Abstract
In less than ten years, a well-designed video-based online learning material can be a powerful learning tool that evokes students' positive emotions while using it. This rewards a valuable learning experience for all students. To enable the development of such learning materials, the knowledge of how design elements influence emotion should be continued so that we could engineer the emotion into video-based online learning materials. That is the main objectives of this research. The research was conducted using Kansei Engineering approach which has been adapted from the pioneer of KE, Prof. Dr. Mitsuo Nagamachi. Using 10 specimens of video obtained from YouTube, 55 Kansei Emotion Words (KW), and 32 evaluation subjects, this research performed evaluation experiment to assess students' emotional response to video-based online learning materials. Multivariate analysis were performed to the averaged evaluation result acquired from subjects to identify the semantic space for video-based online learning material for higher education and investigate the associated design elements to be used as a guide in designing video-based online learning material, which embeds target emotion in its design. The findings in this research reveal five pillars of Kansei semantic space of emotions for video-based online learning materials. Based on Factor Analysis, it reveals three main pillars; professional-motivated, fun, joking-humorous and two additional pillars; deceptive and puzzled. Other than that, this research also described design elements of video-based online learning materials that evoke specific emotions based on five pillars that identified after performed Partial Least Square Analysis. Although there are some limitations and constraints during the research are conducted, they have contributed little portion of knowledge to confirm some important emotions in an online learning environment particularly in video-based learning.
Metadata
Item Type: | Thesis (Masters) |
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Creators: | Creators Email / ID Num. Adnan, Hazlina 2013290426 |
Contributors: | Contribution Name Email / ID Num. Thesis advisor Redzuan, Fauziah UNSPECIFIED |
Subjects: | T Technology > TJ Mechanical engineering and machinery |
Divisions: | Universiti Teknologi MARA, Shah Alam > Faculty of Computer and Mathematical Sciences |
Programme: | Master of Science in Information Technology |
Keywords: | Kansei Engineering, Online learning, Factor Analysis |
Date: | 2016 |
URI: | https://ir.uitm.edu.my/id/eprint/63270 |
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