# Prediction of Students' Adaptability Using Explainable AI in Educational Machine Learning Models
## 5 Comparative Analysis of XAI Techniques and Synthesis of Findings  
After completing an extensive training program involving a varied set of machine learning models, our research delved into a thorough examination of model predictions using a range of XAI techniques. Each technique offered unique insights into the decision-making processes underlying the adaptability predictions, with SHAP and ALE providing a global view of feature contributions, LIME and Anchors elucidating local, instance-specific explanations, Counterfactuals revealing the sensitivity of predictions to feature perturbations, and ALE plots assessing the average feature effects across the data distribution. A comparative analysis of the XAI outputs revealed both similarities and divergence in feature importance rankings and explanations. Accordance across methods reinforced the perceived significance of certain features, such as 'class duration' and 'financial condition', suggesting these are robust determinants of adaptability predictions. Divergences prompted a careful reconsideration of complex feature interactions and model behavior in various scenarios, guiding further analytical probing.  
This study leverages a diverse set of XAI techniques, including SHAP, LIME, Anchors, Counterfactual, and ALE, to comprehensively interpret student adaptability prediction models. The amalgamation of these methods sheds light on key features influencing adaptability levels, contextualizing predictions within the educational landscape. This holistic understanding forms the foundation for potential educational interventions, emphasizing the necessity of considering engagement duration and addressing socioeconomic factors.  
The research makes a unique and valuable contribution to the areas of machine learning interpretability and educational policy-making by utilizing a combination of different XAI approaches to clarify the decision-making processes of educational data models. It not only enhances the interpretability of educational data through a systematic comparative analysis of various XAI techniques but also identifies and examines the complex socio-economic, institutional, and demographic factors influencing student adaptability. The thorough exploration and results of this study provide the basis for developing detailed and targeted interventions. A notable feature of this research is its innovative methodology, which integrates and compares insights from several XAI techniques to gain a comprehensive understanding of model predictions. This method offers a detailed perspective on how various factors affect student adaptability, enabling the creation of well-informed and impactful educational policies and practices. By bridging the theoretical aspects of machine learning with practical, actionable insights, the study demonstrates the value of XAI in crafting educational strategies that are both equitable and tailored to enhance student outcomes. These contributions represent a significant advancement in the responsible application of AI within the educational sector, ensuring that predictive models are leveraged in a manner that is both transparent and beneficial to student learning and adaptability.