Multimodal temporal network analysis to improve learner support and teaching

Abstract

A learning process involves interactions between learners, teachers, machines and formal and/or informal learning environments. These interactions are relational, interdependent and temporal. The emergence of rich multimodal learner data suggests the development of methods that can capture time-stamped data from multiple sources (e.g., heart rate data and eye tracking data), thus allowing researchers to examine learning as a continuous process rather than a static one. This leads us to propose a new methodological approach, the Multimodal Temporal Network Analysis to: i) measure temporal learner data deriving from the relevant interactions and ii) ultimately support learners and their teachers in learning and/or teaching activities. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International 1 (CC BY 4.0).