How to Build More Generalizable Models for Collaboration Quality? Lessons Learned from Exploring Multi-Context Audio-Log Datasets using Multimodal Learning Analytics

Abstract

Multimodal learning analytics (MMLA) research for building collaboration quality estimation models has shown significant progress. However, the generalizability of such models is seldom addressed. In this paper, we address this gap by systematically evaluating the across-context generalizability of collaboration quality models developed using a typical MMLA pipeline. This paper further presents a methodology to explore modelling pipelines with different configurations to improve the generalizability of the model. We collected 11 multimodal datasets (audio and log data) from face-to-face collaborative learning activities in six different classrooms with five different subject teachers. Our results showed that the models developed using the often-employed MMLA pipeline degraded in terms of Kappa from Fair (.20 < Kappa < .40) to Poor (Kappa < .20) when evaluated across contexts. This degradation in performance was significantly ameliorated with pipelines that emerged as high-performing from our exploration of 32 pipelines. Furthermore, our exploration of pipelines provided statistical evidence that often-overlooked contextual data features improve the generalizability of a collaboration quality model. With these findings, we make recommendations for the modelling pipeline which can potentially help other researchers in achieving better generalizability in their collaboration quality estimation models.