Contextual Academic Achievement Analysis Affected by COVID-19 Pandemic of Higher Education Learners in Thailand Using Machine Learning Techniques
January 8, 2024 2024-01-13 13:46Contextual Academic Achievement Analysis Affected by COVID-19 Pandemic of Higher Education Learners in Thailand Using Machine Learning Techniques

Contextual Academic Achievement Analysis Affected by COVID-19 Pandemic of Higher Education Learners in Thailand Using Machine Learning Techniques
Abstract: This research aims to present the context and impact that the Thai education system has experienced from the COVID-19 pandemic in Thailand. It consists of three research objectives: (1) to study the context of the impact on academic achievement from the COVID-19 pandemic in higher education, (2) to develop a model for clustering the academic achievement of students in higher education during the COVID-19 pandemic in Thailand, and (3) to compare the academic achievement of students in higher education during the COVID-19 pandemic in Thailand. The research data were 43,230 transactions (1961 students) from four educational programs at the Faculty of Business Administration and Information Technology, Rajamangala University of Technology Tawan-ok, the results showed that the context of the impact on the education system among tertiary learners has decreased in the number of graduates during the COVID-19 pandemic. However, students graduating during the COVID-19 pandemic in Thailand had higher levels of academic achievement than those in normal circumstances. The findings reflect those learners who achieved academic achievement during the COVID-19 pandemic were more persevering and tolerant than those in the traditional system.
Keywords: Academic achievement model, COVID-19 challenges, disruptive technologies in education, educational data mining
Cite this paper
Phanniphong, K., Nuankaew, W.S., Teeraputhon, D., Nuankaew, P. (2023). Contextual Academic Achievement Analysis Affected by COVID-19 Pandemic of Higher Education Learners in Thailand Using Machine Learning Techniques. In: So-In, C., Londhe, N.D., Bhatt, N., Kitsing, M. (eds) Information Systems for Intelligent Systems . Smart Innovation, Systems and Technologies, vol 324. Springer, Singapore. https://doi.org/10.1007/978-981-19-7447-2_15