# Toward Predicting Student's Academic Performance Using Artificial Neural Networks (ANNs)
## 4 Results
### Methods and Performance  
The prediction accuracy of students' performance varies due to many factors such as data size, evaluated variables, and the type of methods used. The methods used in the assessed studies are introduced in Table 4, including the type of ANN or algorithm used, model accuracy, and remarkable findings.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Algorithm/ANN Type** & **Model Accuracy** & **Findings** \\ \hline
[20] & MLP & 98.30\% &
\begin{tabular}{c} ANN predicted the success of the students with high \\ accuracy, very high, with 98.3\% \\ \end{tabular} \\ \hline
[21] & Probabilistic Neural Network (PNN) &
\begin{tabular}{c} Pure quadratic \\ regression at 0.957 \\ \end{tabular} & PNN predictor had the least accuracy of 85.89\% \\ \hline
[22] & NN and logistic regression & 93\% &
\begin{tabular}{c} The result indicates superior accuracy of NNs over the \\ linear regression model \\ \end{tabular} \\ \hline
[23] & Feed backpropagation & 84.60\% &
\begin{tabular}{c} The potential of the Artificial Neural Network for \\ predicting student performance \\ \end{tabular} \\ \hline
[24] & ANN, SVM algorithms, and MR & \
\begin{tabular}{c} RMSE = 0.325, \\ MAE = 0.212 \\ (MLP-ANN) \\ \end{tabular} &
\begin{tabular}{c} MLP-ANN provided the most superior outcome, most \\ minor in terms of errors \\ \end{tabular} \\ \hline
[25] & ANN Particle Swarm Optimization & 97.9 &
\begin{tabular}{c} Good accuracy of the model to predict \\ student performance \\ \end{tabular} \\ \hline
[26] & \
\begin{tabular}{c} MANFIS-S (Multi Adaptive \\ Neuro-Fuzzy Inference System with \\ Representative Sets) \\ \end{tabular} & NA &
\begin{tabular}{c} The superiority of MANFIS-S over the related \\ algorithms in term of accuracy \\ \end{tabular} \\ \hline
[27] & \
\begin{tabular}{c} Backpropagation multilayer \\ perceptron neural network, \\ \end{tabular} & 87\% to 100\% &
\begin{tabular}{c} Greater accuracy of the ANN compared to traditional \\ methods such as discriminant analysis. In addition, the \\ ANN provided information on those predictors \\ \end{tabular} \\ \hline
[28] & Feedforward MLP & NA &
\begin{tabular}{c} The combination of within-method and between \\ method triangulation provided very solid results, \\ \end{tabular} \\ \hline
[29] & \
\begin{tabular}{c} Decision tree, Random Forest, \\ Extreme gradient boosting, \\ ANN MLP \\ \end{tabular} & 78.20\% &
\begin{tabular}{c} ANN was proposed to predict a behavioral model \\ which is capable of improving academic performance. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Findings of literature review based on the algorithm method or type of ANN, the model accuracy, and findings.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **Predictors** & **Outcome** \\ \hline
[36] & 116 features for the production phase (product data) and 84 for the & Team performance \\  & learning phase & \\ \hline
[12] & 23 factors including academic demographic, social, and behavioral & Student at risk \\  & factors with prior semester performance. & \\ \hline
[37] & 11 variables include socio-economic background, university entrance & CGPA \\  & examination results, and CGPA. & \\ \hline
[38] & 123 variables, including prior academic achievement, tuition fees, & \\  & students’ socioeconomic status, students’ home characteristics, & \\  & students’ household status, students’ background information, high & \\  & school characteristics, working status, university background, and & \\  & academic performance in higher education. & \\ \hline
[39] &
\begin{tabular}{c} Application score, vulnerability index, gender, population segment, \\ application priority, application instance, school type, regime, province, \\ ethnicity, disability \\ \end{tabular} & Level pass \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._