Evaluation of Student Feedback Within a MOOC Using Sentiment Analysis and Target Groups
Many course designers trying to evaluate the experience of participants in a MOOC will find it difficult to track and analyse the online actions and interactions of students because there may be thousands of learners enrolled in courses that sometimes last only a few weeks. This study explores the u...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Athabasca University Press
2020
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/1222558edb994d539321c48f8f348a0a |
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| Sumario: | Many course designers trying to evaluate the experience of participants in a MOOC will find it difficult to track and analyse the online actions and interactions of students because there may be thousands of learners enrolled in courses that sometimes last only a few weeks. This study explores the use of automated sentiment analysis in assessing student experience in a beginner computer programming MOOC. A dataset of more than 25,000 online posts made by participants during the course was analysed and compared to student feedback. The results were further analysed by grouping participants according to their prior knowledge of the subject: beginner, experienced, and unknown. In this study, the average sentiment expressed through online posts reflected the feedback statements. Beginners, the target group for the MOOC, were more positive about the course than experienced participants, largely due to the extra assistance they received. Many experienced participants had expected to learn about topics that were beyond the scope of the MOOC. The results suggest that MOOC designers should consider using sentiment analysis to evaluate student feedback and inform MOOC design.
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