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 |
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| Otros Autores: | , , , , |
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International Review of Research in Open and Distance Learning
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF Full text outside of ProQuest |
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MARC
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| 022 | |a 1492-3831 | ||
| 024 | 7 | |a 10.19173/irrodl.v26i1.8068 |2 doi | |
| 035 | |a 3177605310 | ||
| 045 | 2 | |b d20250301 |b d20250331 | |
| 084 | |a 68600 |2 nlm | ||
| 100 | 1 | |a Chen, Xieling | |
| 245 | 1 | |a Automatic Classification of Online Learner Reviews Via Fine-Tuned BERTs | |
| 260 | |b International Review of Research in Open and Distance Learning |c Mar 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Language | ||
| 653 | |a Automatic classification | ||
| 653 | |a Datasets | ||
| 653 | |a Deep learning | ||
| 653 | |a Ontology | ||
| 653 | |a Data analysis | ||
| 653 | |a Automation | ||
| 653 | |a Feedback | ||
| 653 | |a Machine learning | ||
| 653 | |a Neural networks | ||
| 653 | |a Decision making | ||
| 653 | |a Online instruction | ||
| 653 | |a Design | ||
| 653 | |a Algorithms | ||
| 653 | |a Designers | ||
| 653 | |a Education | ||
| 653 | |a Semantics | ||
| 653 | |a Learning Activities | ||
| 653 | |a Literature Reviews | ||
| 653 | |a Distance Education | ||
| 653 | |a Sample Size | ||
| 653 | |a Learning Experience | ||
| 653 | |a Personal Autonomy | ||
| 653 | |a Instructional Materials | ||
| 653 | |a Short Term Memory | ||
| 653 | |a Coding | ||
| 653 | |a Online Courses | ||
| 653 | |a Artificial Intelligence | ||
| 653 | |a Writing Instruction | ||
| 653 | |a MOOCs | ||
| 653 | |a Student Writing Models | ||
| 653 | |a Language Processing | ||
| 653 | |a Course Content | ||
| 653 | |a Learner Engagement | ||
| 653 | |a Educational Facilities Improvement | ||
| 653 | |a Attention | ||
| 700 | 1 | |a Zou, Di | |
| 700 | 1 | |a Xie, Haoran | |
| 700 | 1 | |a Cheng, Gary | |
| 700 | 1 | |a Li, Zongxi | |
| 700 | 1 | |a Fu Lee Wang | |
| 773 | 0 | |t International Review of Research in Open and Distributed Learning |g vol. 26, no. 1 (Mar 2025), p. 57 | |
| 786 | 0 | |d ProQuest |t Education Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3177605310/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3177605310/fulltext/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3177605310/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://eric.ed.gov/?id=EJ1463472 |