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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 \u201cassessment\u201d, \u201ccontent\u201d, \u201ceffort\u201d, \u201cusefulness\u201d, \u201cenjoyment\u201d, \u201cfaculty\u201d, \u201cinteraction\u201d, and \u201cstructure\u201d. The article provides valuable insights into the online course evaluation and selection processes for learners in D1 and D2 groups. Notably, both groups prioritize \u201ceffort\u201d and \u201cfaculty\u201d, while D2 learners value \u201cassessment\u201d and \u201cenjoyment\u201d, and D1 learners value \u201cusefulness\u201d more. This study demonstrates the efficacy of leveraging online learner reviews and topic modeling for automating MOOC evaluation and informing learners\u2019 decision-making processes.<\/jats:p>","DOI":"10.1007\/s13042-024-02203-6","type":"journal-article","created":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T11:01:59Z","timestamp":1716289319000},"page":"4973-4998","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Leveraging text mining and analytic hierarchy process for the automatic evaluation of online courses"],"prefix":"10.1007","volume":"15","author":[{"given":"Xieling","family":"Chen","sequence":"first","affiliation":[]},{"given":"Haoran","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Xiaohui","family":"Tao","sequence":"additional","affiliation":[]},{"given":"Fu Lee","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Cao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,21]]},"reference":[{"key":"2203_CR1","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1080\/10494820.2020.1802298","volume":"29","author":"Y Nie","year":"2021","unstructured":"Nie Y, Luo H, Sun D (2021) Design and validation of a diagnostic MOOC evaluation method combining AHP and text mining algorithms. 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