Machine Learning-Based Students’ Sentiment towards E-Learning amid COVID-19 Pandemic

Authors

  • Fida Muhammad Khan Department of Computer Science, University of Science & Technology, Bannu, Pakistan
  • Zahid Iqbal Department of Computer Science, University of Science & Technology, Bannu, Pakistan
  • Muhammad Shoaib Akhtar Department of Electrical Engineering, University of Science & Technology, Bannu, Pakistan
  • Inam Ullah Khan Department of Computer Science, University of Lakki Marwat, Pakistan

DOI:

https://doi.org/10.62019/abbdm.v4i1.132

Abstract

The global COVID-19 pandemic has created exceptional difficulties for college and university students. With educational institutions shutting down and an abrupt shift to online learning, students are facing new challenges. Despite the advantages of e-learning, developing nations like Pakistan are experiencing major hurdles due to a lack of internet access and financial constraints. As a result, students' academic performance is being negatively impacted. Researchers are using sentiment analysis of social media platforms such as Facebook, Twitter, and YouTube to gain insight into student experiences during this challenging time. This study seeks to create a machine learning-based sentiment classification system that can determine the sentiment of higher education students in Pakistan towards e-learning classes during the COVID-19 pandemic. The primary objective of this research is to identify and categorize the challenges and problems that university students face in their e-learning classes. The research employed Multi-Nominal NB and Gaussian NB classifiers to examine the sentiment of online reviews and comments of students. We achieve 99% accuracy using Gaussian NB and 98% accuracy using Multinomial NB classifiers. According to the study's results, the COVID-19 epidemic has had a significant effect on the education system, with the vast majority of feedback being negative (70%). Only a small percentage of feedback was classified as neutral (29.9%) or positive (4%). This finding underscores the urgent need for better solutions for online learning during and after the pandemic. The study underscores the importance of addressing these unique challenges faced by students in developing countries during the pandemic. There is a pressing need for innovative solutions that can ensure that higher education remains effective and accessible in these challenging times. By utilizing sentiment analysis and machine learning, researchers can gain a better understanding of the experiences of students during this crisis. This understanding can inform targeted interventions and solutions that can improve academic performance and well-being for students in developing countries like Pakistan. The findings of this research provide important insights and recommendations for policymakers, educators, and stakeholders in the higher education sector.

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Published

2024-03-27

How to Cite

Khan, F. M., Iqbal, Z., Akhtar, M. S., & Khan, I. U. (2024). Machine Learning-Based Students’ Sentiment towards E-Learning amid COVID-19 Pandemic. The Asian Bulletin of Big Data Management, 4(1). https://doi.org/10.62019/abbdm.v4i1.132