# Prediction of Students' Adaptability Using Explainable AI in Educational Machine Learning Models
## 1 Introduction  
A critical area of concern for educators, policymakers, and academics in the quickly changing field of education is comprehending and improving student adaptation [1, 2]. The ability of students to effectively navigate and respond to diverse learning environments, technological advancements, and varying educational methodologies highlights the importance of adaptive learning systems tailored to individual needs [3]. The field of education has seen a revolutionary change with the introduction of machine learning techniques, which have provided new ways to forecast and enhancestudent adaptation [4]. However, the black-box nature of complex machine learning models often impedes their interpretability, raising concerns regarding their applicability in educational settings where transparency and comprehensibility are paramount. The inability of educators to understand the machine learning algorithms' decision-making process due to their black-box nature has been a key barrier in the learning space [5; 6]. Because of this, there is increasing interest in building and evaluating interpretable machine learning models that can be used to identify student adaptability factors. These models should not only produce precise predictions but also offer valuable insights into the decision-making process. Student adaptability is a complex construct encompassing the ability to effectively adapt, learn, and succeed in diverse educational settings. It involves factors such as socio-economic background, individual learning styles, technological proficiency, and environmental influences [7; 8]. Understanding and measuring adaptability require a holistic approach that integrates diverse data sources and utilizes sophisticated models capable of capturing intricate patterns within these datasets. Even though machine learning models perform predictably well across a range of areas, their opacity poses a substantial difficulty when used in educational settings. Interpretability issues make it more difficult for stakeholders to understand model predictions and restrict the discovery of useful information that is essential for creating customized interventions that improve student flexibility. In order to close this gap, new developments in interpretable machine learning methods provide ways to decipher how complicated models make decisions [9; 10; 11]. In response to the evolving challenges and opportunities in education, this study innovates by introducing a comparative analysis of a novel combination of interpretable machine learning models tailored to enhancing our understanding of student adaptability. Through the meticulous employment of SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), Anchor, Accumulated Local Effects (ALE), and Counterfactual explanations, we navigate the intricacies of balancing model interpretability against predictive accuracy. By leveraging a dataset rich in student attributes and adaptability levels, our research offers unprecedented insights into the predictors of adaptability, navigating the complexities inherent in educational data. Grounded in cutting-edge techniques and established educational theories, our methodology facilitates a detailed examination of the myriad factors influencing student outcomes. The application of machine learning in this context, with its capacity to analyze complex datasets and reveal intricate patterns, marks a significant advancement in educational research. This study not only underscores the predictive power and interpretability of machine learning models but also highlights their potential to inform targeted, effective educational interventions, thereby pioneering a comprehensive exploration aimed at unveiling and acting upon the factors influencing student adaptability, thereby fostering informed and personalized educational strategies. The rest of the paper is arranged as follows: Section 2 provides an overview of related work on XAI and interpretable models for student adaptability.