Empirical analysis of factors influencing student satisfaction with online learning systems during the COVID-19 pandemic in Thailand

Abstract

Starting in early 2020, Thailand's education system came to a grinding halt due to the global COVID-19 pandemic, which created a fervor-like effort to move from traditional classrooms to online education. However, the process has experienced significant troubles. Therefore, starting in June 2021, multiple-stage random sampling and simple random sampling were used to select a sample of 270 Thai high school students across nine Thai provinces. Using a network of Thai teachers, students were assisted with their questionnaire input using Google Form. LISREL 9.1 software was used to conduct the subsequent goodness-of-fit (GOF) assessment and the confirmatory factor analysis (CFA). A structural equation model (SEM) was used for the 53-item questionnaire, which contained eight latent variables, 18 observed variables, and ten hypotheses. Descriptive statistics were used to analyze the SEM's output and ten hypotheses. After that, it was calculated that the model's causal variables had a positive effect on SS, which had an R 2 of 54%. The analysis also revealed that when ranked by total effect (TE) values, performance expectancy (PE = 0.43) was most significant, followed by actual use (AU = 0.30), learner interaction (LI = 0.18), and behavioral intention (BI = 0.12). Overall, hypotheses testing established three moderately strong correlations, four weak correlations, and three unsupported hypotheses. The novelty of our study is the growing concern of stakeholders for how online learning affects student satisfaction due to the deadly global COVID-19 pandemic. This study's research contribution is that it is unique in that it was conducted during the pandemic lockdown while students were participating in Thai Ministry of Education (MOE) online courses. This paper contributes to the online education domain by providing research directions and implications for future researchers. In conclusion, the study confirmed that the model adequately explained causal relationships between variables and presented direct and indirect significant impacts on online SS, promoting learners' better academic performance and knowledge acquisition.

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