Early detection of Type 2 diabetes is necessary for its prevention. The prediction models for detection systems normally employ common factors that may not properly fit all persons having different health conditions. Therefore, this study proposes a method for type 2 diabetes prediction with factors representing personal health conditions. More specifically, this study proposes a novel prediction method named Average Weighted Objective Distance (AWOD) based on the assumption that the individual has diverse health conditions resulting from different individual factors, a requirement for an effective prediction model. AWOD is a modification of Weighted Objective Distance (WOD) by applying information gain to reveal significant and insignificant individual factors having different priorities, which are represented by different weights. For AWOD, the data set is divided into a training set used to determine all relevant thresholds and constant values required for AWOD calculation and the testing set. In particular, AWOD is designed for binary classification problems with a relatively small dataset. To validate the proposed method, two datasets from open sources, Pima Indians Diabetes (Dataset 1) and Mendeley Data for Diabetes (Dataset 2) each containing 392 records, were studied. The prediction performance for both datasets is compared with the machine learning-based prediction methods, including K-Nearest Neighbors, Support Vector Machines, Random Forest, and Deep Learning. The comparison results showed that the proposed method provided 93.22% and 98.95% accuracy for Dataset 1 and Dataset 2, respectively, which are higher than those provided by other machine learning-based methods.

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.

This paper aims to study the problem of the dropout situation of students in the business computer program at the University of Phayao. It consists of three sub-goals: 1) identifying the factors, 2) studying the relationship of factors, and 3) testing the relationship model. Data collected 389 students from the Department of Business Computer at University of Phayao from the academic year 2012-2019. The tools used in the research are statistical data analysis and machine learning. It consists of percentages, decision tree algorithms, cross-validation methods, and the confusion matrix performance. The results showed that the dropout rate of learners in business computer program tended to increase even though the number of new students decreased. In addition, it was found that factors affecting the dropout consisted of seven courses: 221110, 221120, 001103, 128221, 005171, 122130 and 128221. The model obtained as a high performance level of prediction with accuracy is equal to 87.21%. Based on the research results, the researcher found that the academic results had a significant influence on dropout, which were mostly obvious with the students in their first academic year.

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.

Artificial intelligence technology brings wide impacts on several dimensions. The impact on the education system is that educational technology has been disrupted, it radically changed the paradigm of learning management. Therefore, this research aimed to study the paradigm shift of the education system focusing on the deployment of artificial intelligence technology to support the learning model in the era affected by the COVID-19 pandemic. There are two research objectives: (1) to study an appropriate self-regulated learning model with data mining techniques for designing appropriate online learning management, and (2) to study the learning achievement factors of learners by applying blended learning and self-regulated learning techniques. The samples were 26 students at the University of Phayao who enrolled in the course 221203 Technology for Business Application in the 2nd semester of the academic year 2020. The research tool is a statistical analysis and machine learning tool. It consists of analyzing pre-test scores, post-test scores, midterm scores, final scores, academic achievement, clustering analysis, and clustering performance. As a result, it found that learners had five reasonable clusters for the academic achievement learning model. The results specified the different learning styles of the learners in two dimensions including online and offline scenarios. Therefore, in future work, the researcher looks forward to performing research in the scope of identifying the suitability and the necessity of converting the face-to-face learning model to a fully online learning model.

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.