## 5 Discussion
### Insights from the XAI Experiment  
The current study used a well-characterized student performance data set to exploit modern ML methods to interpret the prediction results. Both types of modern ML methods (i.e., CNN-LSTM and Conv-LSTM) demonstrated the best and the second-best performances from the experiments. This work shows that modern DL techniques are not necessarily black boxes. Descriptive knowledge can be presented to explain the assessments of features regarding future results, especially in the context of the presented study, i.e., student performance early prediction. From the students' point of view, black-box understanding might be unacceptable because they need reasons to categorize them as at-risk students [37]. Therefore, an explainable prediction could improve student experience during learning in the VLE. Accuracy of the prediction model can be essential but explainability is more important [38]. Explainability is a must-have feature on AI-based prediction systems without limiting the accuracy. Nonetheless, explainability is a feature that supports the development of ESPP but does not replace the role and assessments made by teachers.  
As depicted in Figure 5, the local explanation does not completely represent the global explanations. To simplify the local explanations, global feature importance needs to be calculated. The instructor can use the feature importance to improve the study experience in the VLE by assessing the ESPP globally. A basic idea to summarize local explanations provided by LIME is by calculating local predictions for many samples and assessing the mean value assigned to each feature importance. Figure 6 illustrates the feature importance rating derived from the explainability methods for the LSTM prediction model. The bar heights denote mean values and error bars indicate standard deviation over data samples. These values were normalized to the range 0 to 1. The model rated assignment consistently as the most important feature. The model pointed out forum, label, file, and homepages with a comparable importance score for important moderate features. For less important features, the results were insignificant for URL, page, and quiz.  
Given the trade-off between performance and explainability, the general performance of modern ML techniques outperformed traditional ML techniques. The results revealed that the proposed CNN-LSTM and Conv-LSTM models provided comparable explainability while achieving higher accuracy than the baseline models. These promising predictive  
Figure 6: The global feature importance from the data set involving recorded activities of all students.  
models have greatly accelerated the development of explainable VLE. However, as displayed in Figure 5, technical presentations of at-risk activities might be less sufficient. A translated at-risk activity in the easy-to-understand ranking (e.g., from \"at-risk\" to \"fair\" to \"outstanding\") might provide a better understanding. In addition, the security aspect, specifically in the federation of the VLE system, could be further analyzed as the federated learning approach could be used to securely train anonymous data sets [39]. This study also encourages future research to evaluate hybrid learning (i.e., online and offline learning) with detailed-class labeling, improving the user experience during the learning process.