# Toward Predicting Student's Academic Performance Using Artificial Neural Networks (ANNs)
## 5 Discussion
### Input Variables Used  
The input variables used in the reviewed studies did not seem to follow a specific pattern except for the use of the student scores and GPA from previous study years. Based on the context of this study, the variables and section of the input varied greatly from one study to another. As an example, when [17] attempted to predict students who will perform better while studying at the Faculty of Engineering and Information Technology, they chose the input variables that were believed to be related to student's understanding of Engineering. The variables included Math I and II, Electronics I, Electrical Circuit I in high school. These inputs were combined with first-year scores in the university. Not all the studies used academic scores; [27] used cognitive variables to determine student performance. The study applied variables such as Working memory (Intelligence and ability to acquire new knowledge), Attention, and Learning strategies. The study used several computerized tasks such as Attention Network Test (ANT) adapted from [41], Automated Operation Span adapted from [42], and the Learning Strategies Questionnaire adapted from [43; 44; 45]. From the surveys, the study extracted the input variables of General Reaction Time, Attentional Networks, Working Memory Capacity, Reaction Time Operation. Cognitive resources/Cognitive processing, Anxiety Management, Alerting Attention, and Orienting Attention. The study also included demographic variables which are related to gender and parents' educational level and occupation. These factors seem to be implemented in many studies [22; 25; 27; 32; 34] as usually this information is easy to be obtained. In addition, the majority of studies used GPA or the various subject scores, [21; 24; 26; 31; 34], due to similar reasons, as this information was easily obtained, especially in a retrograde setting to predict future scores or success and failure of the same students. Studies that examined the virtual learning environment [20] chose corresponding input variables such as the number of messages on the LMS viewed and posted, content creation, files viewed, and quiz efforts. These data were easily obtained and recorded in the LMS. Relatively similar variables were used by [25] in predicting the e-learning students' graduation levels. The input variables included Gender, Total Login, Quiz Training, Message, Upload Assistance, Forum, Training, Chat, and Discussion. The two studies yielded a high accuracy of the ANN, achieving 98.30% and 97.9, respectively. The virtual learning environment is becoming increasingly popular in a recent study [29] which predicts student success at the master levels. The study used extracted information from the Moodle platform, which aimed to monitor and extract certain input variables included access, number of visits, clicks per hour time of each access, Clicks in resources and Messages in the forum. Moreover, the result indicated enhancement in the student performance with the increase in each of the variables. Students' level of distraction from internet use and the time spent on the internet were among the variables applied in examining the grades of honor students in the [35] study. This study employed a combination of exciting inputs that were believed to affect student performance. These input variables included the previously mentioned variables, number of books borrowed from the library, college entrance examination scores, and the first midterm score; the model demonstrated satisfactory accuracy up to 84%. Similar observations were reported in other literature [46], and also used in-term or end-of-term scores as input factors in circulating student success. In a review conducted, it was discovered that overall grade averages and internal assessments (quizzes, lab studies, and attendance) are commonly used in predicting student results. External assessments (final exam) and demographic characteristics (gender, age, family status, and impairment status) were also used to forecast student progress in certain research.