Purpose The purpose of this study was to investigate factors that influence the intention to use mobile learning (m-learning) by learners in developing countries such as Thailand. This study integrated two theories; namely, the unified theory of acceptance and use of technology (UTAUT), which focuses on technology, and uses and gratifications theory (UGT), which involves studying learners’ motivation. Design/methodology/approach Applying a quantitative research method, this study conducted a survey of 359 undergraduates. The partial least squares methods and a statistical analysis technique based on the structural equation modelling (SEM), were used to analyse the data. Findings The results revealed that the performance expectancy, cognitive need, affective need and social need had significant effect on intention to use m-learning. Furthermore, this study found a significant effect of the cognitive need on the performance expectancy and social need on effort expectancy. Practical implications This research model has provided guidelines for the effective development of educational applications for use on mobile devices. The findings can be applied as guidelines for public organizations to develop educational strategies to further encourage the development of online learning. Originality/value This research closed a gap of understanding from previous studies by integrating UTAUT and UGT. The method derived from the theoretically integrated model could be applied to study the intentions for the implementation the mobile learning application from the context of developing countries such as Thailand.

Twenty-First Century Education is a design of instructional culture that empowers learner-centered through the philosophy of "Less teaching but more learning". Due to the development of technology enhance learning in developing countries such as Thailand, online learning is rapidly growing in the electronic learning market. ClassStart is a learning management system developed to support Thailand's educational management and to promote the student-centred learning processes. It also allows the instructor to analyse individual learners through system-generated activities. The study of online learning acceptance is primarily required to successfully achieve online learning system development. However, the behavioural intention of students to use online learning systems has not been well examined, in particular, by focusing specific but representative applications such as ClassStart in this study. This research takes the usage of ClassStart as research scenario and investigates the individual acceptance of technology through the Unified Theory of Acceptance and Use of Technology, as well as technological quality through the Delone and McLean IS success model. A total of 307 undergraduate students using ClassStart responded to the survey. The Partial Least Squares method, a statistics analysis technique based on the Structural Equation Model (SEM), was used to analyze the data. It was found that performance expectancy, social influence, information quality and system quality have the significant effect on intention to use ClassStart. Keyword: ClassStart, Unified Theory of Acceptance and Use of Technology (UTAUT), Delone and McLean IS, Thailand.

Recognizing the underlying relationship between e-learning practice and the institutional environments hosted in, the Chinese educational practice on branching high school students into science, technology, engineering, and mathematics (STEM) and non-STEM academic major groups before being admitted into universities or colleges is examined. By extending the wellestablished Technology Acceptance Model (TAM) with computer self-efficacy, this study aims to examine the difference in perceptions and behaviours on e-learning adoption from the STEM and non-STEM students. The results revealed that STEM"s score of computer self-efficacy, perceived ease of use and behavioural intention to use e-learning are all greater than non-STEM"s.

Purpose This study aims to study the adoption of online learning in higher education through the perspective of the readiness of the following factors: self-directed learning (SDL), motivation for learning (ML), online communication self-efficacy (OCE) and learner control (LC). This was an empirical study in the context of developing countries, specifically Thailand. Design/methodology/approach This research applied a quantitative study method by collecting data from 605 higher education students in autonomous government institutions. The data analysis applied a structural equation model (SEM) to identify the significant determinants that affected the adoption of online learning. Moreover, this study applied a neural network model to examine the findings from the SEM. Findings From the data analysis using the SEM and neural network model, the results matched each other. The results of the empirical study were firm and supported that the readiness factors of students had statistical significance in the following order: SDL, OCE, LC and ML. Practical implications The study results showed an operational perspective to be prepared for online teaching, both for the related department of the Ministry of Education to support the infrastructure for online learning and for universities and instructors to create learning conditions and design teaching processes consistently with the online learning context. Originality/value Since the learning management in the 21st century is focused on student-centred learning, the empirical results obtained from this study presented the view of learners’ readiness that would influence the acceptance of online learning. In addition, this research presented the challenges and opportunities of online instruction during the COVID-19 pandemic.

Food recommendation system is one of the most interesting recommendation problems since it provides data for decision-making to users on selection of foods that meets individual preference of each user. Personalized recommender system has been used to recommend foods or menus to respond to requirements and restrictions of each user in a better way. This research study aimed to develop a personalized healthy food recommendation system based on collaborative filtering and knapsack method. Assessment results found that users were satisfied with the personalized healthy food recommendation system based on collaborative filtering and knapsack problem algorithm which included ability of operating system, screen design, and efficiency of operating system. The average satisfaction score overall was 4.20 implying that users had an excellent level of satisfaction.