# Transfer Learning from Deep Neural Networks for Predicting Student Performance
## 4 Research Methodology
### Data Analysis  
In the present study, we selected data regarding five compulsory courses of two undergraduate programs offered by the Aristotle University of Thessaloniki in Greece. More precisely, three courses (Physical Chemistry I (Spring 2018) and Analytical Chemistry Laboratory (Spring 2018, Spring 2019)) were offered by the department of Chemical Engineering, while two courses (Physics III (Spring 2018, Spring 2019)) were offered by the department of Physics. Table 1 provides detailed information regarding the gender and target class distribution of the five courses.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Course** & **Female** & **Male** & **Pass** & **Fail** \\ \hline C1: Physical Chemistry I (Spring 2018) & 122 & 43.3\% & 160 & 56.8\% & 134 & 47.5\% & 148 & 52.5\% \\ C2: Physics III (Spring 2018) & 90 & 50.0\% & 90 & 50.0\% & 74 & 41.1\% & 106 & 56.9\% \\ C3: Analytical Chemistry Lab (Spring 2018) & 57 & 48.2\% & 72 & 55.8\% & 105 & 81.4\% & 24 & 18.6\% \\ C4: Physics III (Spring 2019) & 80 & 50.6\% & 78 & 49.4\% & 68 & 43.0\% & 90 & 57.0\% \\ C5: Analytical Chemistry Lab (Spring 2019) & 61 & 52.1\% & 56 & 47.9\% & 100 & 85.5\% & 17 & 14.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Gender and target class distribution of the courses.  
Each course was supported by an online LMS, embedding a plethora of resources and activities. The course pages were organized into topic sections containing the learning material in the form of web pages, document files and/or URLs, while the default announcements forum was enabled for each course allowing students to post threads and communicate with colleagues and tutors. Each course required the submission of several assignments, which were evaluated on a grading scale from zero to 10. All sections were available to the students until the end of the semester, while the course final grade corresponded to the weighted average of the marks of all submitted assignments and the finishing exam. Note that successful completion of the course required a minimum grade of five.  
For the purpose of our study, the collected datasets comprised six different types of learning resources: forums, pages, recourses, folders, URLs and assignments (Table 2). For example, course \(C_{1}\) was associated with one forum, seven pages, 17 resources, two folders and eight assignments, three of which were compulsory. Regarding the forum module, we recorded the total number of views for each student. We also recorded the total number of times students accessed a page, a resource, a folder or a URL. Moreover, two counters were embedded in the course LMS, aggregating the number of student views (course total views) as well as the number of every type of recorded activity for a student (course total activity). Learning activities that were not accessed by students were not included in the experiments, while a student who did not access a learning activity was marked with a zero score. Finally, a custom Moodle plugin was developed, enabling the creation of the five datasets [30].  
It is worth noting that there were certain differences among the five courses (Tables 1 and 2). At first, they were offered by different departments (Physics and Chemical Engineering) and they had different format and content. Although courses \(C_{2}\), \(C_{4}\) and \(C_{3}\), \(C_{5}\) encompassed the same topic--that is, Physics and Chemistry, respectively--their content varied depending on the academic year of study. In addition, courses \(C_{1},\ C_{2},\ C_{4}\) were theoretical (Physical Chemistry and Physics), while \(C_{3},\ C_{5}\) were laboratory courses (Analytical Chemistry Lab). Moreover, each course required the submission of a different number of assignments. Finally, it should be noted that different students attended these courses.