Browsing by Author "Hasith, M. G. S"

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    MoocRec: Learning styles-oriented MOOC recommender and search engine
    (IEEE, 2019-04-08) Aryal, S; Porawagama, A. S; Hasith, M. G. S; Thoradeniya, S. C; Kodagoda, N; Suriyawansa, K
    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.
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    Using Pre-trained Models As Feature Extractor To Classify Video Styles Used In MOOC Videos
    (IEEE, 2018-12-21) Aryal, S; Porawagama, A. S; Hasith, M. G. S; Thoradeniya, S. C; Kodagoda, N; Suriyawansa, K
    Massive Open Online Courses (MOOCs) have emerged as new learning phenomenon in the field of e-learning. Over recent years, it has attracted a significant number of learners as well as researchers. A wide range of researches is being carried out across multiple aspects of MOOCs. Video lectures are the most fundamental component in a MOOC. There are standard video styles that are normally used across several MOOC platforms, such as, talking head, demonstration, slides, animation etc. This paper presents an Image-Based classification approach of the video styles where a single video is split into multiple image frames, and then each frame is classified into one of the video style-category. Different classifier models built on top of each state-of-the-art deep neural architectures, including VGG16, InceptionV3, and ResNet50 are evaluated and the comparison of results is shown. Furthermore, the paper also discusses a numeric method to calculate the composition level of a single video style in multi-style filed videos based on the classification results.