Big Data Research on Personalized Learning in Computer Education: A Thematic Evolution Analysis

ABSTRACT This paper presents a systematic review of the literature on personalized learning in computer education from 2014 to 2024, using a thematic evolution analysis to uncover the origins and emerging hotspots in this field. The review shows that the research hotspots in personalized learning present an apparent trend from early interventions focusing on academic warning systems and performance prediction, to technology‐enabled intelligent tutoring and resource recommendation, and then to the development of a range of competencies for computational thinking. Furthermore, the emerging application of novel technologies, such as generative AI, is advancing personalized learning toward an even more intelligent and human‐AI collaborative stage. Finally, the paper summarizes the existing challenges in methodological transparency, integration of educational praxis, and ethical balance, and highlights potential avenues for future research and practice.