Bringing Collaborative Analytics using Multimodal Data to the Masses: Evaluation and Design Guidelines for Developing a MMLA System for Research and Teaching Practices in CSCL

Citation (APA 7)

Chejara, P., Kasepalu, R., Prieto, L., P., Rodríguez-Triana, M. J., & Ruiz-Calleja, A. (2024). Bringing collaboration analytics using multimodal data to the masses: Evaluation and design guidelines for developing a mmla system for research and teaching practices in CSCL. In the 14th International Learning Analytics and Knowledge Conference (LAK24). ACM. https://doi.org/10.1145/3636555.3636877

Abstract

The Multimodal Learning Analytics (MMLA) research community has significantly grown in the past few years. Researchers in this field have harnessed diverse data collection devices such as eye-trackers, motion sensors, and microphones to capture rich multimodal data about learning. This data, when analyzed, has been proven highly valuable for understanding learning processes across a variety of educational settings. Notwithstanding this progress, an ubiquitous use of MMLA in education is still limited by challenges such as technological complexity, high costs, etc. In this paper, we introduce CoTrack, a MMLA system for capturing the multimodality of a group’s interaction in terms of audio, video, and writing logs in online and co-located collaborative learning settings. The system offers a user-friendly interface, designed to cater to the needs of teachers and students without specialized technical expertise. Our usability evaluation with 2 researchers, 2 teachers and 24 students has yielded promising results regarding the system’s ease of use. Furthermore, this paper offers design guidelines for the development of more user-friendly MMLA systems. These guidelines have significant implications for the broader aim of making MMLA tools accessible to a wider audience, particularly for non-expert MMLA users.