Deep learning with data transformation and factor analysis for student performance prediction

Abstract

Student performance prediction is one of the most concerning issues in the field of education and training, especially educational data mining. The prediction supports students to select courses and design appropriate study plans for themselves. Moreover, student performance prediction enables lecturers as well as educational managers to indicate what students should be monitored and supported to complete their programs with the best results. These supports can reduce formal warnings and expulsions from universities due to students' poor performance. This study proposes a method to predict student performance using various deep learning techniques. Also, we analyze and present several techniques for data pre-processing (e.g., Quantile Transforms and MinMax Scaler) before fetching them into well-known deep learning models such as Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN) to do prediction tasks. Experiments are built on 16 datasets related to numerous different majors with appropriately four million samples collected from the student information system of a Vietnamese multidisciplinary university. Results show that the proposed method provides good prediction results, especially when using data transformation. The results are feasible for applying to practical cases.