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Automatic Classification of Online Learner Reviews Via Fine-Tuned BERTs

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Publicado en:International Review of Research in Open and Distributed Learning vol. 26, no. 1 (Mar 2025), p. 57
Autor principal: Chen, Xieling
Otros Autores: Zou, Di, Xie, Haoran, Cheng, Gary, Li, Zongxi, Fu Lee Wang
Publicado:
International Review of Research in Open and Distance Learning
Materias:
Language
Automatic classification
Datasets
Deep learning
Ontology
Data analysis
Automation
Feedback
Machine learning
Neural networks
Decision making
Online instruction
Design
Algorithms
Designers
Education
Semantics
Learning Activities
Literature Reviews
Distance Education
Sample Size
Learning Experience
Personal Autonomy
Instructional Materials
Short Term Memory
Coding
Online Courses
Artificial Intelligence
Writing Instruction
MOOCs
Student Writing Models
Language Processing
Course Content
Learner Engagement
Educational Facilities Improvement
Attention
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Resumen:Massive open online courses (MOOCs) offer rich opportunities to comprehend learners’ learning experiences by examining their self-generated course evaluation content. This study investigated the effectiveness of fine-tuned BERT models for the automated classification of topics in online course reviews and explored the variations of these topics across different disciplines and course rating groups. Based on 364,660 course review sentences across 13 disciplines from Class Central, 10 topic categories were identified automatically by a BERT-BiLSTM-Attention model, highlighting the potential of fine-tuned BERTs in analysing large-scale MOOC reviews. Topic distribution analyses across disciplines showed that learners in technical fields were engaged with assessment-related issues. Significant differences in topic frequencies between high- and low-star rating courses indicated the critical role of course quality and instructor support in shaping learner satisfaction. This study also provided implications for improving learner satisfaction through interventions in course design and implementation to monitor learners’ evolving needs effectively.
ISSN:1492-3831
DOI:10.19173/irrodl.v26i1.8068
Fuente:Education Database