# Explainable AI in Education: Techniques and Qualitative Assessment
## 5 Discussion  
In this experiment, two ML models--the DT and ANN model--were assessed according to their interpretability and performance. The performance indicators for the DT and ANN classifiers demonstrate clear variations in their efficacy, as seen in Table 3. While the ANN model showed a better accuracy of 92.09% and a slightly higher standard deviation of 0.0012, the DT attained an accuracy of 89% with a standard deviation of 0.0002. Overall, these metrics (accuracy, recall, F-score, and precision) show that the ANN classifier performs better than the DT on all assessed variables, indicating increased reliability, as well  
Figure 6: Aggregated feature importances from LIME explanations. Based on this approach, “num_of_attempts” emerged as the most important feature in the model. Figure 7: Aggregated feature importances from SHAP explanations. Based on this approach, “assessment_count” and “score” emerged as the most important features in the model. as effectiveness, in classification tasks. Nevertheless, performance by itself does not give a whole picture, especially when it comes to the explainability and stability of the model. Table 4 presents the stability and fidelity of the different explanatory methods used with the ANN and DT classifiers. The DT proved this, achieving a 100% score for both fidelity and stability. This suggests that the DT's explanations were completely consistent between instances and that the behavior of the local surrogate models accurately approximated it. However, for this particular instance (see Table 5), the original model's prediction was incorrect, as seen by the disparity between the real class and the predicted class. Also, if both the original and surrogate models predict the same incorrect class, the fidelity score will still be 100%. Due to several causes, such as decision boundary restrictions, model complexity, or insufficient representation of particular instances in the training data, the DT model could have misclassified specific instances and predicted Class 0 instead of Class 1. This model tends to suffer from overfitting, particularly when dealing with noisy data or many features that are weakly correlated with the target outcome. The DT splits the data sequentially on individual features, which can lead to highly specialized branches that do not generalize well to new, unseen data. For instance, strong predictors like \"assessment_count\" were heavily utilized in the top splits of the DT model created for the OULA dataset, as has been previously reported. But this binary decision could miss more complex relationships, such as how the combination of delayed assignment submissions (delay with 0.044912) and total clicks on resources (sum_click at 0.033845) influence overall performance. The simple splits in the tree struggle to capture these complex interactions between features, which results in overspecialized branches that are not very portable to new student data. The model's accuracy of 89% indicates that its overall performance is still good despite these poorer predictors. For the most significant predictors, it is evident how interpretable the model is; nevertheless, its understanding of the weaker features is rather limited. However, LIME, when applied to ANN model, showed marginally less fidelity (80%) and stability (80%), but it still had many benefits, especially when it comes to handling complex data and capturing intricate patterns. SHAP, when also applied to the ANN model, demonstrated comparable fidelity (79.39%) but significantly outperformed LIME in stability (98.43%). Both SHAP and LIME apply for local explanations, helping to interpret individual predictions by attributing importance to specific features. The lower fidelity score suggests that the SHAP and LIME surrogate models do not fully replicate the ANN's predictions; however, the ANN is particularly good at picking up on small relationships within the data that more simple models, like the DT, could miss.