Learning Analytics and Personalized Learning Resource Recommendation in a University Computer Course: An Empirical Study
With the development of digital learning environments in higher education, researchers and educators have begun to give more and more attention to how to use large-scale learning process data to provide good, personalized instructional support. The subject of research for this paper is the university’s computer course “Fundamentals of Programming,” and a personalized learning-resource-recommendation framework based on learning analytics has been developed and applied to collect behavior data, implement Bayesian knowledge tracing, create learner profiles, and perform hybrid resource ranking. A one-semester quasi-experimental study was carried out on 102 first-year undergraduate students; one group (n = 52) was given personalized resource recommendations as the experimental group, and the other group (n = 50) continued to use the traditional fixed course materials as the control group. Based on the above results, the experimental group performed better than the control group in the post-test (t = 5.63, P < 0.001, Cohen’s d = 1.10), showed more active learning behavior, and reached a recommendation precision@5 of 0.78; this is much higher than that of the popularity-based baseline (0.41). Therefore, it has been decided that learning analytics will be employed to integrate process data with personalized instruction in university computer courses, and thus a reproducible technical path and empirical evidence for precision teaching have been obtained in this field.
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