Study on Success and Fitness Model of Applying Cloud Computing Technologies in Learning and Teaching

• Main Authors: Tzung-Han Wu, 吳宗翰
• Other Authors: Mon-Yen Luo
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/51166163055009794548

碩士 === 國立高雄應用科技大學 === 資訊工程系 === 102 === With the advancement of information technology, cloud service is neither bounded by time nor space, and can provide its service by Internet.it has gradually penetrated into any fields. There are more attempts and related research trying to cloud technology into teaching in education. What kind of cloud technology be applied to teaching and learning? Could we know how to effectively use cloud technology to improve existing defects of teaching and learning? Can importing cloud computing technology truly enhance student learning or improve the effectiveness of teaching? There are further study to clarify important issues. The purpose of this study was to propose a systematic assessment of the cloud teaching model. This study integrates Task/Technology Fit model(include: learning task, cloud task, and fit),DeLone & McLean’s IS success model(include:system quality, information quality,service quality and system satisfaction), and computer self-efficacy, to understand student’s continuance intention of cloud e-learning system.This study used cloud e-learning system of the southern technology university. This is a study of university students who had used cloud e-learning system, 116 questionnaires issued, and 106 valid questionnaires were retrieved, except the invalid ones. Statistical method is Partial Least Squares. we came up with the following three conclusions. 1. Learning task, cloud task, and computer self-efficacy has significantly positive effects on fit. 2. System quality, information quality, system quality, and fit has significantly positive effects on system satisfaction. 3. After PLS analysis, the total effect influencing continuance intention ranks as follow: fit, system satisfaction, cloud task, computer self-efficacy, information quality, system quality, service quality, learning task.