A systematic review on the deployment and effectiveness of data analytics in higher education to improve student outcomes
Abstract
This is a systematic review conducted of primary research literature published between 2007 and 2018 on the deployment and effectiveness of data analytics in higher education to improve student outcomes. We took a methodological approach to searching databases; appraising and synthesising results against predefined criteria. We reviewed research on the effectiveness of three differentiated forms of data analytics: learning, academic and learner analytics. Student outcomes are defined as retention, academic performance and engagement. Our results find that three quarters of studies report the use of educational data analytics to be effective in improving student outcomes but their relationship with student outcomes requires further and more robust investigation and assessment. We argue that research must interpret and communicate effectiveness qualitatively, as well as quantitatively, by including the student voice in assessments of impact.
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A lack of theoretical grounding in LA reviews includes underdeveloped theories of change.
“and underdeveloped theories of change (Foster & Francis, 2020).”
From Unpacking student engagement in higher education learning analytics: a systematic review (2024) · cites this paper as Foster & Francis, 2020
Foster and Francis (2020) found evidence that outcomes can be predicted by LA, but inconsistency across terms and definitions led to difficulties in interpreting results.
“While Foster and Francis (2020) found evidence to suggest that outcomes can be predicted by LA, inconsistency across terms and definitions led to difficulties in the overall interpretation of results.”
From Unpacking student engagement in higher education learning analytics: a systematic review (2024) · cites this paper as Foster and Francis (2020)
Studies using qualitative data collection and analysis methods were rare.
“However, these were rare, with relatively few studies using qualitative data collection and analysis methods (Banihashem et al., 2022;Foster & Francis, 2020).”
From Unpacking student engagement in higher education learning analytics: a systematic review (2024) · cites this paper as Foster & Francis, 2020
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- Authors
- Foster, C., & Francis, P.
- Affiliations
- Northumbria University (GB)
- Corresponding author
- Foster, Carly
- Francis, Peter
- Venue
- Assessment & Evaluation in Higher Education
- Publisher
- Informa UK Limited
- Published
- 2019-12-18
- Volume
- 45
- Issue
- 6
- Pages
- 822-841
- ISSN
- 0260-2938 (ISSN-L)
- 1469-297X
- OpenAlex
- W2995010956
- Cited by
- 55
- References
- 61
- Citations by year
- 2026: 7 · 2025: 17 · 2024: 12 · 2023: 12 · 2022: 3 · 2021: 3 · 2020: 1
- FWCI
- 0.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Assessment & Evaluation in Higher Education (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Social Sciences › Education › Higher Education Research Studies
- Keywords
- educational data analytics, learning analytics, academic analytics, student outcomes, student retention, academic performance, student engagement, student voice, higher education
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