Students' performance-prediction-Model based on Physical and Physiological Constraints

Abstract

The consequences of the Covid-19 pandemic changed the education system and the lifestyle of all students in Jordan. To reduce the infection rate among students, the education institutes in Jordan decided to adopt online learning as an alternative to face-to-face education. The fast shift to online education raises a potent concern regarding its efficiency. For instance, many students in Jordan cannot afford digital tools and do not have an internet connection. Furthermore, the psychological impact of enforcing online learning is not fully recognized. This study presents two regression models based on Multilayer Perceptron (MLP) neural network and Random Forest (RF) regressor to analyze and predict students' performance in Jordan before and during the lockdown and under physical and psychological constraints. In this study, the Dataset of Jordanian University Students' Psychological Health Impacted by Using E-learning Tools during COVID-19 (JUSPH) is divided into four subsets based on their chronological timeline (Before/After Covid-19), physical and psychological states. Besides, the four subsets are pre-processed using a Simple Imputer (SI), label encoder, and on-hot encoding to impute the missing value and handle the categorical data, respectively. Then, the features are selected by using the Low Variance (LV) filter. Afterward, MLP and RF regressor is used to predict the future students' performance under online education in the following semester. Results showed that the proposed MLP models achieved the best accuracy score of 99.94% on the Before Covid-19 physical Subset, while the RF model achieved the best accuracy score of 85.58% on the After Covid-19 Psychological subset.