Visualizing Collaboration in Teamwork: A Multimodal Learning Analytics Platform for Non-Verbal Communication

Abstract

Developing communication skills in collaborative contexts is of special interest for educational institutions, since these skills are crucial to forming competent professionals for today’s world. New and accessible technologies open a way to analyze collaborative activities in face-to-face and non-face-to-face situations, where collaboration and student attitudes are difficult to measure using traditional methods. In this context, Multimodal Learning Analytics (MMLA) appear as an alternative to complement the evaluation and feedback of core skills. We present a MMLA platform to support collaboration assessment based on the capture and classification of non-verbal communication interactions. The developed platform integrates hardware and software, including machine learning techniques, to detect spoken interactions and body postures from video and audio recordings. The captured data is presented in a set of visualizations, designed to help teachers to obtain insights about the collaboration of a team. We performed a case study to explore if the visualizations were useful to represent different behavioral indicators of collaboration in different teamwork situations: a collaborative situation and a competitive situation. We discussed the results of the case study in a focus group with three teachers, to get insights in the usefulness of our proposal. The results show that the measurements and visualizations are helpful to understand differences in collaboration, confirming the feasibility the MMLA approach for assessing and providing collaboration insights based on non-verbal communication.

Relationships Between Social Interactions and Belbin Role Types in Collaborative Agile Teams

“…These questionnaires and indicators can be applied in interdisciplinary studies together with other tools, such as those provided by multimodal learning analytics (MMLA). Thus, qualitative analysis can be combined with technology to quantify team members' activity through different sensors for audio [27], geospatial location [28], movement [29], microexpressions [30], etc. This interaction data collected by the sensors can be analyzed using traditional statistical techniques, artificial intelligence (AI) [31], and even social network analysis (SNA) [32].…”

Collaboration Skills in Educational Robotics: A Methodological Approach—Results from Two Case Studies in Primary Schools

“…. " [52], it is meaningful and of critical importance to enhance it in every way possible, and this approach is designed for all the students to have as much of an equal opportunity to participate as possible.…”