DATA-DRIVEN PERSONALIZED TEACHING IN PRIVATE UNIVERSITIES: A PRACTICAL EXPLORATION BASED ON THE K-MEANS CLUSTERING ALGORITHM
Keywords:
Private universities, Personalized teaching, K-means clustering, Teaching according to aptitude, Learning analyticsAbstract
Addressing the practical problem that students in private universities exhibit significant individual differences and that the traditional unified teaching model can hardly accommodate personalized needs, this paper proposes a data-driven personalized teaching reform path based on the K-means clustering algorithm. Taking 43 students from the 2024 cohort of the Artificial Intelligence program at a private university as the practical subjects, this study selects three categories of multi-dimensional feature indicators—daily performance, teacher feedback, and final examination scores—and employs the K-means clustering algorithm to divide students into four learning types with internal homogeneity, based on which differentiated teaching objectives, content organization, teaching methods, and evaluation strategies are formulated. Through a comparative practice in two subsequent specialized courses taught by the same teacher to the same class, the class pass rate increased from 95.35% to 100%, while the average score and excellence rate also showed a continuous upward trend. The study demonstrates that cluster analysis based on routine teaching data can effectively identify the internal differences among student groups, provide an objective basis for differentiated instruction, and features low implementation cost and strong operability, thereby offering a practical and feasible path for AI-empowered teaching reform in private universities.References
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