Students’ Perceptions of E-Learning in Malaysian Universities: Sentiment Analysis Based Machine Learning Approach

Reem Baragash, Hanan Aldowah, Irfan N Umar
Journal of Information Technology Education: Research  •  Volume 21  •  2022  •  pp. 439-463

To gain insight into the opinions and reviews of Malaysian university students regarding e-learning systems, thereby improving the quality and services of these systems and resolving any problems, concerns, and issues that may exist within the institution.

This exploratory study examines the students’ perceptions of e-learning in Malaysia based on Sentiment Analysis (SA) to gain a clear insight into their feelings about the quality of e-learning systems and related services in Malaysian universities to determine whether these opinions are positive or negative.

The data was collected from Twitter; the Full Archive Search API Premium v1.1 tire was chosen to access the tweets from November 1, 2019, to December 30, 2020. The R programming language library package “rtweet” was applied to access the search API and query the tweets. To classify students’ opinions, sentiment analysis-based Machine Learning (ML) with Support Vector Machine (SVM) was utilized. Rapid Miner, a statistical and data mining tool, was used to determine the sentiment of tweets and the accuracy of the ML algorithm. After preparing the data, RapidMiner was used to pre-process and classify the final 1201 tweets based on sentiment, and National Research Council (NRC) word-emotion lexicon was used to detect the presence of eight emotions in the tweets. The confusion matrix is used to determine the classifier’s performance.

This research provided evidence for the effective use of sentiment analysis as an indicator that may contribute to the development of educational systems, specifically, e-learning systems in Malaysian universities.

Based on the findings, the majority of students have a positive opinion about e-learning systems in Malaysian universities. Precisely, the results showed that 65% of sentiments were classified as positive and 35% as negative. Moreover, among the eight emotions, the majority of the tweets expressed a higher level of trust, anticipation, and joy.

The study findings could help classify the teachers’ strengths and weaknesses graphically based on the students’ positive and negative feedback. These findings would also help decision-makers and educationalists be more aware of students’ feelings (sentiments) and concerns. Thus, using social media sentiment analysis should be encouraged as a valuable source of information that may assist their educational decision-making, e-learning development, and performance evaluation.

The findings may encourage other researchers to apply SA based ML approach and use Twitter as a data source to discover users’ opinions about certain issues in learning and teaching processes.

Our study confirmed that social media data could provide valuable and supportive information about educational systems and procedures in e-learning for appropriate decision-making regarding future development and strategies.

Future work can experiment with other classification models and different ML classification algorithms as well as other feature extraction methods and compare the results to find the best accuracy that can improve the classification results

e-learning systems, sentiment analysis, machine learning, university student
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