This bibliometric analysis identifies growth patterns and thematic evolution in educational data mining research, indicating a shift toward machine learning and predictive methodologies.
Key Points
The analysis reveals a 777.8% increase in publication output, highlighting significant research growth in educational data mining.
Notably, there was a 215-fold increase in machine learning themes, indicating a shift towards predictive methodologies.
The study utilized keyword analysis and PRISMA-guided selection procedures to evaluate 436 peer-reviewed publications indexed in Scopus.
Results suggest a transition in educational data mining from foundational exploration to a mature phase, characterized by methodological pluralism.