M2LADS: A System for Generating MultiModal Learning Analytics Dashboards in Open Education

TL;DR

M2LADS is a web-based system that integrates and visualizes multimodal data from MOOC learners to enhance learning analytics and support personalized feedback and interventions.

Contribution

The paper introduces M2LADS, a novel platform that combines diverse multimodal data sources into interactive dashboards for open education environments.

Findings

Supports integration of biometric, behavioral, and performance data

Enables holistic analysis of learner engagement and attention

Facilitates personalized feedback and learning improvements

Abstract

In this article, we present a Web-based System called M2LADS, which supports the integration and visualization of multimodal data recorded in learning sessions in a MOOC in the form of Web-based Dashboards. Based on the edBB platform, the multimodal data gathered contains biometric and behavioral signals including electroencephalogram data to measure learners' cognitive attention, heart rate for affective measures, visual attention from the video recordings. Additionally, learners' static background data and their learning performance measures are tracked using LOGCE and MOOC tracking logs respectively, and both are included in the Web-based System. M2LADS provides opportunities to capture learners' holistic experience during their interactions with the MOOC, which can in turn be used to improve their learning outcomes through feedback visualizations and interventions, as well as to enhance learning analytics models and improve the open content of the MOOC.