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Treffer: Automatic Classification of Online Learner Reviews via Fine-Tuned BERTs

Title:
Automatic Classification of Online Learner Reviews via Fine-Tuned BERTs
Language:
English
Authors:
Xieling Chen, Di Zou, Haoran Xie, Gary Cheng, Zongxi Li, Fu Lee Wang
Source:
International Review of Research in Open and Distributed Learning. 2025 26(1):57-79.
Availability:
Athabasca University Press. 1200, 10011-109 Street, Edmonton, AB T5J 3S8, Canada. Tel: 780-497-3412; Fax: 780-421-3298; e-mail: irrodl@athabascau.ca; Web site: http://www.irrodl.org
Peer Reviewed:
Y
Page Count:
23
Publication Date:
2025
Document Type:
Fachzeitschrift Journal Articles<br />Reports - Research
Descriptors:
MOOCs, Distance Education, Online Courses, Course Evaluation, Electronic Learning, Student Satisfaction, Classification, Content Analysis, Automation, Artificial Intelligence, Technology Uses in Education, Educational Technology
ISSN:
1492-3831
Entry Date:
2025
Accession Number:
EJ1463472
Database:
ERIC

Weitere Informationen

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.

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