# Predicting Student Academic Performance by Means of Associative Classification
## 4 Results
### Model Exploration  
To explore the high-quality rules generated by the \(L^{3}\) classifier we focused the attention on the most reliable student profiles. Specifically, we first counted, for each high-quality rule, the number of classified students as well as the percentages of correctly and incorrectly classified ones. Next, we analyzed the subset of high-quality rules that (i) have been applied to at least 1% of the students, and (ii) have exclusively produced correct predictions. All the other rules were discarded, as they potentially generated unreliable predictions. Rule exploration aims at answering the research questions RQ2 and RQ3 posed in Section 1. It entails exploring model interpretability since the extracted rules can be easily understood by non-expert end-users. Figure 2: Algorithms’ comparison in terms of F1-score, precision, recall and balanced accuracy of class _fail_. Research question (RQ2): What are the most discriminating features to forecast the exam success rates at different time points? To answer to this question, we analyzed the frequency of occurrence of the single features and of the pairs of features in the selected student profiles. Figure 3 shows the percentage of rules including specific features in their antecedent at three representative time points: \(t_{0}\) (before the entry test), \(t_{1}\) (after the entry test and the BS course choice, but before the start of the semester) and \(t_{5}\) (at the end of the first semester). The plot highlights the features that mostly influence student performance separately for each course and time point. At \(t_{0}\) only the student-specific features are considered and, unsurprisingly, the most important one is the high school grade (HS-gr). Age turned to be strongly correlated with the success rate as well, because students who are older than average often achieved bad results during high school. At \(t_{1}\) the entry test grade (GRE-gr) plays a significant role in rate prediction, in combination with HS-gr. The BS course choice (e.g., mechanical engineering vs computer science engineering) is also an important feature, because the attitude of the students towards the different disciplines depends on the perceived importance in their future. At \(t_{5}\) the effect of the download of educational material (e.g., MA-mat) and of the streaming activity (e.g., MA-str) for the different courses are visible. The high school degree remains very relevant, while in general age decreases its importance. The activities carried out in one course may influence other courses. For example, since MA provides students with the basic concepts for both LA and PH, the exam grade (whenever available) is particularly discriminating. The radar plots in Figure 4 deepen the feature analysis for the 2 nd-semester LA course. Features are grouped in different categories (personal data and scholastic history, entry test and BS course choice, activity in two sample courses, MA and CS, educational material download and exam success) and their importance is compared at all the time points (from 0 to 9). For example, graph (a) confirms that the high school grade (HS-gr) has an important impact on student performance during the whole academic year. On the  
Figure 3: Frequency of occurrence (in percentage) of the features appearing in the rule antecedents at different time points. contrary, gender is relevant only at the beginning, because the student activity during the semester becomes more and more important to foresee exam success rate. Graph (b) shows that the test grade (GRE-gr) and the BS course choice have an impact only in the first semester, and during the second semester (when the LA course is on) other features become much more important. Graph (c), about students' activity in the MA course, shows that studying MA is strongly related to passing the LA exam.