Behavioral clustering for adaptive learning object recommendation in higher education using learning analytics for personalized student success

Abstract Higher education institutions increasingly rely on Learning Management Systems to deliver instruction, yet many platforms employ one-size-fits-all content delivery that fails to accommodate heterogeneity in student engagement, motivation, and performance. This study presents a behavioral clustering framework that derives learner profiles from behavioral and psychological proxies and maps them to a proposed adaptive learning object recommendation framework. Using a dataset of 14,003 anonymized student records with 16 attributes, including 12 behavioral, psychological, and environmental clustering inputs, we applied K-Means++ clustering (k = 6), validated by the Elbow Method and Silhouette Analysis, and benchmarked against four competing algorithms. Cluster distinctiveness was characterized through ANOVA on Final Grade (primary outcome: F(5, 13,997) = 9365.49, p < 0.001, η 2 = 0.770; Exam Score sensitivity analysis: F(5, 13,997) = 6822.77, p < 0.001, η 2 = 0.709), Tukey HSD post hoc tests, and chi-square analysis with Cramér's V. Six distinct learner profiles emerged, spanning from Highly Engaged Achievers to At-Risk Learners, with a mean exam score difference of 40.17 points between the highest and lowest-performing clusters. An affinity-scoring model combined with an FSLSM mapping matrix and diversity regularization (λ = 0.30) was proposed as a conceptual framework to translate validated profiles into cluster-specific learning object recommendations. The clustering achieved robust separation (Silhouette = 0.62), and the recommendation component provides an interpretable mapping that awaits empirical validation in live LMS environments. Because the clustering inputs are self-reported proxies rather than direct LMS interaction traces, validation with LMS data remains necessary to assess applicability to observed learning behavior.