# Predicting Student Academic Performance by Means of Associative Classification
## 4 Results
### Model Exploration
It also evidences that the streaming activity (MA-str) is mainly relevant at the end of the semester and during the exam break: students use educational materials from the beginning of the semester, but concentrate the streaming activity closer to exam sessions. Graph (e) shows that passing the 1 st-semester exams (MA-gr, CH-gr, CS-gr) has a strong influence on passing LA in the second semester (the influence is higher in case of MA than CS, coherently with the topic focus). The performance in the first semester of the first year is probably the most discriminating factor in classifying students as active or inactive. Extracting association rules allows us to explore the most relevant item co-occurrences relative to different feature combinations [40]. Figure 5 shows the importance of a subset of relevant combinations of feature for LA at different time points. For example, the scholar history (HS-loc, HS-gr) is relevant to predict success rate before any university activity (i.e., before \(t_{1}\)), including entry test and BS course choice. After that, and until almost the end of the semester (\(t_{4}\)), the high school grade and the entry test grade coupled with the BS course choice (HS-gr, BS course), (GRE-gr, BS course) became the most relevant feature combination. During the first semester (from \(t_{2}\) to \(t_{5}\)) educational material download of two courses has the strongest effect (MA-mat, CS-mat), showing student active participation. Finally, during the second semester (when LA is given), the most relevant feature combination is the number of downloads of educational material combined with the grade achieved for the MA course (LA-mat, MA-gr). Figure 4: LA: Analysis of the relevance of single features. Research question (RQ3): which combinations of feature values have been frequently used to assign the exam success rates? Tables 6-9 report a selection of high-quality rules of different types extracted from the datasets of a representative course (MA). Specifically, Tables 6-8, respectively, report the selected single-feature rules extracted from each feature set (SSC, T-MAT, STREAM-VL), while Table 9 reports a selection of mixed-feature rules. For each rule we report the average support and confidence values computed over all the five cross-validation folds as well as their corresponding standard deviations. The table content is organized as follows. Each rule has a progressive number. For each rule we report the time point at which the rule was extracted (from \(t_{0}\) to \(t_{11}\), according to the notation introduced in Table 2) and its content (in the form body \(\rightarrow\) head) as well as its main quality indices. For the sake of brevity, the discretized feature values within each rule are abbreviated as None (N), Low (L), Fair (F), or High (H). Since similar rules may be extracted for the other courses, for each rule we indicated which courses have similar rules in column _Similar rules_. Finally, a short comment on the rule is given in Column _Description_. The rules give interesting insights into the career progress of the enrolled students. For example, rule 4 in Table 6 indicates that the students who received fairly low admission grades and who are older than average are more likely to fail the MA exam. To prevent exam failures, the university could organize recovery courses for the students who have not achieved a sufficient grade in the part of the admission tests related to mathematics. Rule 10 in Table 7 indicates that the students who have downloaded a large part of the educational materials by the first half of the MA course are likely to pass the exam. The extraction of such a profile prompts specific reinforcement action, i.e., stressing the importance of using educational material and encourage students to keep going like they are doing.