Challenges and opportunities of multimodal data in human learning: The computer science students' perspective

Abstract

Multimodal data have the potential to explore emerging learning practices that extend human cognitive capacities. A critical issue stretching in many multimodal learning analytics (MLA) systems and studies is the current focus aimed at supporting researchers to model learner behaviours, rather than directly supporting learners. Moreover, many MLA systems are designed and deployed without learners' involvement. We argue that in order to create MLA interfaces that directly support learning, we need to gain an expanded understanding of how multimodal data can support learners' authentic needs. We present a qualitative study in which 40 computer science students were tracked in an authentic learning activity using wearable and static sensors. Our findings outline learners' curated representations about multimodal data and the non‐technical challenges in using these data in their learning practice. The paper discusses 10 dimensions that can serve as guidelines for researchers and designers to create effective and ethically aware student‐facing MLA innovations.

Learning Analytics Based on Wearable Devices: A Systematic Literature Review From 2011 to 2021

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Personalisation methods in e‐learning‐A literature review

“…Previous literature reviews of personalised learning addressed the significance of the adaptive features of the employed e-learning system [170] as well as the customisation features for learners to take control of the learning process [26,181,186]. Originally, these reviews have been focused on the adaptive criteria [224], individual differences among users [203,209], identification of different learning methods [263] and the effectiveness of various ways to achieve personalised learning [271].…”

Systematic Analysis of Artificial Intelligence-Based Platforms for Identifying Governance and Access Control

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