This research was conducted in order to determine whether math anxiety was effective in the selection of secondary school students. This research, in which the mixed method was adopted, was conducted in a secondary school at Siirt. The participants (157 pupils) were determined by convenient sampling method from the four different classes (two 5th grade and two 8th grade). Math anxiety levels of the students were determined by the Mathematics Anxiety Scale. Sociometry technique was used to determine the informal friends groups in each class. For this purpose, a friend preference form was used. Students' friends preferences were transformed into data matrix and Hierarchical Clustering Analysis was used to form informal friends groups for each class. Math anxiety scores of informal groups were compared with Kruskal Wallis test. Accordingly, there were significant differences between the mathematical scores of the informal groups in the branches of the 5th and 8th grades. This finding was evaluated that mathematics anxiety may be a factor in the selection of friends.  

Science literacy, which is included in Programme for International Student Assessment (PISA) as an assessment area, is an important research and discussion area of science education literature with all its dimensions. In this study, the clustering results of the students from 34 Organization for Economic Cooperation and Development (OECD) countries participating in the PISA 2015 test and sampled by systematic sampling method are obtained by K-Means Clustering and Two-Step Cluster Analysis using the factor scores and PISA science literacy average scores. It is thought that the study is of great importance in terms of dividing individuals into clusters according to science instruction methods and the mean of plausible values and having an idea about how each cluster is defined. As a result of the K-means cluster analysis, it was determined that the input variable with the highest level of importance in the formation of the first and third clusters in which the students with the highest scores were included was teacher-directed science instruction, and after this variable, the input variable with the highest level of importance was the perceived feedback from science teachers. Within the scope of the Two-Step Clustering Analysis, it was determined that teacher-directed science instruction has the most importance in terms of the decomposition of clusters, followed by adaptive instruction in science lessons in terms of importance level.

During the pandemic of Coronavirus disease 2019 (COVID-19), English as a foreign language (EFL) students have to study and submit their assignments and quizzes through online systems using electronic files instead of hardcopies. This has created an opportunity for teachers to use computer tools to conduct preliminary assessment of the students’ writing performance and then give advice to them timely. Hence, this paper proposed some indicators which were essay readability scored by Flesch Reading Ease (FRE), length of essays, errors in writing and a method to assist the teachers in providing writing feedback to the students. The results showed a large difference in FRE, the number of words, sentences, paragraphs and errors. The K-means clustering findings were applied to classify groups of students based on writing proficiency indicators. The findings also revealed that the number of words and sentences in the essays indicated some deficiencies. The concept of paragraph should be reinforced while some specific errors such as misspelling, grammatical and typographical errors found need to be eliminated. This study showcased that the computer tools should be integrated to process the students’ essays so that the teachers can pinpoint the problems and make suggestions to their students in appropriate time. Lastly, the results can be served as the guidelines for the teachers to develop and adjust teaching materials pertinent to writing to enhance the writing performance of EFL learners.

The influence of COVID-19 has caused a sudden change in learning patterns. Therefore, this research studied the learning achievement modified by online learning patterns affected by COVID-19 at Rajabhat Maha Sarakham University. This research has three objectives. The first objective is to study the cluster of learning outcomes affected by COVID-19 at Rajabhat Maha Sarakham University. The second objective is to develop a predictive model using machine learning and data mining technique for clustering learning outcomes affected by COVID-19. The third objective is to evaluate the predictive model for clustering learning outcomes affected by COVID-19 at Rajabhat Maha Sarakham University. Data collection comprised 139 students from two courses selected by purposive sampling from the Faculty of Information Technology at the Rajabhat Maha Sarakham University during the academic year 2020-2021. Research tools include student educational information, machine learning model development, and data mining-based model performance testing. The research findings revealed the strengths of using educational data mining techniques for developing student relationships, which can effectively manage quality teaching and learning in online patterns. The model developed in the research has a high level of accuracy. Accordingly, the application of machine learning technology obviously supports and promotes learner quality development.

Teachers' classroom practice encompasses diverse facets of teacher behavior. Understanding teachers’ classroom practice is important as the literature has been well documented its association with student, teacher, and classroom outcomes. Positive teacher-student relationships and proper classroom management, both are important aspects of teachers’ classroom practices, have been empirically linked to increasing student academic performance and reducing problem behaviors. This study explored patterns of teacher classroom practices across teacher-student relationships and classroom management and validated them using a person-oriented approach, k-means clustering, on three sets of data with over 600 participants. Using indicators of classroom assessment scoring system (CLASS), three distinct teacher profiles were identified: supportive, intermediate, and detached. Supportive teachers demonstrated high levels of teacher-student relationships and classroom management, while detached teachers showed low levels of both. Intermediate teachers fell between these two groups. The study also compared these profiles based on teacher self-efficacy, work stress, and classroom climate. Supportive teachers had the highest classroom climate scores, followed by intermediate and detached. Work stress was comparable for intermediate and supportive teachers, but both were lower than detached. No group differences were found for self-efficacy. This study contributes to the understanding of teacher behaviors with students in the lower grades.