# Predicting Student Academic Performance by Means of Associative Classification
## 3 Materials and Methods
### Profile Extraction and Ranking  
The associative models generated by the \(L^{3}\) classifier at different time points are collected and analyzed to gain knowledge about classifiers' decisions. Classification rules related to rate _fail_ describe at-risk student profiles. For example, rule {(_Entry test_, [60, 70]), (_Fraction of video-lectures accessed_, \(<\)5%)] \(\rightarrow\) {(_Success rate_, _fail_}) describes a profile of students who have achieved fairly good test outcomes and who have not downloaded the video-recordings of the in-class lectures. Conversely, classification rules related to rate _pass_ describe successful student profiles. Profiles can be classified as (i) at-risk profiles, if they are peculiar to the success rate _fail_, or (ii) successful profiles, if they are peculiar to success rate _pass_. Note that profiles are peculiar to a given course and period of time. Hence, they may change while considering different courses and periods. Further profile categorizations can be based on the number and type of features involved. Specifically,  
* Single-feature profiles are profiles characterized by a single feature category. According to the feature categorization reported in Section 3.1, they can be further classified as profiles on student-specific characteristics (SSC profiles, in short), profiles on student's engagement indicators (SEI profiles), or profiles on assessment scores (AS profiles) depending on the category of the reference feature. * Mixed-feature profiles are profiles that are modeled on multiple data feature categories. They extend single-feature profiles by combining features of different categories. While single-feature profiles could be identified also using decision tree models, mixed-feature ones are peculiar to associative models, which encompass rules including items belonging to multiple features in the rule antecedent. For the sake of simplicity, mixed-feature profile categories will be denoted by combining the same abbreviations indicated above of single-feature profiles. For example, SSC+AS profiles are those characterized by features belonging to both student-specific characteristics and assessment scores. For example, rule {(_Entry test_, [60, 70]), (_Fraction of video-lectures accessed_, \(<\)5%)] \(\rightarrow\) {_Success rate_, _fail_}) can be considered to build an at-risk SEI+AS profile, because the assigned success rate is _fail_, while the non-class features in the rule antecedent belong to classes _Student's engagement indicators_ and _Assessment scores_, respectively. Teachers could be interested either in looking at the profile associated with a specific student or in identifying the most recurrent student profiles. In the latter case, once an associative model has already been applied to a significant number of students, the corresponding profiles can be filtered based on their actual usage. Specifically, identifying which classification rules have actually been applied by the classifier to each student allows teachers to associate students with recurrent risk profiles. For example, if rule {(_Entry test_, [60, 70]), (_Fraction of video-lectures accessed_, \(<\)5%)] \(\rightarrow\) {_Success rate_, _fail_}) has been used to classify 5% of students, then the corresponding at-risk profile could be deemed as particularly relevant to understand the career progress of the students. At-risk and successful profiles are first ranked by decreasing number of classified students. Then, the top ranked profiles are manually explored in order to gain insight into the analyzed students. When the students described by a profile have attended the course exam, exam outcomes are used to discriminate between reliable profiles and not. For example, if the majority of the students who were classified using rule {(_Entry test_, [60, 70]), (_Fraction of video-lectures accessed_, \(<\)5%)] \(\rightarrow\) {_Success rate_, _fail_}) actually failed the exam, then the corresponding profile could be deemed as reliable.