Technology became considerably more critical for mathematics teachers during the Covid-19 pandemic era. Apart from examining pre-service mathematics teachers' knowledge about technology integration, which failed to reflect the unique characteristics of mathematics and underrated teachers' perception toward using technology in mathematics classrooms, this study aims to examine pre-service mathematics teachers' technology integrated competency through an enhancement program. Data were gathered from 25 pre-service mathematics teachers at Lampang Rajabhat University through journals, artifacts, and focus group interviews. Quantitative and qualitative analysis was by the research analytic framework's categories to define changes in participants' technology integrated competency. The primary finding was that participants gained a better knowledge of technology integrated lesson design during a four-month period. Most participants moved their emphasis away from technology as a teaching aid and toward providing students with mathematical learning instruments. Additionally, they emphasized the significance of their courage. They did not overlook the necessity of adequate mathematical knowledge for teaching when it came to improving mathematics teachers' roles in creating a successful technology integrated mathematics lesson. It was discovered in this study that the cooperative initiation and open lesson observation of pre-service mathematics teachers had a direct effect on their lesson preparation.

In this work, a new machine learning-based model is proposed to predict undergraduate students' reading scores using their listening scores as the primary data. The performance of several machine learning techniques, including neural networks, gaussian process regression, and random forests, was calculated and compared in order to predict the reading test results of the students. The dataset included the listening and reading test results of 1145 students who took the English proficiency exam at Lampang Rajabhat University's language center in Lampang, Thailand. According to the results, the suggested model has a classification accuracy range of 64–75%. Only three different types of parameters—listening scores, departmental data, and faculty data—were used to make the predictions.

In Thai mathematics classrooms, there is a lack of attention to support students’ mathematical problem-solving skills by working from real-world contexts that make sense to students. This study aimed to investigate how pre-service mathematics teachers’ problem solving can be explored in their content course, intervening with technology and Realistic Mathematics Education (RME) through the Mean Value Theorem (MVT) lesson. The study included nine pre-service mathematics teachers purposively selected from a public university in Thailand who attended a Calculus course. Data was collected from classroom artifacts, observation notes, and interviews. It was found in this study that the intervention of technology and RME in pre-service mathematics teachers’ content courses has the potential to build pre-service mathematics teachers’ problem-solving abilities. It was also discussed that the intervention could use RME to conceptualize mathematics theorem and cultivate Polya’s problem-solving steps. The findings provide light on the efficacy of using technology and RME in enhancing problem-solving skills among other content courses and could be used to inform the creation of mathematics curricula and instructional strategies in undergraduate content courses for mathematics education programs.

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Educational data classification has become an effective tool for exploring the hidden pattern or relationship in educational data and predicting students’ performance or teachers’ competency. This study proposes a new method based on machine learning algorithms to predict the technology-integrated competency of pre-service mathematics teachers. In this paper, we modified the inertial subgradient extragradient algorithm for pseudomonotone equilibrium and proved the weak convergence theorem under some suitable conditions in Hilbert spaces. We then applied to solve data classification by extreme learning machine using the dataset comprised of the technology-integrated competency of 954 pre-service mathematics teachers in a university in northern Thailand, longitudinally collected for five years. The flexibility of our algorithm was shown by comparisons of the choice of different parameters. The performance was calculated and compared with the existing algorithms to be implemented for prediction. The results show that the proposed method achieved a classification accuracy of 81.06%. The predictions were implemented using ten attributes, including demographic information, skills, and knowledge relating to technology developed throughout the teacher education program. Such data driven studies are significant for establishing a prospective teacher competency analysis framework in teacher education and contributing to decision-making for policy design.