The development of modern technologies facilitated for teachers to integrate and use them in managing their teaching classroom. The aim of this paper is to explore how teachers in the Khon Kaen University (KKU) Smart Learning Project use the smart learning concept and innovation to manage their classroom in the digital learning environment. The qualitative research was conducted to clarify the teaching process of teachers in Grade 7-9 at junior high schools using KKU Smart Learning with three subjects (namely, Mathematics, Science, and English). Consequently, 18 in-depth interviews and six observations were done. The results of this research showed that teachers applied and used the basis of KKU Smart Learning technologies, contents, and guidelines to manage their classroom, including design teaching activities in accordance with the learners' characteristics; create a pleasant learning atmosphere to enhance their thinking ability, knowledge and skills; organize learning activities and solve problematic situations; promote learning motivation; develop and evaluate learners' competence. However, the classroom management of teachers faced with some problems concerning the limitation of internet connection, lack of digital devices and digital literacy of teachers and learners. The findings of this paper have implications for educators, administrators, teachers and stakeholders to design holistic solutions to enhance the quality of educational activities and academic performance.

In this research, a new numerical method, called the hybrid finite difference–finite element (hybrid FD–FE) method, is developed to solve 2-D magnetotelluric modeling by taking advantage of both the finite difference (FD) and finite element (FE) methods. With the hybrid FD–FE method, the model is first discretized as rectangular blocks and separated into two zones: the FD and FE zones. The FD zone is set for the subregions where topography or bathymetry does not appear. The FD approximation, which is fast, accurate and requires less memory resources, is then applied. For the FE zones where topography or bathymetry exists, the rectangular blocks are transformed into quadrilateral elements to handle the topography or bathymetry appropriately. Then, the FE approximation with quadrilateral elements, which is more accurate for topography or bathymetry zones, is applied. The system of equations for the hybrid FD–FE method is then formed according to the FD and FE schemes. The obtained system is a combination of the FD and FE equations. Three numerical methods are applied to test models with and without topography and bathymetry. The accuracy and efficiency in terms of errors, computational time and memory storage are presented, compared and discussed. The numerical experiments indicate that the FD scheme has a shorter computational time than the other schemes when modeling without topography and retains accuracy equivalent to that of the FE method, whereas FE is more practical when modeling with topography and bathymetry. However, our proposed hybrid FD–FE method is efficient in both situations. Without topography or bathymetry, its efficiency and accuracy approach those of the FD scheme. With topography and bathymetry, the hybrid FD–FE method is as accurate as FE, but its speed is slightly slower than that of FD. In terms of memory storage, the hybrid FD–FE method consumes slightly more storage than the FD method. This hybrid FD–FE method can be further extended and implemented for 3-D magnetotelluric modeling for more efficient computation.

This study aimed to investigate the success factors for transforming classrooms into learning communities in digital learning ecosystem (DLE) of Thailand’s secondary schools. Quantitative research was conducted by using a questionnaire as the research instrument to measure teachers’ evaluation of factors. Purposive sampling was applied to obtain a sample group of secondary schools. The questionnaires were sent to teachers at secondary schools in 20 provinces in Northeast Thailand that were employing the smart learning project for their teaching and learning. Data analysis was performed using descriptive statistics, exploratory factor analysis, and confirmatory factor analysis. The results revealed that learning support technologies, teachers, and learners were the three most important factors influencing the development of DLE (x =4.64, x =4.61, and x =4.49, respectively). The findings of this study have implications for educators, administrators, and teachers to review and discover appropriate ways to invest the necessary conditions that can enhance the quality of DLEs and improve teaching and learning activities in the digital environment at secondary schools.

In this research, we explore the application of artificial neural networks, specifically the vector-quantized temporal associative memory (VQTAM) and VQTAM coupled with locally linear embedding (VQTAM-LLE) techniques, for simulating 2-D magnetotelluric forward modeling. The study introduces the concepts of VQTAM and VQTAM-LLE in the context of simulating 2-D magnetotelluric responses, outlining their underlying principles. We rigorously evaluate the accuracy and efficiency of both VQTAM variants through extensive numerical experiments conducted on diverse benchmark resistivity and real-terrain models. The results demonstrate the remarkable capability of VQTAM and VQTAM-LLE in accurately and efficiently predicting apparent resistivity and impedance phases, surpassing the performance of traditional numerical methods. This study underscores the potential of VQTAM and VQTAM-LLE as valuable computational alternatives for simulating magnetotelluric responses, offering a viable choice alongside conventional methods.