## 1 Introduction  
Educational systems around the world are under growing pressure to improve student success and reduce dropout rates. A cornerstone of such efforts is the ability to accurately predict students' academic performance. Early identification of at-risk students allows for timely intervention, personalized educational support, and optimal resource allocation, thus playing a crucial role in promoting educational equity and minimizing learning loss [1, 2]. The growing field of educational data mining (EDM) has provided robust analytical tools for this purpose. The rapid digitalization of education has led to the generation of vast amounts of data, including behavioral patterns, assessment records, interaction logs, demographic details, and academic histories. This evolution has enabled the application of artificial intelligence (AI) and machine learning (ML) algorithms to extract meaningful patterns and develop predictive models in educational settings. These models are capable of leveraging high-dimensional data to produce accurate predictions about students' future outcomes [1, 2, 3]. Recent advances in machine learning and explainable AI continue to transform educational data mining, with growing calls for fairness and transparency in predictive analytics [4; 5]. Among the widely used ML algorithms for academic performance prediction are Random Forest, XGBoost, decision tree, and logistic regression. Studies have reported predictive accuracies ranging from 70% to 95% depending on feature engineering, model selection, and dataset characteristics [6; 7]. However, despite advances in predictive accuracy, many models fall short in critical areas, such as interpretability, fairness, and generalizability. For instance, complex models like XGBoost often function as \"black boxes,\" limiting their acceptance and integration into educational decision-making processes. Additionally, the incorporation of sensitive attributes such as gender, age, or socioeconomic status can introduce algorithmic bias, potentially leading to inequitable outcomes [8; 9]. Explainable AI (XAI) techniques, such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations), have emerged as essential tools to demystify these models. SHAP assigns global and local importance scores to features based on game theory, while LIME builds interpretable local surrogate models. In educational contexts, interpretable outputs are essential to foster trust, ensure transparency, and enable actionable insights [10; 11; 12]. Another significant concern is fairness. Machine learning models trained on historical data may perpetuate or even amplify existing biases, particularly if sensitive features correlate with performance outcomes. Algorithmic fairness metrics such as demographic parity and Equalized Odds are increasingly used to measure such biases. Moreover, techniques like adversarial debiasing have shown promise in mitigating these disparities while maintaining predictive accuracy [13; 14; 15]. Reproducibility and data accessibility pose additional challenges. Many predictive studies rely on proprietary datasets, making it difficult to validate and generalize findings. Open-access datasets such as the UCI Student Performance dataset offer a transparent alternative, enabling replicable experimentation. Nonetheless, this dataset also has limitations, including limited diversity, narrow geographic scope, and representation of only Portuguese secondary school students [16]. To address these limitations, this study applies a diverse methodological framework that integrates interpretability and fairness techniques to enhance the utility and robustness of findings derived from this dataset. This study aims to bridge these gaps by conducting a comprehensive evaluation of academic performance prediction using open-access data.