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.

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 objectives of this research were 1) to study the factors of the relationship between the learner context and the curriculum in higher education, 2) to construct of the model relationship between the learner context and the curriculum in higher education, and 3) to test the quality and accuracy after having the model and prototype application of the relationship between the learner context and the curriculum in higher education. Seven instruments were used in this research, including mean, standard deviation, percentages, decision tree, text mining, cross validation, and confusion matrix. The research findings are as follows; 1) The factors that are important to the learner’s continuing studying consist of two factors: education system, and interest in studying. 2) The results of the model performance showed that the model has a high level of accuracy (76.50%). 3) The result of the prototype test application by the user is also acceptable, with 68.98 percent accuracy from 1,109 testers. In the future, the researcher has the expectation to develop more accurate predictions.

No abstract

The Coronavirus epidemic 2019 has a serious impact on the education system of Thailand. Therefore, the research aims (1) to study and compare the academic achievement of higher education students affected by the situation of Corona Virus Disease 2019 (COVID-19) epidemic, (2) to construct an academic achievement model with educational engineering technology to support the learning management process of higher education institutions, and (3) to evaluate the academic achievement model. The research approaches were carried out according to the theory of data mining development using the CRISP-DM methodology (Cross-Industry Standard Process for Data Mining). The data collection was divided into two main parts according to the educational situation. The first part is a normal situation with data collected from 506 students form four courses during the second semester in the academic year of 2019. On the other hand, the second part is an abnormal situation with data collected from 475 students from four courses during the first semester in the academic year of 2020. From the research results and findings, the researchers believe that both of the traditional and online teaching and learning management enabled learners to make an academic achievement. It is imperative that the teacher is aware of the learner’s importance to graduate on time. For the future, the researcher needs to present the findings to the stakeholders in order to prepare for the unexpected situation.