# Prediction of Students' Adaptability Using Explainable AI in Educational Machine Learning Models
## 4 Results and Discussion
### Result Analysis with XAI Techniques
#### 4.3.1 SHAP (Shapley Additive exPlanations)  
Using the mean SHAP as a guide, Figure 4 provides a detailed examination of how different factors affect the adaptability levels of students. SHAP values quantify the contribution of each feature to the predictive model's output, offering a measure of importance based on average impact magnitude. This bar graph segregates the influence of features into three adaptability predictions: High (green), Moderate (blue), and Low (pink). The length of each bar represents the mean absolute SHAP value, which is a composite measure of both the strength and consistency of a feature's effect on the model's predictions. 'Class Duration' emerges as a dominant feature, its substantial mean SHAP value indicating a  
Figure 3: Model accuracy comparison.  
significant and positive correlation with student adaptability across all levels. Its greatest impact is observed in the High adaptability category, suggesting that extended instructional periods may enhance a student's ability to adapt to varying educational demands.  
Financial Condition' is another prominent feature, especially in predicting Low adaptability, highlighting the potential obstacles faced by students with fewer economic resources. Similarly, 'Institution Type' displays a varied influence, with a positive association for High adaptability, possibly reflecting the superior resources and support available at certain institutions.  
On the other hand, 'Load-shedding' predominantly affects Low adaptability predictions, alluding to the detrimental effects of inconsistent electricity on educational continuity. Meanwhile, demographic attributes such as 'Age' and 'Gender' show moderate effects, indicating their complex but less pronounced roles. Technology-related attributes, namely 'IT Student' status and 'Device' usage, although impactful, have lesser mean SHAP values, suggesting their influence is secondary compared to educational and socio-economic factors.  
This figure illustration using SHAP values highlights how diverse student adaptation is. It gives educational stakeholders a data-driven platform to build focused interventions that enhance positive aspects and lessen negative ones, creating a climate that encourages flexible learning.  
The use of SHAP in a prediction model to ascertain student adaptability levels is seen in Figure 5. The figure is divided into three parts: (a) the attributes under analysis, (b) the corresponding SHAP values, and (c) the force plot. The base value (f(x)) represents the average output when no features affect the prediction. In this instance, the model predicted a 'Low' adaptability level, indicated by a prediction value of 1. Features with red segments, such as 'Education Level' and 'Network Type', contribute to a higher adaptability prediction ('Moderate' or 'High'), while features with blue segments, such as 'Financial Condition' and 'Self Lms', are associated with a lower adaptability prediction ('Low'). The force plot elucidates the model's complex reasoning for an individual prediction. For example, a favorable 'Financial Condition' and access to 'Internet Type' influence the adaptability prediction away from 'High', while the absence of engagement with 'Self Lms' and lower 'Education Level' push the prediction towards 'Low' adaptability. The analysis of this figure can help educational institutions understand the complex causes of student adaptability and fit their resources and interventions accordingly.  
Figure 4: (Global Explanation) Mean impact of features on predicted student adaptability levels using SHAP values.