# Toward Predicting Student's Academic Performance Using Artificial Neural Networks (ANNs)
## 4 Results
### Predictors and Outcome Variables
The socioeconomic variables were seen in some studies, and some involve family details. \begin{table}
\begin{tabular}{c c c} \hline
**Ref.** & **Predictors** & **Outcome** \\ \hline \multirow{2}{*}{[20]} & Number of messages on the LMS both viewed and posted, content & \\  & creation, files viewed, quiz efforts & Course outcome \\ \hline \multirow{2}{*}{[21]} & (GPA) for the first three years of study & Fifth-year and final Cumulative Grade Point \\  & & Average (CGPA) \\ \hline \multirow{2}{*}{[22]} & GP scored in some subjects & CGPA \\ \hline \multirow{4}{*}{[23]} & The first year of university score, high school score, subject result score & \\  & of math I and II, Electronics I, Electrical Circuit I, the number of the & \\  & credit that the student passed during the first year of college, & \\  & demographic variables, type of high school (private or public), location & \\  & of the school (inside or outside of Palestine), and the student gender & \\ \hline \multirow{2}{*}{[24]} & First three years, residency training records, gender & success on pre-board exams \\ \hline \multirow{2}{*}{[25]} & Gender, training, forum, chat, discussion, upload assistance, & \\  & The message, quiz training, and total login & \\ \hline \multirow{4}{*}{[26]} & Dataset 1: 30 attributes ([PERSON] and [PERSON], 2008), data set 2:8 attribute, & Data set 1: 3 output first-semester grade, \\  & GPA, CPA, no. of scholarships, score 1,2,3,4 and gender, data set 3:3 & second-semester grade and final grade \\  & input variables (step duration, incorrect and hints) obtained from & \\  & ([PERSON] et al., 2010) & 3 2 outputs (Correct First Attempt and Correct) & \\  & & obtained from ([PERSON] et al., 2010) \\ \hline \multirow{2}{*}{[27]} & Cognitive and demographic variables & (grade-point average, GPA). \\ \hline \multirow{2}{*}{[28]} & 21 attributes, passed the course and the student’s affiliation & Predict the mean grades and credits of the students \\ \hline \multirow{4}{*}{[29]} & 39 attributes, students’ gender, region, educational level, age range, & \\  & neighborhood crime rate (IMD), number of times they have previously & \\  & participated in the course, enrolled credits, disability, and the final & Student success \\  & exam result (passed/failed). In addition, the number of times the & \\  & student has interacted with any of the online course contents was & \\  & counted throughout the courses & \\ \hline \multirow{2}{*}{[30]} & Gender, content score, time spent, number of entries to content, & Student performance \\  & homework score. Attendance, archived courses & \\ \hline \multirow{4}{*}{[31]} & University entrance examination score, the average overall score of & \\  & high school graduation, examination, the elapsed time between & \\  & graduating from high school and obtaining university admission, & \\  & location of student’s high school, type of high school attended, gender & \\ \hline \multirow{2}{*}{[32]} & 12 input variables, classified into academic, parent, person, managerial & Student pass or fail \\  & and social & \\ \hline \multirow{2}{*}{[33]} & Exam results and other factors, such as the location of the student’s & Student performance \\  & high school and the student’s gender & \\ \hline \multirow{2}{*}{[34]} & Socioeconomic variables, school type variables, student’s previous & Student performance \\  & achievement variables, tutor’s expertise variables & \\ \hline \multirow{4}{*}{[35]} & Students’ internet accessing details including the total length of & \\  & internet time, active periods, traffic, college entrance examination & \\  & scores represent the students’ initial knowledge level and learning & Student grade in subjects \\ \cline{1-1}  & book-borrowing numbers, and birth dates, first midterm & \\ \cline{1-1}  & examination scores & \\ \hline \end{tabular}
\end{table} 
Table 3: Predictors and outcomes used in the studies.