154436 - Retention factors in STEM education identified using learning analytics a systematic review.pdf (805.71 kB)
Retention factors in STEM education identified using learning analytics: a systematic review
journal contributionposted on 2023-05-21, 15:10 authored by Chunping LiChunping Li, Nicole HerbertNicole Herbert, Soonja YeomSoonja Yeom, Erin MontgomeryErin Montgomery
Student persistence and retention in STEM disciplines is an important yet complex and multi-dimensional issue confronting universities. Considering the rapid evolution of online pedagogy and virtual learning environments, we must rethink the factors that impact students’ decisions to stay or leave the current course. Learning analytics has demonstrated positive outcomes in higher education contexts and shows promise in enhancing academic success and retention. However, the retention factors in learning analytics practice for STEM education have not been fully reviewed and revealed. The purpose of this systematic review is to contribute to this research gap by reviewing the empirical evidence on factors affecting student persistence and retention in STEM disciplines in higher education and how these factors are measured and quantified in learning analytics practice. By analysing 59 key publications, seven factors and associated features contributing to STEM retention using learning analytics were comprehensively categorised and discussed. This study will guide future research to critically evaluate the influence of each factor and evaluate relationships among factors and the feature selection process to enrich STEM retention studies using learning analytics.
Publication titleEducation Sciences
Department/SchoolSchool of Information and Communication Technology
Place of publicationSwitzerland
Rights statement© 2022 by the authors. Licensee MDPI, Basel, Switzerland. his is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License, (https://creativecommons.org/licenses/by/4.0/)