Multimodal Learning Analytics (MMLA) In Education – A Game Changer for Educators

Abstract

Multimodal Learning Analytics (Hereafter MMLA) has developed as a potential educational strategy, utilising several data modalities to get deeper insights into students’ learning experiences. This article thoroughly examines MMLA techniques and their use in educational settings. MMLA provides a comprehensive knowledge of learners’ behaviours, interactions, and cognitive processes by combining different data sources such as video, audio, gesture, and physiological data. The article opens with a quick introduction to learning analytics and its growing importance in learning environments. It goes over how to use multimodal data, including gaze, facial expressions, body language, and activity, to get insight into student engagement and collaboration patterns. Wearable gadgets and sensors enhance MMLA by collecting physiological data and providing insights into students’ emotional moods, cognitive load, and physical involvement. Moreover, the article explores the use of Kinect sensors for body language tracking. The study findings concluded on the feasibility of using MMLA through a learning analytics model for higher education. Educators can receive meaningful insights from MMLA by leveraging its power to optimise teaching practises, develop personalised learning experiences, and identify students who may require more support. Integrating different data modalities enables educators and researchers to make better-informed decisions, paving the path for a more successful and learner-centred education system.