# Explainable AI in Education: Techniques and Qualitative Assessment
## 4 Results  
The architecture of a DT model that was created using the dataset is shown in Figure 3, which offers important insights into the variables affecting a student's success or failure. The most important feature, \"assessment_count\", which indicates how many assessments a student has finished, is highlighted at the root node of the tree. The student is predicted by the tree to fail if the assessment count is four or fewer, but passing is more likely when the assessment count is larger. This conclusion makes sense since students often do better the more assessments they complete. There are other features that influence the decision-making process besides the root node. The second most significant attribute was the \"score\", which represents the average performance on assessments. Students have a higher chance of passing if they do well on assessments. It is interesting to note that other variables like \"delay\", which can indicate how long it takes to complete assessments, and \"sum_click\", which represents all online activity or interactions, also matter. These variables demonstrate that a student's chances of success may be raised by making more efforts and by actively engaging. The LIME approach was used to give the model predictions' interpretability after the ANN model was applied to the dataset. With LIME, we can deconstruct this model's intricate decision-making process and gain an understanding of the separate roles that each feature plays in generating a forecast. We may assess how various factors affect the model outputs and gain a better understanding of the reasoning behind the predictions by concentrating on specific students, like Students 29-30. By outlining the relative significance of various features in predicting the class, LIME helps to make these opaque models clearer, as demonstrated by the accompanying visualizations. As seen in the visualization for Students 29-30 (see Figure 4), the LIME approach was used to explain the predictions produced by the ANN model. Green bars signify positive contributions that support the outcome (PASS), and red bars signify negative contributions that contradict the result (FAIL). The plot features are displayed along the y-axis. For example, the feature code_presentation_2014\(\leq\) 0.00 for Student 29 has a significant negative contribution, which contradicts the PASS result. However, features such as code_module_CCC\(\leq\) 0.00 and sum_click > 3.52 strongly support the PASS result. Figure 3: A visualization of the top portion of the DT model, focusing on the most significant features and splits due to space constraints. Based on this approach, “assessment_count” emerges as the most important feature in the model. The SHAP method was applied to make the model predictions and understand the individual roles that each feature plays in producing the predictions. By analyzing specific students, such as Students 501 and 801, we evaluated how different factors influence the model's outputs and gain deeper insights into the logic behind the predictions. SHAP highlights the relative importance of features in determining the predicted class, as demonstrated by the accompanying heatmap visualization. SHAP was used to explain the model's predictions such as Students 501's and 801's visualization (see Figure 5). Green shades show negative contributions that contradict the result, whereas orange shades show positive contributions that support the outcome. For instance, the feature code_presentation_2013B significantly contributed negatively (\(-0.28\)) to the result for Student 801. Conversely, attributes like score have a positive impact and support the forecast. The mean and standard deviation, computed across 50 repetitions and five-fold cross-validation, were the sets that were compared with the feature importance (see Table 3). These metrics offer a thorough assessment of the reliability and accuracy of the model across various data subsets.