Impact of Predictive Learning Analytics in Higher Education: A Systematic Literature Review

This systematic literature review scrutinizes the role of Predictive Learning Analytics (PLA) in higher education from 2017 to 2023. Anchored by the PRISMA flowchart, the methodology involved a rigorous selection process from databases such as IEEE Xplore, Sage, ACM Digital Library, PubMed, Scopus, and ERIC. This systematic literature review filtered the studies based on inclusion criteria: peer-reviewed articles published in English, with full texts available, and excluded non-peer-reviewed sources, non-English publications, and those lacking definitive outcomes. The review addresses four questions in HEI's: the labeling and definition of PLA, its impact on student success, its role in enhancing teaching practices, and its influence on administrative functions. From the initial 142 records, 17 studies met the criteria, providing a comprehensive insight into PLA's role in shaping the academic landscape. The findings indicate that PLA is labeled as a strategic tool for leveraging student data to forecast and enhance learning outcomes. The analysis reveals PLA's significant contribution to student success through early identification of at-risk students and personalized intervention strategies. Furthermore, PLA has been instrumental in refining teaching methods and providing actionable insights to educators. For administrators, PLA informs resource allocation, curriculum development, and policy-making. This review emphasizes the transformative potential of PLA in optimizing higher education outcomes, signifying a paradigm shift towards data-driven educational strategies