Abstract
<jats:p>The increasing volume and complexity of educational data necessitate sophisticated approaches for personalized learning and academic guidance [1]. Specifically, the application of big data analytics and machine learning techniques offers a robust framework for developing intelligent recommendation systems that can effectively steer students toward suitable academic paths [2]. Such systems leverage diverse datasets, including academic performance metrics, demographic information, and socioeconomic indicators, to construct predictive models that forecast student success and inform optimal subject selection [3]. This proactive approach mitigates student attrition by enabling timely interventions and tailoring educational experiences to individual needs [4]. The integration of such sophisticated systems not only enhances student retention rates but also optimizes educational efficacy by providing data-driven insights into pedagogical strategies and curriculum development [5]. These models often undergo rigorous data preprocessing, including cleaning, scaling, oversampling, and feature selection, to ensure unbiased and generalized outcomes [6]. This comprehensive data preparation is crucial for maximizing the predictive accuracy score of the recommendation engine, ensuring that the system can discern subtle patterns within student profiles to optimize subject alignment with their aptitudes and aspirations [7]. Furthermore, the incorporation of fairness-aware predictive frameworks is crucial to mitigate bias and ensure equitable treatment across diverse student cohorts, thereby addressing potential disparities often overlooked by models relying solely on academic data [8]. Indeed, the expansive and varied nature of educational big data—derived from learning management systems, student information systems, and administrative records—necessitates advanced analytical techniques to extract meaningful insights for predictive modeling [9].</jats:p>