Please use this identifier to cite or link to this item:
https://rda.sliit.lk/handle/123456789/1635
Title: | MoocRec: Learning styles-oriented MOOC recommender and search engine |
Authors: | Aryal, S Porawagama, A. S Hasith, M. G. S Thoradeniya, S. C Kodagoda, N Suriyawansa, K |
Keywords: | MoocRec Learning Styles Oriented MOOC Recommender Search Engine |
Issue Date: | 8-Apr-2019 |
Publisher: | IEEE |
Citation: | S. Aryal, A. S. Porawagama, M. G. S. Hasith, S. C. Thoradeniya, N. Kodagoda and K. Suriyawansa, "MoocRec: Learning Styles-Oriented MOOC Recommender and Search Engine," 2019 IEEE Global Engineering Education Conference (EDUCON), 2019, pp. 1167-1172, doi: 10.1109/EDUCON.2019.8725079. |
Series/Report no.: | 2019 IEEE Global Engineering Education Conference (EDUCON);Pages 1167-1172 |
Abstract: | Massive Open Online Courses (MOOCs) are the new revolution in the field of e-learning, providing a large number of courses in different domains to a wide range of learners. Due to the availability of several MOOC providers (including edX, Coursera, Udacity, FutureLearn), a specific domain has multiple courses spread across these platforms that confuses a learner on selecting the most suitable course for him. It is a tedious manual task for the learner to browse through various courses before he finds the best course that meets his learning requirements and objectives. MoocRec is a unique learning styles-oriented system that recommends the most suitable courses to a learner from different MOOC platforms based on their learning styles and individual needs. The courses are recommended based on the mapping of Felder and Silverman learning styles with the standard video styles used in MOOC videos (including talking head, slide, tutorial/demonstration). MoocRec also allows the learners to search for courses using specific topics to provide an enhanced personalized learning environment. Results show that MoocRec is strongly reliable and can be used for personalized learning. |
URI: | http://rda.sliit.lk/handle/123456789/1635 |
ISSN: | 2165-9567 |
Appears in Collections: | Department of Computer Science and Software Engineering -Scopes Research Papers - IEEE Research Papers - SLIIT Staff Publications |
Files in This Item:
File | Description | Size | Format | |
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MoocRec_Learning_Styles-Oriented_MOOC_Recommender_and_Search_Engine.pdf Until 2050-12-31 | 412.7 kB | Adobe PDF | View/Open Request a copy |
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