abstract

Developing learning analytics dashboards (LADs) is a growing research interest as online learning tools have become more accessible in K-12 and higher education settings. This paper reports our multimodal classroom engagement data analysis and dashboard design process and the resulting engagement dashboard. Our work stems from the importance of monitoring classroom engagement, which refers to students' active physical and cognitive involvement in learning that influences their motivation and success in a given course. To monitor this vital facade of learning, we developed an engagement dashboard using an iterative and user-centered process. We first created a multimodal machine learning model that utilizes face and pose features obtained from recent deep learning models. Then, we created a dashboard where users can view their engagement over time and discover their learning/teaching patterns. Finally, we conducted user studies with undergraduate and graduate-level participants to obtain feedback on our dashboard design. Our paper makes three contributions by (1) presenting a student-centric, open-source dashboard, (2) demonstrating a baseline architecture for engagement analysis using our open-access data, and (3) presenting user insights and design takeaways to inspire future LADs. We expect our research to guide the development of tools for novice teacher education, student self-evaluation, and engagement evaluation in crowded classrooms.