Leveraging text mining and analytic hierarchy process for the automatic evaluation of online courses
Article
Chen, Xieling, Xie, Haoran, Tao, Xiaohui, Wang, Fu Lee and Cao, Jie. 2024. "Leveraging text mining and analytic hierarchy process for the automatic evaluation of online courses." International Journal of Machine Learning and Cybernetics. https://doi.org/10.1007/s13042-024-02203-6
Article Title | Leveraging text mining and analytic hierarchy process for the automatic evaluation of online courses |
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ERA Journal ID | 125217 |
Article Category | Article |
Authors | Chen, Xieling, Xie, Haoran, Tao, Xiaohui, Wang, Fu Lee and Cao, Jie |
Journal Title | International Journal of Machine Learning and Cybernetics |
Number of Pages | 26 |
Year | 2024 |
Publisher | Springer |
Place of Publication | Germany |
ISSN | 1868-8071 |
1868-808X | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s13042-024-02203-6 |
Web Address (URL) | https://link.springer.com/article/10.1007/s13042-024-02203-6 |
Abstract | This study introduced a multi-criteria decision-making methodology leveraging text mining and analytic hierarchy process (AHP) for online course quality evaluation based on students’ feedback texts. First, a hierarchical structure of online course evaluation criteria was formulated by integrating topics (sub-criteria) identified through topic modeling and interpreted based on transactional distance and technology acceptance theories. Second, the weights of the criteria in the hierarchical structure were determined based on topic proportions. Third, the AHP was employed to determine the overall relative advantage of online courses and their relative advantage within each criterion based on the hierarchical framework and criterion weights. The proposed approach was implemented on the datasets of 6940 reviews for knowledge-seeking courses in Art, Design, and Humanities (D1) and 44,697 reviews for skill-seeking courses in Computer Science, Engineering, and Programming (D2) from Class Central to determine ranking positions of nine courses from both D1 and D2 as alternatives. Results revealed common concerns among knowledge and skill-seeking course learners, encompassing “assessment”, “content”, “effort”, “usefulness”, “enjoyment”, “faculty”, “interaction”, and “structure”. The article provides valuable insights into the online course evaluation and selection processes for learners in D1 and D2 groups. Notably, both groups prioritize “effort” and “faculty”, while D2 learners value “assessment” and “enjoyment”, and D1 learners value “usefulness” more. This study demonstrates the efficacy of leveraging online learner reviews and topic modeling for automating MOOC evaluation and informing learners’ decision-making processes. |
Keywords | Analytic hierarchy process (AHP); Topic mining; Course selection; MOOCs ; Automatic evaluation |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460502. Data mining and knowledge discovery |
Byline Affiliations | Guangzhou University, China |
Lingnan University of Hong Kong, China | |
School of Mathematics, Physics and Computing | |
Hong Kong Metropolitan University, China | |
Hefei University of Technology, China |
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https://research.usq.edu.au/item/z848y/leveraging-text-mining-and-analytic-hierarchy-process-for-the-automatic-evaluation-of-online-courses
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