Attitudes and learning styles can affect academic achievement at different levels. While analyzing attitudes and learning styles can not only use basic statistics, using advanced tools to analyze the students' in-depth elements is discussed. Therefore, this research offers an appropriate method for clustering academic achievement (GPA) that support student’s attitudes and learning styles. At the same time, this research is aimed to study the level of attitudes towards learning styles in different academic achievement of students at the University of Phayao. The data collection was conducted from 195 students from 17 schools and colleges at the University of Phayao, Thailand. The results show that there is a variety of cluster in students’ attitudes and learning styles with a significant pattern (types of success) of the students’ model, while the model performance has a very high efficiency to the model. In future work, it will be applied with other universities in Thailand and also used in developing applications for providing a program recommended for appropriate educational programs.

The Self-Regulated Learning (SRL) strategies can be the best. It can be achieved by a sub-goal that will be more important in the younger generation. This paper proposes the process of developing factors (attributes) which are related to the development of learning styles through self-regulated strategies. The objectives of this paper are (1) to study the perception and attitude toward the attributes of students with self-regulated learning of the students in higher education, and (2) to find the level of acceptance towards the factor of SRL using applied statistics and machine learning technology. The results show that two tools have proved the respondents and the factors of SRL in the accepted level. Besides, the results found that Thai higher education students still focus on formal learning, which conflicts with the behavior and us-age of Internet and telephone in the classroom. In future work, the author is committed to develop and apply a self-regulated learning strategy model with a combination of collaborative learning strategies of blended learning. Also, it supports undergraduate students in analyzing the factors and studying the behavior patterns of learners in suitable modern learning.

An educational program that does not accept the change of disruptive technology will inevitably result in future destruction. There are two objectives including (1) to construct reasonable students’ dropout prediction model for business computer disciplines, and (2) to evaluate the model performance. Data collected consists of 2,017 records from students who enrolled in the business computer program at the School of Information and Communication Technology, the University of Phayao. Research tools are divided into two parts. (1) Modelling; it consisted of the Artificial Neural Network Algorithm, Decision Tree Algorithm, and Naïve Bayes Algorithm. (2) Model testing; it consisted of the confusion matrix performance, accuracy, precision, and recall measurement. It is a clear innovation in the research that the researcher combines the knowledge of data science in analysis to improve the academic achievement of students in higher education in Thailand. From the analysis results, its show that the model developed from using Artificial Neural Network algorithms has the highest accuracy in the first three data sets (89.04%, 92.70% and 93.71%), and the last model is appropriate for Naïve Bayes algorithm (91.68%). Finally, it is necessary to conduct additional research and present research results to relevant parties and organizations.

The COVID-19 situation has a serious global impact on the education system. Thus, the research purpose is aimed to construct the models of online learning strategies for Thailand students on learning management in the coronavirus 2019 scenario. The research methodology was conducted according to the process of the cross-industry standard process for data mining, known as the CRISP-DM model for developing the best research. The data collected 487 students from the University of Phayao (UP), and Rajabhat Maha Sarakham University (RMU) from the 1st semester in academic year 2020. The collected data has been agreed upon in accordance with research ethics. The results of the study revealed that the factors influencing the model consisted of 8 out of 38 attributes, with a high predictive accuracy (85.14%). Finally, the researchers can plan for the management of teaching and learning for students at the University of Phayao to solve the Coronavirus 2019 Scenario in the academic year 2021 and the future.

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