# Toward Predicting Student's Academic Performance Using Artificial Neural Networks (ANNs)
## 5 Discussion
### Artificial Neural Networks  
A neural network is an equation that describes a mathematical structure made up of several interlinked computational elements called neurons, similar to the name given to the central element of the human nervous system. In some instances, these neurons, referred to as perceptrons, or nodes in the network topology, perform a simple operation on their inputs and send the output to corresponding nodes [23; 30]. Neural networks are polymorphic in structural formation and parallel in algorism computation, and they can be described as a densely interconnected system of processing elements capable of parallel computation. An ANN contains an input layer (which can be thought of as the independent variables or the predictor variables), one or more hidden layers, and a categorical dependent variable-like output layer. All ANNs use multiple processing entities that can be trained. This is a process in which the network learns and adapts to patterns of inputs by developing a unique mathematical relationship based on the perceived given pattern of input data sets using the match of the input variables to the outcomes, for each case. The difference between the traditional analytical methodologies and the ANN is that the typical methods consider a specific type of connection between the input variables and outcome variables and then use many suitable procedures to change the values of the parameters in the model. At the same time, neural networks create a mathematical relationship by \"learning\" the patterns of all inputs from any of the individual cases used in training the network [23; 30]. ANNs can generate a predicted outcome for each case entered throughout the training phase. In addition, one of the significant benefits of machine learning that makes it increasingly popular over the traditional statistical methods is the in case of incorrect prediction. The network has the ability to adjust and recompute the weights of the mathematical relationships among the input variables that represent the predictors and, with the expected outcome, weights that are represented in the network's hidden layers. The outcome-dependent variables are the predicted output, which is usually a continuous variable as in the case of predicting student performance. As such, the outcome is usually success or failure, or pass or fail; this result is linked with a unique value for each input case (or subject). The input variables have information on the likelihood of relating to every one of the categorical classifications of the output variables used in the adopted ANN's development. During the training process, the network improves its accuracy in replicating the test cases' known outcomes. Until one or more predetermined stopping criteria are met, the neural network improves its predictions. A minimum level of accuracy, learning rate, persistence, number of iterations, amount of time, and other criteria can be used as stopping criteria [23]. In the reviewed articles, the authors usually divided the data set into the actual data input, which was usually made of between 60-70% of the obtained data; in some studies, the remaining 40-30% was used for training of the ANN model. In some of the cases, 10% of the data was used to determine the accuracy of the proposed ANN model. One of the major advantages of machine learning, including the ANN methods, is that after the model has been trained, the predictive capability of the trained model is tested with the remaining cases in the dataset, which is reported in some studies to be as low as 10% of the original data set. This process is important to the validation of the model's predictive capacity. The validation process involves observing the weights in the model that are fixed to the values obtained during the training phase. The process is vital before the developed network is used to predict classes of outcomes in a new set of data. It can then be used to predict future outcomes in cases where the outcome is still unknown [20, 24, 29, 32].