Analysis of Students’ Performance in an Online Discussion Forum: A Social Network Approach

Khan, Arnob Islam; Kaliteevskii, Vasilii; Shnai, Iuliia; Chechurin, Leonid

doi:10.1007/978-3-030-34983-7_66

Cited by 2 publications

(1 citation statement)

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“…First, recent reviews on the application of sentiment analysis on education affirm that the most used supervised classification techniques are Support vector machine (SVM) and Naive Bayes (NB) and MOOCs discussion forums are the most used resources to analyse learners' sentiment (Mite-Baidal et al 2018). Khan et al (2019), provide a ranking of students based on their participation in the online discussion forums, by applying text analytics along with the lexicon-based approach of sentiment analysis to evaluate the importance of each student's communication. Kastrati et al (2020), proposed a framework for aspect-based sentiment analysis of students' feedback of MOOCs; using a supervised neural network, this framework can help learners to identify the most important aspects of the course based on their feedback.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis on Massive Open Online Courses (MOOCs)

Chanaa

Faddouli

2022

International Journal of Information and Communication Technology Education

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Massive open online courses (MOOCs) have evolved rapidly in recent years due to their open and massive nature. However, MOOCs suffer from a high dropout rate, since learners struggle to stay cognitively and emotionally engaged. Learner feedback is an excellent way to understand learner behaviour and model early decision making. In the presented study, the authors aim to explore learner sentiment expressed in their comments using machine learning and multi-factor analysis methods. They address several research questions on sentiment analysis on educational data. A total of 3311 messages, posted on a MOOC discussion forum, were analysed and categorized using machine learning and data analysis. The results obtained in this study show that it is possible to perform sentiment analysis with very high accuracy (94.1%), and it is also possible to periodically supervise the variations in learners' sentiments. The results of this study are very useful. In the context of online learning, it is very beneficial to have information about learner sentiment.

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Section: Related Workmentioning

confidence: 99%