Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1476
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dc.contributor.authorSankalpa, R.-
dc.contributor.authorSankalpani, T.-
dc.contributor.authorSandeepani, T.-
dc.contributor.authorRansika, N.-
dc.contributor.authorKodagoda, N.-
dc.contributor.authorSuriyawansa, K.-
dc.date.accessioned2022-03-04T03:38:27Z-
dc.date.available2022-03-04T03:38:27Z-
dc.date.issued2020-12-10-
dc.identifier.isbn978-1-7281-8412-8-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/1476-
dc.description.abstractMOOCs (Massive Open Online Courses) are a new revolution in the field of e-learning. MOOCs are capable of providing several thousands of learners with access to courses over the internet. MOOCs are produced in many different video production styles and these styles play an important role in helping the consumer stay engaged and interested in the courses. MOOCs provide a large number of courses in different domains to a wide range of learners. It has become difficult and a timeconsuming task for a user to find the most suitable courses that suit a learner’s personal preferences. This paper describes how to recommend a course based on the preferred video style of the learner and the basic learning style of the learner which determines the learner’s preferences on other materials in a course. In the course recommendation process, this paper also describes how to classify the course in order to recommend the most appropriate massive open online courses for users according to their most preferred video production style.en_US
dc.language.isoenen_US
dc.publisher2020 2nd International Conference on Advancements in Computing (ICAC), SLIITen_US
dc.relation.ispartofseriesVol.1;-
dc.subjectE-learningen_US
dc.subjectOnline learningen_US
dc.subjectVideo productionen_US
dc.subjectMachine Learningen_US
dc.subjectVideo styleen_US
dc.subjectLearning style modelsen_US
dc.titleMOOCs Recommender Based on User Preference and Video Qualityen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ICAC51239.2020.9357278en_US
Appears in Collections:2nd International Conference on Advancements in Computing (ICAC) | 2020

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