Deep Learning with Data Transformation and Factor Analysis for Student Performance Prediction

Tran Thanh Dien,Sang Hoai,Nguyen Thai-Nghe,Nguyen Thanh-Hai

doi:10.14569/ijacsa.2020.0110886

Tran Thanh Dien, Sang Hoai + Show 2 more

Open Access

https://doi.org/10.14569/ijacsa.2020.0110886

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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.

Highlights

The number of students who have been warned and forced to leave school tends to increase
This study presents an approach of deep learning techniques [9] using the convolutional neural network on 1D data (CN1D) and the Long Short Term Memory (LSTM) to build a student’s performance prediction model for predicting student performance in semesters based on the course’s achievement results of the previous semesters
We almost achieve the best performance with deep learning (13 datasets in Mean Absolute Error (MAE) and 12 datasets in Root Mean Squared Error (RMSE) out of 16 datasets, respectively)

Summary

Introduction

The number of students who have been warned and forced to leave school tends to increase. One of the reasons can be that students are not able to evaluate and predict correctly his or her ability to select appropriate courses. Student performance is an important task of higher educational institutions because it is a criteria for high quality universities that are based on excellent profile of their academic achievements. There are several definitions on student performance. According to [1], student performance can be obtained by measuring the learning assessment and curriculum. Most of the studies mentioned about graduation being the measure of students’ success [2], [3]

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Results

Conclusion