# Toward Predicting Student's Academic Performance Using Artificial Neural Networks (ANNs)
## 5 Discussion
### Artificial Neural Networks
The review of the literature revealed that although ANNs provide high accuracy of the prediction results, the literature seems to use ANNs to a lesser extent as the application of ANN is limited to sophisticated software such as SPSS and MATLAB [9, 20, 22, 27]. This software requires expert knowledge of statistics, algorithms, and data mining, which is not available for most teachers and educators, limiting the real-life application and use of such software. Additionally, other algorithms and data mining such as Decision Tree algorithms are considered more frequently used due to the availability of a more user-friendly application developed by the University of Waikato, New Zealand. The software is free licensed and uses data mining and predictive analysis to combine visualization tools and graphical user interfaces. These features attract more users to use the software in comparison to ANN. The search inclusion criteria focused on ANNs, although in some studies, the technique was not used alone; [21] used Probabilistic Neural Networks (PNNs) based on the DDA (Dynamic Decay Adjustment). Compared with the Tree Ensemble Predictor, the Random Forest Predictor, the Naive Bayes Predictor, the Decision Tree Predictor, and the Logistic Regression Predictor using the Konstanz Knowledge Miner (KNIME). Ref. [22] used both NNs in comparison with conventional algorithms, which is logistic regression. Similarly, [25] also used ANNs, multiple regression, and SVM algorithms. Ref. [21] used MANFIS-S (Multi Adaptive Neuro-Fuzzy Inference System with Representative Sets)  
Ref. [32] used a technique known as Factor selection, based on principal component analysis, to reduce the number of variables in the survey to smaller numbers of constructs known as dimension reduction. The new constructs were then used as input for the ANN. Other studies used ANN to validate the results obtained by other methods [28, 29]. Multi-layer perceptrons (MLPs) are the most popular type of ANN algorithm employed by the majority of the studies [9, 20, 21, 24, 27, 29, 32, 34]. Few studies used different types of MLP, including feedforward [23, 28] other studies employed the Backpropagation NN [23, 27]. Other studies employed different techniques such as Elman neural network, BPNN [29], Probabilistic Neural Network (PNN) in [16], Particle Swarm Optimization in [23], Cuckoo Search (CS), and Cuckoo Optimization Algorithm (COA) in [31, 33]. There is no apparent association between the type of ANN used and the level of accuracy, nor the sample size used. The only link that is proposed or seen to enhance the accuracy of the prediction model is the training of the network, as presented in [24]. The study contributes the high accuracy of the prediction to the combination of use of the ANN together with other methods such as cluster analyses and Kohonen networks and not to the nature of the type of variables or attributes selected. Furthermore, a similar observation was indicated by [25]. All the reviewed studies indicated superior accuracy of the ANN over the other traditional prediction algorithms and other data mining techniques. Only in one study was it found that PNN showed the least accuracy compared with the Tree Ensemble Predictor, the Random Forest Predictor, the Naive Bayes Predictor, the Decision Tree Predictor, and the Logistic Regression Predictor using Konstanz Knowledge Miner (KNIME) [21].