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Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic

Eileen du Plooy¹, Daleen Casteleijn^2,3, Denise Franzsen²

¹eFundanathi, School of Therapeutic Sciences, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.

|November 11, 2024

Abstract

Introduction:

Higher education institutions face persistent challenges of student retention and academic progress. Personalized adaptive learning has the potential to address these issues as it leverages educational technology to tailor learning pathways according to individual student needs.

Objective:

To elucidate the key characteristics of personalized adaptive learning in higher education and its impact on academic performance and engagement.

Methods:

The Joanna Briggs Institute scoping review methodology was followed. Key international databases were searched to retrieve articles. The titles and abstracts of selected studies were imported into Covidence. Peer-reviewed journal articles, theses, and dissertations focusing on undergraduate students engaged in personalized adaptive learning, published between 2012 and 2024 were included. Data was extracted and charted in Covidence. Results were summarised through a narrative synthesis and visually presented in a PRISMA-ScR flow diagram.

Results:

This review included 69 eligible studies. The findings reveal insights into the multifaceted nature of personalized adaptive learning, which include platforms, implementation strategies, perceived strengths and limitations by instructors and students. Pre-knowledge quizzes were reported as the most common indicator for activating adaptive content delivery, and McGraw-Hill's Connect LearnSmart and Moodle were the most utilized adaptive platforms. Improved academic performance was reported by 41 of the studies (n = 41, 59 %), and 25 studies (n = 25, 36 %) indicated increased student engagement.

Conclusion:

This study highlights the potential of personalized adaptive learning to positively impact academic performance, student engagement and learning, despite technological limitations. Further research is encouraged to address technological challenges, build on strengths and refine implementation and application of personalized adaptive learning in higher education.

Keywords:

Adult learning Architectures for educational technology system Distance education and online learning Human-computer interface Teaching/learning strategies

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