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This systematic review investigated how Higher Education Institutions (HEIs) optimise data analytics in postgraduate programmes to enhance student achievement. Existing research explores the theoretical benefits of data analytics but lacks practical guidance on strategies to effectively implement and utilise data analytics for student success. As such, this review aimed to identify data analytics approaches used by HEIs and explore challenges and best practices in their application. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Five databases were searched. Studies that examined data analytics in HEIs postgraduate programmes and their impact on student learning were included. Studies that were solely theoretical or in non-postgraduate settings were excluded. Twenty-six studies were included. Quality assessment using the Critical Appraisal Skills Programme (CASP) Checklist was employed. The review identified various data analytics approaches including descriptive, predictive, and prescriptive analytics, among others. These approaches can improve foundational skills, create supportive learning environments, and optimise teaching strategies. However, limitations (standardised tests, data integration) and privacy concerns were acknowledged. Recommendations include developing a comprehensive evaluation system, equipping educators with the skills to utilise diverse analytics to enhance student achievement, fostering open communication about data use, and cultivating a data-literate student body. While diverse approaches were explored, the review’s lack of specific contextual details may limit the generalisability of findings. To mitigate this, the review categorised techniques and provided references for further exploration. |
