DISCOVERING LEARNER PERSONAS IN AI-ASSISTED ENGLISH LANGUAGE LEARNING USING COSINE-BASED CLUSTERING: IMPLICATIONS FOR PERSONALIZED SUPPORT IN GCC CONTEXTS
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The rapid expansion of artificial intelligence (AI)-assisted English language learning tools has introduced substantial variability in learner outcomes due to differences in behavioural patterns, task engagement, and usage strategies among non-native learners, particularly within Omani educational contexts. This heterogeneity creates a methodological challenge in identifying consistent learner profiles without relying on predefined or subjective labels. This study investigates the effectiveness of unsupervised cosine-based clustering in identifying distinct learner personas in AI-assisted English learning environments. The study utilizes a dataset of 15,000 learner interaction records obtained from Kaggle, incorporating demographic attributes, behavioural features, task modalities, and learning outcome indicators. A structured experimental methodology is employed, beginning with baseline Euclidean K-Means clustering, followed by dimensionality reduction using Singular Value Decomposition (SVD), and subsequent clustering using cosine similarity across multiple algorithms, including K-Means, Gaussian Mixture Models, Agglomerative Clustering, and BIRCH. The results reveal that cosine-based K-Means clustering (k = 6) achieves a Silhouette Score of 0.678 compared to 0.10 for baseline Euclidean clustering, representing an absolute improvement of 0.578 and approximately a sixfold increase in clustering performance. Compared to SVD-based Euclidean clustering (Silhouette = 0.41), cosine similarity improves clustering effectiveness by approximately 65%, while the Davies–Bouldin Index decreases to 0.56 and the Calinski–Harabasz Index increases to 33,074. The findings indicate that cosine-based unsupervised modelling effectively identifies distinct learner personas, demonstrating that learning-gain variations are driven by behavioural interaction patterns rather than usage intensity alone.
JEL Classification Codes: G32, F65, L66, L25, M41.
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