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https://ptsldigital.ukm.my/jspui/handle/123456789/513268Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Zulaiha Ali Othman, Assoc. Prof. Dr. | - |
| dc.contributor.author | Fauziah Redzuan (P47880) | - |
| dc.date.accessioned | 2023-10-16T04:35:09Z | - |
| dc.date.available | 2023-10-16T04:35:09Z | - |
| dc.date.issued | 2016-10-15 | - |
| dc.identifier.other | ukmvital:96649 | - |
| dc.identifier.uri | https://ptsldigital.ukm.my/jspui/handle/123456789/513268 | - |
| dc.description | Having quality e-learning is in demand in line with the demand of e-learning in current education system. One of the main concerns in e-learning is on e-learning design. E-learning design involves e-learning; system design, course design and material design. Previous research has found that understanding the learner’s emotion is important in order to design a better quality e-learning material. However, previous researches heavily emphasize more on cognition rather than emotion. Quite recently, many researches also unveil that emotion is as important as cognition either learning traditionally or online, however less research focus on design criteria that evoke the emotions of the learners that promote positive learning experiences. Therefore, this research aims to propose a design model for having an affective e-learning material design that evoke emotion to facilitate online learning for positive learning experience. In this research Kansei Engineering (KE) methodology is adopted, as it has been a successful proven method for capturing emotion and relate the emotion with specific design elements. KE is also popular in various domains such as in robotic, manufacturing and service. Adopting the KE methodology leads on identifying three research objectives. The first is identifying the important learners’ emotional responses towards e-learning material. 36 respondents participated in the experiment using 10 online courses as the specimens. The Factor Analysis and the expert evaluation reveal 55 important kansei emotions from 478 emotions words. The second is identifying the important design elements of the e-learning material that influence positive and negative emotional responses. This research used online Database course as research domain. 48 respondents participated in the experiments using 16 online courses as the specimens using the 55 kansei emotion words. The result has found four important pillars or kansei semantic space of kansei emotions based on the e-learning material, such as engaged-motivated, challenging-thinking, touched-warm and humor. The important pillar is analyzed using Partial Least Square analysis. Other additional result found: a significant relationship between the demographic profiles of the learners and the emotions such as the gender; a significant relationship between the learning styles of the learners and the emotions based on Felder and Silverman Learning Style Model; and more importantly reveals the design specification for specific emotions. The experiments in this study are conducted in a public institution of higher learning in Malaysia which limited to tertiary education, Malay culture and young age in early 20s.With such results had leads to third objective, to propose a design model for positive learning experience based on the design of emotional e-learning material. The interview result from the second experiment based on the perspective of the learners shows that the proposed design model is important towards promoting positive learning experience in e-learning environment.,Certification of Master's/Doctoral Thesis" is not available | - |
| dc.language.iso | eng | - |
| dc.publisher | UKM, Bangi | - |
| dc.relation | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat | - |
| dc.rights | UKM | - |
| dc.subject | E-learning | - |
| dc.subject | Kansei Engineering | - |
| dc.subject | Human engineering | - |
| dc.title | An emotional e-learning material design model based on Kansei Engineering approach | - |
| dc.type | Theses | - |
| dc.format.pages | 310 | - |
| dc.identifier.callno | T59.7.F338 2016 3 tesis | - |
| dc.identifier.barcode | 002765(2017) | - |
| Appears in Collections: | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat | |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_96649+SOURCE1+SOURCE1.0.PDF Restricted Access | 533.23 kB | Adobe PDF |  View/Open |
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