Design and Validation of a Diagnostic MOOC Evaluation Method Combining AHP and Text Mining Algorithms
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| Title: | Design and Validation of a Diagnostic MOOC Evaluation Method Combining AHP and Text Mining Algorithms |
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| Language: | English |
| Authors: | Nie, Yanjiao (ORCID |
| Source: | Interactive Learning Environments. 2021 29(2):315-328. |
| Availability: | Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Peer Reviewed: | Y |
| Page Count: | 14 |
| Publication Date: | 2021 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Online Courses, Evaluation Methods, Course Evaluation, Mathematics, Data Analysis, Accuracy, Validity, Educational Quality, Standards |
| DOI: | 10.1080/10494820.2020.1802298 |
| ISSN: | 1049-4820 |
| Abstract: | The proliferation of massive open online courses (MOOCs) highlights the necessity of developing accurate and diagnostic evaluation methods to assess the courses' quality and effectiveness. Hence, this study proposes a diagnostic MOOC evaluation (DME) method that combines the Analytic Hierarchy Process algorithm and learner review mining to integrate expert opinions, standardized rubrics, and learner feedback into the evaluation process. For this purpose, 30 MOOCs from the Coursera website were purposively selected and evaluated using the DME method and the results compared with expert evaluation and learner rating scores. The preliminary findings, in general, support the feasibility, accuracy, and diagnostic utility of the DME method and its suitability as a low-cost, sophisticated, and accurate method for MOOC evaluation. Finally, the study discusses several limitations and technical issues of the DME method that should be addressed in future research and practice. |
| Abstractor: | As Provided |
| Entry Date: | 2021 |
| Accession Number: | EJ1292442 |
| Database: | ERIC |
| Abstract: | The proliferation of massive open online courses (MOOCs) highlights the necessity of developing accurate and diagnostic evaluation methods to assess the courses' quality and effectiveness. Hence, this study proposes a diagnostic MOOC evaluation (DME) method that combines the Analytic Hierarchy Process algorithm and learner review mining to integrate expert opinions, standardized rubrics, and learner feedback into the evaluation process. For this purpose, 30 MOOCs from the Coursera website were purposively selected and evaluated using the DME method and the results compared with expert evaluation and learner rating scores. The preliminary findings, in general, support the feasibility, accuracy, and diagnostic utility of the DME method and its suitability as a low-cost, sophisticated, and accurate method for MOOC evaluation. Finally, the study discusses several limitations and technical issues of the DME method that should be addressed in future research and practice. |
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| ISSN: | 1049-4820 |
| DOI: | 10.1080/10494820.2020.1802298 |
