Aryal, SPorawagama, A. SHasith, M. G. SThoradeniya, S. CKodagoda, NSuriyawansa, K2022-03-152022-03-152018-12-21S. Aryal, A. S. Porawagama, M. G. S. Hasith, S. C. Thoradeniya, N. Kodagoda and K. Suriyawansa, "Using Pre-trained Models As Feature Extractor To Classify Video Styles Used In MOOC Videos," 2018 IEEE International Conference on Information and Automation for Sustainability (ICIAfS), 2018, pp. 1-5, doi: 10.1109/ICIAFS.2018.8913347.2151-1810https://rda.sliit.lk/handle/123456789/1683Massive 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.enUsing Pre-trained ModelsFeature ExtractorClassify Video StylesMOOC VideosArticle10.1109/ICIAFS.2018.8913347