  Machine learning and deep learning models are pivotal in educational
contexts, particularly in predicting student success. Despite their widespread
application, a significant gap persists in comprehending the factors
influencing these models' predictions, especially in explainability within
education. This work addresses this gap by employing nine distinct explanation
methods and conducting a comprehensive analysis to explore the correlation
between the agreement among these methods in generating explanations and the
predictive model's performance. Applying Spearman's correlation, our findings
reveal a very strong correlation between the model's performance and the
agreement level observed among the explanation methods.
