MOLAM: A Mobile Multimodal Learning Analytics Conceptual Framework to Support Student Self-Regulated Learning

TL;DR

This paper introduces MOLAM, a mobile multimodal learning analytics framework designed to support and enhance student self-regulated learning in online distance education by providing continuous, real-time feedback and support.

Contribution

It presents a novel framework integrating multimodal data for real-time monitoring and support of self-regulated learning in online environments.

Findings

Proposes a new mobile multimodal learning analytics framework.

Highlights potential for continuous, real-time SRL support.

Aims to improve student performance and skills development.

Abstract

Online distance learning is highly learner-centred, requiring different skills and competences from learners, as well as alternative approaches for instructional design, student support, and provision of resources. Learner autonomy and self-regulated learning (SRL) in online learning settings are considered key success factors that predict student performance. SRL comprises processes of planning, monitoring, action and reflection according to Zimmerman. And typically focuses on three key features of learners: (1) use of SRL strategies, (2) responsiveness to self-oriented feedback about learning effectiveness, and (3) motivational processes. SRL has been identified as having a direct correlation with students success, including improvements in grades and the development of relevant skills and strategies. Such skills and strategies are needed to become a successful lifelong learner. This chapter introduces a Mobile Multimodal Learning Analytics approach (MOLAM). I argue that the development of student Self-Regulated Learning would benefit from the adoption of this approach, and that its use would allow continuous measurement and provision of in-time support of student SRL in online learning contexts.