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VideoMark: A video-based learning analytic technique for MOOCs
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VideoMark: A video-based learning analytic technique for MOOCs

Nen-Fu Huang, Hao-Hsuan Hsu, So-Chen Chen, Chia-An Lee, Yi-Wei Huang, Po-Wen Ou and Jian-Wei Tzeng
2017 IEEE 2nd International Conference on Big Data Analysis, ICBDA 2017, pp.753-757
10/2017

Abstract

Learning analytics learning behaviors learning processes massive online open courses (MOOCs) video-based learning Signal Processing Computer Networks and Communications Information Systems Information Systems and Management
The massive online open course (MOOC) revolution offers opportunities for learning analytics regarding the diversity in learning activities of MOOCs. In MOOCs, video lectures represent the principal learning resource, and learners spend most of their time engaged in self-paced, independent learning. Therefore, MOOCs present an innovative form of video-based learning. How to improve this learning environment by adding additional features to video lectures represents a path for progress. Few studies have investigated the interaction between learners and the video content; however, improvement in this domain is necessary. This paper proposes a video-based learning analysis approach called VideoMark, which enables learners to judiciously review, consolidate, and clarify specific concepts introduced in MOOCs. This analytical approach can be applied to MOOC-style ShareCourse programs in Taiwan. Finally, this analytical approach is presented as a visual keyword cloud for learners to clarify specific learning concepts in the MOOC learning process.

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