Automatic assessment of comment quality in active video watching (2020)
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Type of Content
Conference Contributions - PublishedPublisher
Asia-Pacific Society for Computers in EducationCollections
Authors
Editors
So H-JRodrigo MMMason JMitrovic AAlternative Title
Automatic quality assessment of comments in active video watching using machine learning techniquesAbstract
Active Video Watching (AVW-Space) is an online platform for video-based learning which supports engagement via note-taking and personalized nudges. In this paper, we focus on the quality of the comments students write. We propose two schemes for assessing the quality of comments. Then, we evaluate these schemes by computing the inter-coder agreement. We also evaluate various machine learning classifiers to automate the assessment of comments. The selected cost-sensitive classifier shows that the quality of comments can be assessed with high weighted-F1 scores. This study contributes to the automation of comment quality assessment and the development of personalized educational support for engagement in video-based learning through commenting.
Citation
Mohammadhassan N, Mitrovic A, Neshatian K, Dunn J (2020). Automatic quality assessment of comments in active video watching using machine learning techniques. Virtual conference: the 28th International Conference on Computers in Education. 23/11/2020-27/11/2020. I. 1-10.This citation is automatically generated and may be unreliable. Use as a guide only.
Keywords
Video-based Learning; Learning Analytics; Applied Machine Learning; Text ClassificationANZSRC Fields of Research
39 - Education::3904 - Specialist studies in education::390405 - Educational technology and computing39 - Education::3904 - Specialist studies in education::390408 - Learning analytics
46 - Information and computing sciences::4611 - Machine learning::461199 - Machine learning not elsewhere classified