# Predicting Student Academic Performance by Means of Associative Classification
## 4 Results
### Learning Context and Related Data  
The university provides B.S. and M.S. engineering courses and adopts a blended learning model. The blended model relies on a massive educational video service, which delivers video recordings of the in-class lectures. The access to the video lectures of a course is allowed to all the enrolled students. Hence, students can exploit video lectures as complementary materials in addition to in-class lectures [43]. Since 2010 the university has video-recorded in the classroom all the courses of the first year of the B.S. in Engineering, that is common to all B.S. engineering curricula. The number of students enrolled to the 1 st-year courses is approximately 5000 per academic year. The learning platform of the university traces the students' interactions with the provided educational materials. Specifically, through the platform students can (i) access and download educational materials (e.g., slides, lecture notes, exam simulations), and (ii) watch (streamed from the educational Web service or downloaded) the video-records of the in-class lectures. As a case study, we applied the proposed methodology on data acquired in the academic year 2018-2019. Specifically, we focused on predicting the exam success rate for all the students enrolled to the 1 st-year B.S. courses. The 1 st-year curricula encompasses the following courses: Mathematical Analysis (MA), Chemistry (CH), Computer Science (CS), Linear Algebra (LA), and Physics (PH), plus an elective course which is not considered in the present analysis because data are not comparable. In the considered academic year, courses MA, CH, and CS were held in the first semester (i.e., from 1 October 2018 to 15 January 2019), while courses LA and PH were held in the second semester (from 1 March 2019 to 15 June 2019). Three examination sessions were scheduled within an academic year: (i) the 1 st semester (winter) session, which is held at the end of the first semester (i.e., from 22 January 2019 to 28 February 2019), (ii) the 2 nd semester (summer) session, which is held at the end of the second semester (i.e., from 16 June 2019 to 22 July 2019), (iii) the autumn session, which is held after the summer break (i.e., from 1 September 2019 to 30 September 2019). Students are free to attend any and all examination sessions, provided that they have already attended the course. To early identify at-risk students, for each student and exam we predicted the success rate in the upcoming examination session. Specifically, we trained separate classifiers at the twelve different time points reported in Table 2. For the sake of brevity, each time point will be hereafter denoted by the corresponding identifier. At each time point we predicted the outcome of the considered student in the upcoming exam (independently of the actual exam attendance). More specifically, for the 1 st-semester courses (MA, CH, CS) at time points from \(t_{0}\) to \(t_{5}\) we predicted the success rate of the winter session exam, from points \(t_{6}\) to \(t_{9}\) we predicted the success rate of the exam held in the summer session, while at points \(t_{10}\) and \(t_{11}\) we predicted the rate of the autumn session. For the 2 nd semester courses, from \(t_{0}\) to \(t_{9}\) we predicted the success rate of the exam in the summer session (because the winter session is not eligible for students at first enrolment), while at points \(t_{10}\) and \(t_{11}\) we predicted the rate of the autumn session. For the courses held in the first semester (MA, CH, and CS), the first exam after course attendance is scheduled in the winter session, while for the other courses (LA, PH) the first exam is scheduled in the summer session. However, for all the 1 st-year courses students may undergo the corresponding exam in any of the aforesaid sessions (e.g., they can undergo the MA exam the first time during the summer session).