Browsing by Author "Jirapun Daengdej"
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ItemDeterminants of student satisfaction on continuing education intention: a case study of private university in CambodiaThe purpose of this study is to examine the impact of determinants of student satisfaction on continuing education intention for a master’s degree at a leading private university, locating in Kampong Cham province, Cambodia. The study was conducted quantitatively and applied the multi-stage sampling technique by employing purposive sampling, simple random sampling, and quotasampling method. A sample size of 600 students from year 1 semester II to year 4 who are studying in various majors in this private university were involved. This study adapted the Confirmatory Factor Analysis (CFA) and the Structural Equation Model (SEM)to identify the relationship and the impact of determinants of student satisfaction on student’s continuing education intention for a master’s degree at a private university. The results revealed that among six determinants of student satisfaction, 4 determinants namely curriculum quality, service facility, teaching quality, and campus life & social integration have significant impact student satisfaction. Moreover, student satisfaction had significantly affected intention for continuing education. Additionally, the most significant determinant of student satisfaction is campus life & social integration, followed by teaching quality, curriculum quality, and service facility, while college administration and financial assistance & tuition costs have no significant impact on student satisfaction.
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ItemFactors affecting intention to E-learning systems in selected Universities in Phnom Penh, CambodiaThis research investigates factors influencing the intention to use e-learning systems in selected universities in Phnom Penh, Cambodia. The conceptual framework has been developed by adopting previous theoretical studies and research models of the modified unified theory of acceptance and use of technology (UTAUT2). Five hundred questionnaires were collected from undergraduate students through Google form survey with universities’ administration assistance. Multi-stage sampling was used: the first stage is stratified random sampling followed by purposive sampling. Collected data were analyzed using the Structural Equation Model (SEM) and Confirmatory Factor Analysis (CFA) to confirm the model fit and hypothesis testing. The findings showed that performance expectancy, effort expectancy and self-efficacy did not have any influence on behavioral intention. However, social influence had the strongest influence on behavioral intention to use e-learning systems, followed by facilitating conditions. Moreover, facilitating conditions and behavioral intention had a significant influence on use behavior of e-learning systems. This study provided theoretical implications for researchers related to technology adoption and information for training institutions, universities, schools and academic staff on issues they need to focus on when they wish to launch any new system or online services.
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ItemA framework for risk management in AI system development projects( 2019) Kitti Photikitti ; Kitikorn Dowpiset ; Jirapun DaengdejIt has been well-known that the chance of successfully delivering a software project within an allocated time and budget is very low. Most of the researches in this area have concluded that “user's requirements” of the systems is one of the most difficult risks to deal with in this case. Interestingly, until today, regardless of amount of effort put into this area, the possibility of project failure is still very high. The issue with requirement can be significantly increased when developing an artificial intelligence (AI) system, where one would like the systems to autonomously behave. This is because we are not only dealing with user's requirements, but we must also be able to deal with “system's behavior” that, in many cases, do not even exist during software development. This chapter discusses a preliminary work on a framework for risk management for AI systems development projects. The goal of this framework is to help project management in minimizing risk that can lead AI software projects to fail due to the inability to finish the projects on time and within budget.
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