The rapid expansion of digital learning environments has generated unprecedented volumes of educational data, enabling researchers and institutions to investigate the factors influencing student performance through learning analytics frameworks. While traditional educational prediction models have primarily emphasized demographic variables and cognitive indicators, contemporary educational research increasingly highlights the importance of psychological constructs such as personality traits, academic motivation, and self-regulation in shaping academic outcomes. The present study explores the integration of these multidimensional variables within a learning analytics framework to predict student performance in higher education contexts. The study adopts an IMRaD-based empirical structure and examines how personality dimensions, motivational orientations, and self-regulatory behaviors collectively contribute to academic achievement prediction.

A quantitative research design was employed using structured surveys, institutional academic records, and behavioral learning management system data collected from undergraduate students across multiple academic disciplines. The research incorporates validated psychometric instruments grounded in the Big Five Personality Theory, Self-Determination Theory, and self-regulated learning frameworks. Advanced analytical techniques, including regression modeling, correlation analysis, and machine learning-based prediction approaches, were utilized to evaluate the predictive relationships among variables. The findings demonstrate that conscientiousness, intrinsic motivation, goal orientation, time management, and metacognitive regulation significantly influence academic performance indicators. The integration of behavioral analytics with psychological constructs substantially improved predictive accuracy compared to conventional academic models.

The study contributes to the growing body of literature on educational data mining and learning analytics by proposing a multidimensional predictive framework capable of supporting personalized learning interventions and early academic support systems. The findings also underscore the necessity of integrating psychological and behavioral variables into institutional analytics infrastructures. Implications for educators, policymakers, curriculum designers, and educational technology developers are discussed, along with limitations and future research opportunities involving adaptive learning environments and artificial intelligence-driven academic prediction systems.

Learning analytics, personality traits, academic motivation, self-regulation, student performance, educational data mining, predictive analytics, higher education, metacognition, academic achievement

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