Detection of Students’ Problems in Distance Education Using Topic Modeling and Machine Learning

Abstract

Following the rapid spread of COVID-19 to all the world, most countries decided to temporarily close their educational institutions. Consequently, distance education opportunities have been created for education continuity. The abrupt change presented educational challenges and issues. The aim of this study is to investigate the content of Twitter posts to detect the arising topics regarding the challenges of distance education. We focus on students in Saudi Arabia to identify the problems they faced in their distance education experience. We developed a workflow that integrates unsupervised and supervised machine learning techniques in two phases. An unsupervised topic modeling algorithm was applied on a subset of tweets to detect underlying latent themes related to distance education issues. Then, a multi-class supervised machine learning classification technique was carried out in two levels to classify the tweets under discussion to categories and further to sub-categories. We found that 76,737 tweets revealed five underlying themes: educational issues, social issues, technological issues, health issues, and attitude and ethical issues. This study presents an automated methodology that identifies underlying themes in Twitter content with a minimum human involvement. The results of this work suggest that the proposed model could be utilized for collecting and analyzing social media data to provide insights into students’ educational experience.