The Role of Educational Data Analytics in Improving Learning Performance: A Systematic Review
Keywords:
Educational data analytics, Personalized learning, Academic motivation, Self-regulated learning, Adaptive feedback, Thematic analysisAbstract
Objective: This study aimed to examine the role of educational data analytics in enhancing learning quality, personalizing instruction, and supporting data-driven decision-making in digital learning environments.
Methods and Materials: This research employed a qualitative systematic review design. Data were collected exclusively through literature review, selecting 12 peer-reviewed journal articles from databases including Scopus, Web of Science, and ScienceDirect. Data were analyzed thematically using Nvivo 14 software through open, axial, and selective coding until theoretical saturation was reached. Inclusion criteria encompassed methodological rigor, conceptual relevance, and publication in scholarly journals.
Findings: The results indicated that educational data analytics serves as an effective tool for improving educational decision-making, identifying learning patterns, and predicting students’ academic performance. Moreover, it enhances learners’ motivation, self-regulation, and engagement while enabling adaptive feedback and personalized learning environments. Despite its advantages, challenges such as limited data literacy, ethical concerns, and inadequate technical infrastructures were identified as barriers to effective implementation in educational systems.
Conclusion: Educational data analytics represents a transformative force in contemporary education by integrating technology, data science, and learning theories to improve learner outcomes and inform pedagogical decisions. However, maximizing its potential requires the development of data literacy skills, establishment of ethical frameworks, and enhancement of digital infrastructure.
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