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Mahasarakham University (MSU) has pursued undergraduate application recruitment under the policy set forth by the Ministry of Higher Education, Science, Research and Innovation (MHESI) which prescribes that universities are required to partake in the Thai University Central Admission System (TCAS). From the year 2018 when MSU joined TCAS to the present MUS found several problems such as a declining number of applicants, the unforeseen tendency of the number of prospective applicants, unspecified prospective applicants, and unknown applicants characteristics in selecting programs in a sequence. In this study, the Mining Association Technique was proposed with the Apriori Algorithm. The high reliability of the mining association was 80.00 %. The results showed that the factors affecting the undergraduate program selection mostly included General Aptitude (GAT) scores, Professional and Academic Aptitude (PAT) scores, school size, school location, and applicant's gender. The results obtained are expected to be used to discover data relationships for selecting programs for the applicants and be further used for planning the undergraduate applicant selection in the MSU's TCAS system. [ABSTRACT FROM AUTHOR]

Thai University Central Admission System (TCAS) is an educational reform policy embracing three main principles; including, 1) students are required to attend classroom until the completion of the upper secondary education (Matthayom 6), 2) each student possessed one right on the courses selected on equitably basis, and lastly, 2) each student possessed one right on the courses selected on equitably basis, and lastly, 3) all higher education institutions subject to the Council of University Presidents of Thailand (CUPT) 's Networks are required to participate in the clearinghouse to administrate student's one right to study. Based on the Mahasarakham University policy, the Mahasarakham University is a public university under the CUPT network that follows Thai University Central Admission System (TCAS). The issues of Thai University Central Admission System (TCAS) showed that the number of right confirmations (Clearing House) was smaller than the admission plans, compared to the large number of applicants interested in continuing study. For this reason, the researcher has an idea of using Data Mining Techniques to identify patterns that have arisen out of student data that have the confirmation right to study, so that the model to be acquired will further be developed into a support system for TCAS. in order that the TCAS Committee can quantify the confirmations and make sure that the number of admissions are mostly consistent with the admission plan. The results indicated that decision trees and association rules on the experimental data was accurate by 82.85 percent on Humanities and Social Sciences, formulating into 89 association rules, and it was accurate by 80.88 percent on Health Science, formulating into 85 association rules, and it was accurate by 78.85 percent on Technology Science, formulating into 85 association rules. The results could be efficient to the development of the TCAS support system. [ABSTRACT FROM AUTHOR]

MahaSarakham University has an application procedure in place that helps students select subjects to study in the bachelor degree program; there are a large number of applicants each semester. The main problem with the application procedure is the number of unqualified students recommended to study in the applied fields: some fields have fewer applicants than shown in the admission plan. According to the admission data, there are a lot of applicants in some fields when compared with fewer in the admission plan’s proportion. Most applicants appliy for fields they are fond of regardless of their admission scores. As a result, many applicants were not selected are using their score for placement. This research has shown how to use the Data Mining via the Decision Tree and Naïve Bayes techniques in order to compare the efficiency model accuracy and develop the model as a decision support system that selects study field opportunities in the bachelor’s degree program. As to the research, there were 69 fields of study with the highest accuracy from the Decision Tree technique and there was 1 field of study with the highest accuracy from the Naïve Bayes technique. [ABSTRACT FROM AUTHOR]