Analysis of Students’ Learning Satisfaction in a Social Community Supported Computer Principles and Practice Course
Yu-Shan Lin 1, Shih-Yeh Chen 2, Yu-Sheng Su 3, Chin-Feng Lai 4 *
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1 National Taitung University, Department of Information Science and Management Systems, Taitung, TAIWAN2 National Taitung University, Department of Computer Science and Information Engineering, Taitung, TAIWAN3 National Central University, Taoyuan, TAIWAN4 National Cheng Kung University, Tainan, TAIWAN* Corresponding Author

This article has been presented in IEEE ICICE 2017 - International Conference on Information, Communication and Engineering held in Xiamen, Fujian, P.R. China on November 17–20, 2017.

This article belongs to the special issue “Selected papers from IEEE ICICE 2017”

Abstract

The study compares the learning satisfaction of two student groups, one takes the fully online course–Introduction to Internet of Things, and the other takes the small private online course (SPOC). In the research framework, learning satisfaction is the dependent variable, and learning engagement, learning presence, video perception, platform perception, and design perception are independent variables. This work adopts online questionnaire survey to collect data from the two student groups. As to research method, Multiple Regression Analysis (MRA) is utilized to test proposed research framework. The results of MRA show that platform perception generates students’ learning satisfaction for SPOC, while video perception and design perception generate students’ learning satisfaction for fully online course. This empirical study elucidates the factors influence learner’s satisfaction and contributes to theory and practice in the domains of online courses.

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

EURASIA J Math Sci Tech Ed, Volume 14, Issue 3, March 2018, 849-858

https://doi.org/10.12973/ejmste/81058

Publication date: 07 Dec 2017

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Article Downloads: 3785

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