Authors : Palash Majumder; Nimisha Beri; Salil Biswas

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/3fwa6w5n

Scribd : https://tinyurl.com/ys6y5cne

DOI : https://doi.org/10.38124/ijisrt/26apr458

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 examined heterogeneous relationships between digital device use (DDU), parental involvement, psychological capital, and English as a Second Language (ESL) achievement among secondary students from the marginalized Matua community of West Bengal, India. Conventional structural equation modeling (SEM) fails to account for unobserved heterogeneity in DDU patterns and the need for data-driven identification of latent subgroups. This study addressed these limitations by implementing a Bayesian Finite Mixture Structural Model (BFM-SEM) using an overfitted finite Gaussian mixture with posterior sparsity for data-driven class discovery, with district membership modeled as a fixed covariate to account for data clustering across two districts (Nadia: n = 301; North 24 Parganas: n = 299; total N = 600). The posterior converged on three empirically distinct DDU profiles: Passive Consumers (40.7%), Balanced Users (35.2%), and Educational Engagers (24.1%; note: percentages sum to 100.0% after rounding adjustment); mean posterior classassignment probabilities exceeded .86 for all classes, indicating high classification certainty. Preliminary conventional SEM confirmed significant structural associations—including negative associations between unguided DDU and psychological capital (β = −.281, p < .001) and positive associations between parental involvement and ESL achievement (β = .185, p < .001)—but revealed poor model fit (RMSEA = 0.165; CFI = .831), and was decisively outperformed by the BFM-SEM (ΔELPD = 367.4, SE = 28.3). Profile-specific structural estimates revealed substantial heterogeneity: the negative association between DDU and psychological capital was strongest among Passive Consumers (β = −.412, 95% CI [−.501, −.323]) and non-significant among Educational Engagers (β = −.048, 95% CI [−.157, .061]). All Bayesian diagnostics confirmed model convergence (R̂ ≤ 1.01; ESS > 400). These findings may help guide differentiated, culturally responsive digital literacy intervention design.

Keywords : Bayesian Finite Mixture Modeling, Data-Driven Class Enumeration, Digital Device Use, ESL Achievement, Latent Profile Analysis, Psychological Capital.

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