DEVELOPMENT OF AN INTELLIGENT SUBJECT RECOMMENDATION SYSTEM BASED ON BIG DATA

Аннотация

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].