Retention Factors in STEM Education Identified Using Learning Analytics: A Systematic Review
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 t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Education Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7102/12/11/781 |
| _version_ | 1797468499934707712 |
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| author | Chunping Li Nicole Herbert Soonja Yeom James Montgomery |
| author_facet | Chunping Li Nicole Herbert Soonja Yeom James Montgomery |
| author_sort | Chunping Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T19:08:19Z |
| format | Article |
| id | doaj.art-6365a97b263e4abc88b138b2407217e1 |
| institution | Directory Open Access Journal |
| issn | 2227-7102 |
| language | English |
| last_indexed | 2024-03-09T19:08:19Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Education Sciences |
| spelling | doaj.art-6365a97b263e4abc88b138b2407217e12023-11-24T04:23:26ZengMDPI AGEducation Sciences2227-71022022-11-01121178110.3390/educsci12110781Retention Factors in STEM Education Identified Using Learning Analytics: A Systematic ReviewChunping Li0Nicole Herbert1Soonja Yeom2James Montgomery3School of Information and Communication Technology, University of Tasmania, Hobart 7001, AustraliaSchool of Information and Communication Technology, University of Tasmania, Hobart 7001, AustraliaSchool of Information and Communication Technology, University of Tasmania, Hobart 7001, AustraliaSchool of Information and Communication Technology, University of Tasmania, Hobart 7001, AustraliaStudent 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.https://www.mdpi.com/2227-7102/12/11/781student retentionstudent successlearning analyticsSTEMhigher education |
| spellingShingle | Chunping Li Nicole Herbert Soonja Yeom James Montgomery Retention Factors in STEM Education Identified Using Learning Analytics: A Systematic Review Education Sciences student retention student success learning analytics STEM higher education |
| title | Retention Factors in STEM Education Identified Using Learning Analytics: A Systematic Review |
| title_full | Retention Factors in STEM Education Identified Using Learning Analytics: A Systematic Review |
| title_fullStr | Retention Factors in STEM Education Identified Using Learning Analytics: A Systematic Review |
| title_full_unstemmed | Retention Factors in STEM Education Identified Using Learning Analytics: A Systematic Review |
| title_short | Retention Factors in STEM Education Identified Using Learning Analytics: A Systematic Review |
| title_sort | retention factors in stem education identified using learning analytics a systematic review |
| topic | student retention student success learning analytics STEM higher education |
| url | https://www.mdpi.com/2227-7102/12/11/781 |
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