Authors : Henoc Tenda; Ange Ntokusi; Innocent Tshiamanda; Celestin Kasela; James Kiba; Azaria Kusakana; Blanchard Kangulumba; Jerome Mwandoki; Rodriguez Luzolo; Dieumerci Kinguangu

Volume/Issue : Volume 11 - 2026, Issue 7 - July

Google Scholar : https://tinyurl.com/3cjcafrf

Scribd : https://tinyurl.com/bdebsdpr

DOI : https://doi.org/10.38124/ijisrt/26jul298

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This study employed K-means clustering on behavioral data gathered from a programming-oriented IT training platform. This study aimed to characterize different learner profiles and map each profile with customized teaching strategies through the Adaptive Cognitive Enhancement Model (ACEM). The dataset includes the behavioral data of 3384 learners over a time span of six months, including activity logs, submissions, and progress of learning missions. We categorized these learners into four levels: passive, basic, intermediate, and advanced learners. We used internal metrics (Silhouette Coefficient: 0.682; Davies–Bouldin Index: 0.777; Calinski–Harabasz Index: 4349.79) to validate the Clustering, and PCA explained 98.90% of the variance in the data. These profiles supported the development of tailored teaching activities according to the ACEM plan, such as staged orientation (for lower-interest learners) to self-staged and peersupported modules (advanced learners). This approach addresses the gap between unsupervised data exploration and pedagogically driven adaptive learning environments. The limitations of this study include the use of a single data platform and the exclusion of cognitive and emotional factors from the analysis. In future research, we plan to refine the model by applying multiple platforms and incorporating psychological data, thereby extending the concept of learner profiling to include psychological data. These findings have important implications for scalable and adaptive educational technologies.

Keywords : K-Means Clustering, Learner Classification, Adaptive Learning, Learning Analytics, IT Training Platforms.

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