Academic course management system evaluation using data mining techniques.

• 主要作者: Kuan, Sung.
• 其他作者: Theng, Yin Leng
• 格式: Theses and Dissertations
• 語言: English
• 出版: 2008
• 主題: DRNTU::Library and information science::Libraries::Information systems
• 在線閱讀: http://hdl.handle.net/10356/14506
• 機構: Nanyang Technological University

Course management systems or e-learning systems are widely adopted in Singapore with an estimated market size at US$106 million in 2005. Research has shown that the success of such system depends on initial adoption (acceptance) and subsequent usage (continuance) of it. As more course management systems are being deployed and its complexity increases, the unanimous decisions are often made by managers or educators who know little about the technology. Many studies found that most course management systems are not friendly enough to influence learners’ continuance and consequently the full benefits could not always be realized. Traditional research techniques of evaluating the usefulness of course management systems often relied on quantitative or qualitative survey methods, and this can be time-consuming and cumbersome, incurring huge costs. This dissertation aims to apply data mining techniques on course management systems of three local universities in Singapore to harvest students’ attitude and usage behaviors. Data mining techniques can be applied on the information collected to provide valuable insights to help educators, managers, designers and developers to implement better course management systems leading to better return on investment. Using the data collected from surveys conducted on three Singapore universities in which 1417 responses were gathered, advanced statistical tests and the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology was used to mine the survey responses. The outcomes were then validated using the modified expectancy disconfirmation theory to understand students’ perceived acceptance, adoption and continuance of the e-learning systems.