Retention Factors in STEM Education Identified Using Learning Analytics
A Systematic Review
Bibliographic Data
| ID | 22049232 |
|---|---|
| Authors | Chunping Li (0009-0000-1669-3882, University of Tasmania), Nicole Herbert (University of Tasmania), Soonja Yeom (0000-0002-5843-101X, University of Tasmania), James Montgomery (0000-0002-5360-7514, University of Tasmania) |
| Year | 2022 |
| Volume | 12 |
| Issue | 11 |
| Pages | 781 |
| Publication date | 2022-11-03 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Education Sciences (JOURNAL) |
| Journal identifiers | ISSN: 2227-7102 • E-ISSN: 2227-7102 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/educsci12110781 |
| Language | EN |
| Citations received | 6 |
| References cited | 80 |
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
A meta systematic review of artificial intelligence in higher education
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How to Study Family Learning Practices Mediated by Digital Platforms
Learning Outcomes Evaluation Through Learning Analytics Systems in Higher Education
Learning analytics should not promote one size fits all
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Preferred Reporting Items for Systematic Reviews and Meta-Analyses
Applying Learning Analytics in Online Environments
Let them choose
Early prediction of undergraduate Student's academic performance in completely online learning
Being more human
Students matter the most in learning analytics
Elements of Success
Orchestrating learning analytics (OrLA)
Student perceptions of their learning and engagement in response to the use of a continuous e-assessment in an undergraduate module
Self-Motivation for Academic Attainment
| Unique citing works | 6 |
|---|---|
| Citations per year | 3 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 6 |