# Predicting Student Academic Performance by Means of Associative Classification
###### Abstract  
The Learning Analytics community has recently paid particular attention to early predict learners' performance. An established approach entails training classification models from past learner-related data in order to predict the exam success rate of a student well before the end of the course. Early predictions allow teachers to put in place targeted actions, e.g., supporting at-risk students to avoid exam failures or course dropouts. Although several machine learning and data mining solutions have been proposed to learn accurate predictors from past data, the interpretability and explainability of the best performing models is often limited. Therefore, in most cases, the reasons behind classifiers' decisions remain unclear. This paper proposes an Explainable Learning Analytics solution to analyze learner-generated data acquired by our technical university, which relies on a blended learning model. It adopts classification techniques to early predict the success rate of about 5000 students who were enrolled in the first year courses of our university. It proposes to apply associative classifiers at different time points and to explore the characteristics of the models that led to assign pass or fail success rates. Thanks to their inherent interpretability, associative models can be manually explored by domain experts with the twofold aim at validating classifier outcomes through local rule-based explanations and identifying at-risk/successful student profiles by interpreting the global rule-based model. The results of an in-depth empirical evaluation demonstrate that associative models (i) perform as good as the best performing classification models, and (ii) give relevant insights into the per-student success rate assignments. learning analytics; classification and regression algorithms; blended learning models +
Footnote †: journal:A strong limitation of many state-of-the-art machine learning models is their limited interpretability and explainability. Model interpretability is ensured whenever the classification model can be easily understood and explored, whereas explainability entails understanding the underlying model characteristics that mainly influence its predictions [8]. In the context of student performance prediction, the lack of model interpretability and explainability could be particularly critical, because teachers can neither verify the appropriateness of the success rate prediction nor tailor the subsequent actions to the actual learners' needs. Previous attempts to address students' performance prediction using interpretable classification models such as decision rules and fuzzy rules have already been made (e.g., [9; 10; 11]). However, as discussed in [12], these models may suffer from the problem of adaptability since they could take decisions based on small samples of data and, therefore, the final classifier could not be representative of the overall trends. This paper proposes to early predict university-level student performance by means of associative classification. Associative classifiers are intepretable yet accurate classification models consisting of association rules [13]. Associative classifiers are known to be more accurate than traditional decision trees and rule-based algorithms. They overcome the problems of decision tree and fuzzy models by focusing on the features of the given test instance, thus increasing the chance of generating more rules that are useful for classifying the test instance [12]. Associative rules represent strong implications between recurrent combinations of feature values and the predicted success rate. For example, a rule may indicate that if a student is female and she has accessed the majority of the course modules, then she is very likely to pass the upcoming exam. The rules are automatically extracted from a (potentially large) labeled dataset, filtered and sorted by relevance, and then applied to unlabeled data. Thanks to their inherent interpretability, associative models can be manually explored and validated by domain experts.