# Prediction of Students' Adaptability Using Explainable AI in Educational Machine Learning Models
## 4 Results and Discussion
### Result Analysis with XAI Techniques
#### 4.3.4 Accumulated Local Effects (ALE) Explanation  
The ALE feature importance analysis, as visualized in Figure 7, provides compelling insights that can guide educational strategies and policy-making. The significant importance of 'Class Duration' suggests that students benefit from extended learning sessions, which may offer a more immersive educational experience. This finding supports the pedagogical approach of increasing instructional time to enhance understanding and retention. Policymakers might consider revising academic schedules to integrate long class periods or more intensive study sessions that could better support students' adaptability and learning outcomes. The importance of 'Age' as a predictive factor of adaptability underscores the importance of developmental considerations in educational planning. Younger students may require more structured support to foster adaptability, such as resilience training and social-emotional learning programs. In contrast, older students may benefit from opportunities that challenge their adaptability skills, such as project-based learning and collaborative assignments that mirror real-world scenarios. The impact of 'Financial Condition' on adaptability predictions reinforces the link between economic factors and educational success. This suggests an urgent need for policies that aim to level the playing field, such as providing financial aid, resources, and support systems for students from less affluent backgrounds. Ensuring that all students have equal access to educational resources is not just a matter of fairness but also a strategic investment in the adaptability and resilience of the future workforce. Gender, network type, and education level highlight the multifaceted nature of adaptability. These findings suggest a tailored approach where educational interventions are sensitive to gender dynamics, technological access, and institutional characteristics. For example, initiatives to bridge the digital divide by improving network connectivity can have far-reaching effects on students' ability to access and engage with digital learning platforms, a necessity in an increasingly connected world. Furthermore, the lower importance of features such as 'Self Lms', 'IT Student', and 'Device' does not diminish their value but rather indicates that their influence on adaptability may be more conditional or indirect. This could inform a carefully balanced approach to technology integration in education, ensuring that technology enhances learning without widening the gap between different student groups. Lastly, the feature 'Load-shedding' highlights the external challenges students face, pointing to the broader social and infrastructural issues that can affect educational outcomes. Addressing such challenges may require collaborative efforts that extend beyond the education sector, involving partnerships with community organizations and government agencies to provide stable learning environments. Figure 7: ALE feature importance for predicting student adaptability level. The ALE feature importance analysis thus serves as a guide for developing comprehensive educational policies and practices that consider the complex interplay of individual, institutional, and societal factors affecting student adaptability. By focusing on these key features, educators and policymakers can create more supportive and effective learning environments that cater to the diverse needs of students, fostering an educational ecosystem that is both equitable and conducive to developing adaptable learners. Our analysis utilized ALE plots to visualize the impact of three critical features--Class Duration, Age, and Financial Condition--on the predicted adaptability levels of students. The resulting plots offer insightful revelations that intersect with contemporary educational theories and bear significant policy implications, see Figure 8.  
Class Duration:  
The ALE plot for 'Class Duration' indicates that longer class times are positively associated with 'High' adaptability predictions.