TAM theory is applied in this research and the research is to study the factors that will lead to acceptance and participation of e-Learning system for the development of IT Literacy for Thai students. It uses the technique of SEM and the result from collecting data of 420 students found that Goodness of fit indicators which also included a chi-square value of 267.84, CMIN/ DF = 1.87, SRMR = .05, GFI = .93, TLI = .97, CFI = .97, RMSEA = .05 and HOELTER = 269. Additionally, there are 2 factors which include; Perceived usefulness (PU) and Perceived ease of use (PEOU). Both factors have positive (+) direct influence on behavioral intention (BI). Moreover, there are 3 factors including Information quality (IQ), Functionality (FL) and Accessibility (A) which have a positive (+) indirect influence on behavioral intention (BI). The BI have been determined based on (PU) perceived usefulness along with perceived ease of use (PEOU). This lead to result in a R 2 = .80. Therefore, the variables described earlier lead to the explanation that 80% of variance on behavioral intention (BI).
The optimal management of personal resources impacts everyone’s quality of life. An investment in graduate education is a sustainable opportunity for improved outcomes in human life, including cognition, behavior, life opportunities, salary, and career. Advanced technology dramatically reduces the risk of personal resources in graduate program admission recommendations that depend on multiple individual needs and preferences. In the digital age, a dynamic recommender system enhances the suitably effective solution for students’ university selections. This study focused on designing, developing, and testing a recommender system for graduate admission using a dynamic multi-criteria AHP and fuzzy AHP approach. The explicit multi-criteria recommender system was a platform as a service (PaaS) web application created to aid in graduate admissions management and decision-making. The design proposed that the bit representation store a dynamic explicit multi-criteria data structure. The recommendations adopting dynamic multi-criteria were validated by comparing them to the programs to which the students were actually admitted and enrolled. They individually ranked the evaluation outcomes of dynamic explicit multi-criteria and alternative preferences to provide graduate admission recommendations. Eighty graduate students in information technology evaluated the recommender system. Using top-1, top-2, and F1-score accuracy, the effective system accuracy performance on the dynamic multi-criteria recommender system was evaluated using AHP and fuzzy AHP approaches. The fuzzy AHP demonstrated marginally greater practical accuracy than the AHP method.
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