Monitoring Cyber Peer-Led Team Learning: A Multimodal Human-in-the-loop Approach

The recent adoption of generative artificial intelligence (AI) tools in education has transformed education and AI-assisted learning. However, researchers embracing applied machine learning (ML) in pedagogical settings continue to face challenges. Lack of publicly available multimodal datasets involving gesture and emotion recognition specifically in education is a bottleneck for building AI-enabled e-learning platforms. In the paper, we take a constructive stance at monitoring group behavior in cyber peer-led team learning (cPLTL) classes in Organic Chemistry using ML. Although past studies have attempted to quantify student engagement in e-learning, their use in the cPLTL use case for AI modeling has not yet been established. The hypothesis underlying our proposed framework is that online peer group behavior can be characterized by a human-in-the-loop model that relies on multiple input modalities. Thus, the aim is to identify behavioral patterns in head and facial movements that are augmented by lexical based sentiment and audio feature extraction. To combat the small data challenge, we propose a framework for the human-in-the-loop (HITL) system that actively learns the past group modalities. HITL strategies enable the algorithm to learn more efficiently from less data iteratively. The model will be implemented using active learning, measures of uncertainty, random sampling and entropy which are key in the design of the study. A qualitative comparison of sentiment modality with ChatGPT’s participant performance evaluation has been discussed. The study will increase the use of AI in tools that support educators in universities using …