A state-of-the-art survey of predicting students' performance using artificial neural networks

Predicting students' performance is one of the most important issue in educational data mining. In order to investigate the state-of-the-art research development in predicting students' performance by using artificial neural networks (ANN), we conducted a survey on 39 important studies on this issue from 2016 to 2021. The results show that: (1) objectives of most prediction model is the performance of learners on the program and course; (2) datasets used for training prediction model are collected from logs of the learning management system; (3) the most commonly used ANN is feedforward neural network; (4) researchers use stochastic gradient descent and Adam algorithm to optimizes the parameters in ANN and configure hyper parameters of ANN manually; (5) feature selection is not necessary because ANN can automatically adjust the weights of artificial neurons; and (6) ANN has better performance than the classical classifiers in predicting student performance. The challenges in previous studies were comprehensively analyzed, suggestions for future research were put forward.