Mining Individual Learning Topics in Course Reviews Based on Author Topic Model
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| Title: | Mining Individual Learning Topics in Course Reviews Based on Author Topic Model |
|---|---|
| Language: | English |
| Authors: | Liu, Sanya, Ni, Cheng, Liu, Zhi, Peng, Xian, Cheng, Hercy N. H. |
| Source: | International Journal of Distance Education Technologies. Jul-Sep 2017 15(3):1-14. |
| Availability: | IGI Global. 701 East Chocolate Avenue, Hershey, PA 17033. Tel: 866-342-6657; Tel: 717-533-8845; Fax: 717-533-8661; Fax: 717-533-7115; e-mail: journals@igi-global.com; Web site: http://www.igi-global.com/journals |
| Peer Reviewed: | Y |
| Page Count: | 14 |
| Publication Date: | 2017 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Online Courses, Student Records, Learning Strategies, Cognitive Style, Data Analysis, Data Collection, Student Interests, Units of Study, Models, Electronic Learning, Information Utilization |
| DOI: | 10.4018/IJDET.2017070101 |
| ISSN: | 1539-3100 |
| Abstract: | Nowadays, Massive Open Online Courses (MOOCs) have obtained a rapid development and drawn much attention from the areas of learning analytics and artificial intelligence. There are lots of unstructured data being generated in online reviews area. The learning behavioral data become more and more diverse, and they prompt the emergence of big data in education. To mine useful information from these data, we need to use educational data mining and learning analysis technique to study the learning feelings and discussed topics among learners. This paper aims to mine and analyze topic information hidden in the unstructured reviews data in MOOCs; a novel author topic model based on an unsupervised learning idea is proposed to extract learning topics for each learner. According to the experimental results, we will analyze and focus on interests of learners, which facilitates further personalized course recommendation and improves the quality of online courses. |
| Abstractor: | As Provided |
| Number of References: | 25 |
| Entry Date: | 2017 |
| Accession Number: | EJ1145219 |
| Database: | ERIC |
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