# Toward Predicting Student's Academic Performance Using Artificial Neural Networks (ANNs)
## 5 Discussion
### Research Domains
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Algorithm/ANN Type** & **Model Accuracy** & **Findings** \\ \hline \multirow{3}{*}{[30]} & \multirow{3}{*}{ANN} & \multirow{3}{*}{80.47\%} & The model was able to predict the student performance accurately, and the study recommends the use of additional inputs such as the number of clicks and other browsing data of the online learning system \\ \hline \multirow{3}{*}{[31]} & Multiple Linear Regression, ANN & \multirow{3}{*}{86.32\%} & Reinforce the fact that a comparative analysis of different training algorithms is always supportive in enhancing the performance of a neural network \\  & & & \\  & & & \\  & & & \\  & & & \\ \hline \multirow{3}{*}{[32]} & MLP ANN with Factor selection & \multirow{3}{*}{87\%} & ANN Model provides higher accuracy compared to Decision Tree, Clustering Via PCA, Bayesian \\ \hline \multirow{3}{*}{[33]} & ANN–GSA, ANN–CS. Feedforward & \multirow{3}{*}{90.64\%} & NN trained by the Cuckoo search could be used in the prediction of students’ academic performance \\ \cline{2-2}  & neural network & & \\ \hline \multirow{3}{*}{[34]} & MLP & \multirow{3}{*}{83\%} & ANN capable of performance predicting successfully \\ \cline{2-2}  & & & \\ \hline \multirow{3}{*}{[35]} & Elman neural network, BPNN & \multirow{3}{*}{84.00\%} & Elman neural network and training the network reasonably, the predictive model is more effective compared to BPNN and the linear regression method \\ \cline{2-2}  & & & \\ \hline \multirow{3}{*}{[36]} & ANN perceptron, Shapley Additive & \multirow{3}{*}{80.76\% and 86.57\%} & Meeting hours, coding deliverable hours significantly predicted team’s performance during the production and learning phases \\  & Explanations (SHAP) & & \\  & & & \\ \hline \multirow{4}{*}{[12]} & ML models, Decision Tree, Iterative & \multirow{4}{*}{86.67\% (Ensemble)} & Ensemble model provided the most accurate prediction \\  & Dichotomiser, Chi-squared & \multirow{3}{*}{86.67\% (Ensemble)} & (0.398 RMS error). \\  & Automatic Interaction Detector, Naive & & \\  & Bayes Classifier, Rule Induction, & \multirow{3}{*}{Academic data, income, and family qualification} \\  & Random Forest, Ensemble NA & & significantly impacted student performance \\ \hline \multirow{3}{*}{[37]} & Levenberg-Marquardt algorithm, B-P & \multirow{3}{*}{84.80\%} & Model has better performance than existing ones and showed effective prediction accuracy. English exam \\  & based supervised learning, & & results strongly correlated with CGPA. \\ \hline \multirow{3}{*}{[38]} & MLP & \multirow{3}{*}{82.10\% (high)} & Prior academic achievement, university background, \\  & & & high school characteristics and students’ SES \\ \cline{1-1}  & & & significantly predicted performance \\ \hline \multirow{3}{*}{[39]} & Multilayer perceptron & \multirow{3}{*}{74.5\%} & Model failed to reach the maximum \\  & & & classification performance. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._school students in conjunction with university student results were used [22; 26]; the same methodology was applied for master's students [27; 28]. Although some studies investigated student performance in the medical discipline [19], most of the studies focused on engineering students including those in the architectural, mechanical, electrical, civil, information technologies, and software disciplines [34]. Other studies used multiple disciplines, including business and marketing, social communication, law, medicine, engineering, and psychology [27]. Some studies focused on E-learning or Virtual Learning Environments (VLEs) [20; 29; 30]. Various sample sizes were used based on the data availability.