# Toward Predicting Student's Academic Performance Using Artificial Neural Networks (ANNs)
A more common approach to solving this type of problem is to use an Artificial Neural network, which simulates the human brain in solving a problem. As a result, an adaptive system, such as an Artificial Neural Network, is being developed to predict a student's performance based on the effects of these factors. Artificial Neural Networks (ANN) are valuable tools for data analysis used to identify and represent functional correlations between variables [4]. ANNs are currently being used to address, forecast, and classify challenges in diverse fields. For example, in the academic domain, ANN is applied for analyzing academic performance [5]. Indeed, a variety of methodological approaches have been used to predict students' academic performance. For example, traditional statistical methods, such as discriminant analysis and multiple linear regressions, are used as the first and most common approach found in the educational literature in Malaysian study performed by [6]. Various studies [7; 8] have used structural equation modelling (SEM) to compare theoretical models to data sets or to test different models of academic performance [7; 8]. In comparison to artificial intelligence computing methods, traditional approaches failed to consistently show the capacity to reach accurate predictions or classifications [9]. As a result, a third approach to predicting student achievement and academic performance indicated in recent literature uses machine learning techniques, such as Artificial Neural Network methods (ANN) and some other techniques including Decision Tree [10], Support Vector Machines [2], Bayesian algorithms [11], and Ensemble Learning [12]. This method has been successfully applied in various fields, including business, engineering, meteorology, and economics, without significant differences in the obtained results or the level of accuracy. ANN is one of the machine learning and data mining algorithms that was used in various research literature and was claimed to provide superior and accurate results regarding predicting student performance. ANN is widely regarded as an effective pattern-recognizer and an important method for classifying potential outcomes. Given the extant literature, there is always room for improvement in machine learning approaches such as ANN (the main focus of this paper) and the prediction model. The review of literature is an important step which enables the researcher to identify the knowledge gap, understand the recent technological trends and examine various methods that yield various degrees of accuracy to identify the factors or input variables and methodologies that yield the highest accuracy of prediction of the student academic achievement and performance. Through literature assessment, the researcher's mind is open to contributing to ANN's knowledge in predicting student achievements and performances. Hence, this paper aims to examine the current literature in a systematic review approach. Also, this review attempts to identify a pattern of the most used ANN techniques used and the nature of the data commonly used to predict student academic performance, in terms of the level of education, sample sizes and the attributes or the inputs used in various models. Additionally, this review tries to establish any link to the factors that contribute to the model's ability to predict student performance accurately. The remainder of this paper is organized as follows: Section 2 presents the related studies and significance of this review. Section 3 introduces the research methodology. Section 4 presents the results, whereas Section 5 discusses the findings and Section 6 discusses the future work. Finally, the conclusion is presented in Section 7.