What Do Scopus Index Keywords Reveal About Educational Data Mining Research? A Bibliometric Analysis (2014-2024)
DOI:
https://doi.org/10.56294/dm20251199Keywords:
Educational Data Mining, Learning Analytics, Bibliometric Analysis, Research Evolution, Machine LearningAbstract
Introduction: Educational Data Mining (EDM) has emerged as a pivotal interdisciplinary field addressing the increasing demand for data-driven educational enhancement. However, a comprehensive understanding of its developmental trajectory is hindered by fragmented literature reviews and a lack of longitudinal analysis spanning critical technological and educational transformations.
Objective: This study investigates the evolution of EDM research over the transformative decade from 2014 to 2024 through systematic bibliometric analysis, aiming to identify growth patterns, thematic developments, and methodological innovations.
Method: We conducted an extensive analysis of 436 peer-reviewed publications indexed in Scopus, employing rigorous keyword analysis, mathematical modeling of research trends, and systematic thematic classification to examine temporal evolution patterns. The methodology utilized PRISMA-guided selection procedures, standardized keyword extraction and normalization, and quantitative measures including growth ratios, Shannon diversity indices, and thematic strength calculations.
Results: Our analysis reveals remarkable research growth, with a 777.8 % increase in publication output, representing a compound annual growth rate of 24.3 %. The findings document a significant paradigmatic shift from descriptive analytics toward predictive methodologies, evidenced by a 215-fold growth in Machine Learning and AI themes and the complete emergence of deep learning applications. Thematic evolution analysis identified 47.3 % of recent keywords as entirely new terms, indicating substantial conceptual expansion.
Conclusions: The research demonstrates EDM's transition from foundational exploration (2014-2017) through rapid expansion (2018-2020) to sophisticated maturation (2021-2024), characterized by methodological pluralism and the integration of advanced computational techniques.
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