Prediction of students’ performance in a national medical exam using machine learning techniques

Predicting student academic performance in educational information systems becomes one of the major concerns in improving the quality of academic institutions. Educational data mining can identify the settings that characterize students’ behavior. This study develops prediction models for students’ performance using neural networks, deep learning, and random forest. Deep learning uses multiple hidden layers to represent the data at a higher level, using linear or non-linear transformations. Random Forest that is, a variety of Decision tree algorithms uses the randomization concept to produce many models based on a random selection of input space. We train the models on a national exam dataset. In the following, we compare results with the neural network as a well-known method. The experimental result shows that Deep learning finds a deep data structure by finding better features and achieves accurate results. Although, Random Forest gains a higher accuracy in predicting students’ performance.