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IntroductionIn the rapidly evolving digital landscape, the imperative for success has increasingly spotlighted the essential need for students to develop critical thinking and reading skills as key competencies for the 21st century. This study sought to explore the potential of constructivism to enhance learners’ critical thinking and reading abilities, alongside evaluating their receptiveness to this pedagogical approach.MethodsA mixed-methods research design was employed, combining a quasi-experimental framework with semi-structured interviews. The quasi-experimental component compared the effectiveness of constructivist and traditional teacher-centered methods in promoting critical thinking skills. Semi-structured interviews were conducted to gauge participants’ satisfaction with the constructivist approach and to gather qualitative data on their experiences.ResultsThe investigation yielded two principal findings. First, the constructivist approach significantly outperformed traditional teacher-centered methods in promoting critical thinking skills among students. Second, the majority of participants reported a high level of satisfaction with the constructivist method, attributing this to the approach’s facilitation of deeper knowledge acquisition and increased opportunities for interaction with peers and instructors.DiscussionTheoretical implications of this study underscore the value of constructivism in fostering an engaging and interactive learning environment conducive to the development of critical analytical skills. Practically, the findings advocate for educational practitioners and institutional leaders to consider the integration of constructivist strategies in reading sessions to bolster students’ critical thinking and reading competencies. Moreover, it emphasizes the necessity for educators to receive comprehensive training on the nuances of constructivist pedagogy to ensure its effective implementation. This study thus contributes to the pedagogical discourse by affirming the efficacy of constructivism in enhancing critical cognitive skills and by outlining a pathway for its successful adoption in educational settings.

Educational reforms in many Asian countries, particularly in China and Vietnam, have highlighted the pivotal role of the constructivist approach. However, the implementation of constructivism into the classroom is still very challenging for teachers. Although there is a wealth of research on constructivism, few studies have focused on the difficulties teachers often encounter when organizing constructivist lessons, especially in the EFL context. To address this gap, this study employed a mixed-methods approach, including triangulated data (a questionnaire, classroom observations, and semi-structured interviews) with the participation of 120 EFL teachers in a private university in Vietnam. The findings indicate that teachers faced a wide array of problems in their constructivist classes, including conceptual, pedagogical, cultural, political, and technological dilemmas. Furthermore, they did not comprehend constructivism as they believed, probably due to a superficial understanding of this approach or the effect of Confucian culture. Additionally, this study verifies and contributes to the framework of Windschitl regarding the four dilemmas teachers often confront in constructivist classrooms. Theoretical and practical implications for future researchers as well as for educational stakeholders and institutional leaders on what should be taken into careful consideration prior to the incorporation of constructivism, were also discussed in detail.

Abstract Mining industrial areas with anthropogenic engineering structures are one of the most distinctive features of the real world. 3D models of the real world have been increasingly popular with numerous applications, such as digital twins and smart factory management. In this study, 3D models of mining engineering structures were built based on the CityGML standard. For collecting spatial data, the two most popular geospatial technologies, namely UAV-SfM and TLS were employed. The accuracy of the UAV survey was at the centimeter level, and it satisfied the absolute positional accuracy requirement of creating all levels of detail (LoD) according to the CityGML standard. Therefore, the UAV-SfM point cloud dataset was used to build LoD 2 models. In addition, the comparison between the UAV-SfM and TLS sub-clouds of facades and roofs indicates that the UAV-SfM and TLS point clouds of these objects are highly consistent, therefore, point clouds with a higher level of detail and accuracy provided by the integration of UAV-SfM and TLS were used to build LoD 3 models. The resulting 3D CityGML models include 39 buildings at LoD 2, and two mine shafts with hoistrooms, headframes, and sheave wheels at LoD 3.

Abstract Achieving a sufficient IELTS band score for academic purposes has been a major goal of many L2 learners around the world, especially those in Asia. However, IELTS writing scores were consistently reported to be the lowest when compared to the scores in speaking, reading, and listening. Despite a growing body of research in IELTS writing, little focused on the role of model essays and noticing hypotheses. The present study aimed to fill in this gap by examining whether or not the implementation of both noticing hypothesis and model essays had a discernible influence on learners’ IELTS task 2 writing. To reach this goal, a quasi-experimental design including a pretest and a posttest was conducted with the voluntary participation of 52 undergraduates. These participants were divided into two groups: control group (CG, n = 25), learning in the conventional method (peer feedback and teacher feedback), and experimental group (EG, n = 27), using the noticing-model essays method. Following this, semi-structured interviews were performed to gain insights into the quantitative data. The results from this mixed-methods approach showed that there were significant gains in the overall performance and in the lexical resources subscale in the EG while no considerable changes were observed in the CG. Additionally, the other subscales (task response, grammatical range and accuracy, and cohesion-coherence) did not witness any significant differences between the two groups. Several pedagogical implications and recommendations for future research, especially in the Asian context, were also discussed.

Online education has become more prevalent in the 21st century, especially after the COVID-19 pandemic. One of the major trends is the learning via Massive Open Online Courses (MOOCs), which is increasingly present at many universities around the world these days. In these courses, learners interact with the pre-designed materials and study everything mostly by themselves. Therefore, gaining insights into their satisfaction of such courses is vitally important to improve their learning experiences and performances. However, previous studies primarily focused on factors that affected learners’ satisfaction, not on how and what the satisfaction was. Moreover, past research mainly employed the narrative reviews posted on MOOC platforms; very few utilized survey and interview data obtained directly from MOOC users. The present study aims to fill in such gaps by employing a mixed-methods approach including a survey design and semi-structured interviews with the participation of 120 students, who were taking academic writing courses on Coursera (one of the world-leading MOOC platforms), at a private university in Vietnam. Results from both quantitative and qualitative data showed that the overall satisfaction of courses on Coursera was relatively low. Furthermore, most learners were not satisfied with their learning experience on the platform, primarily due to inappropriate assessment, lack of support, and interaction with teachers as well as improper plagiarism check. In addition, there were moderate correlations between students’ satisfaction and their perceived usefulness of Coursera courses. Pedagogically, teachers’ feedback and grading, faster support from course designers as well as easier-to-use plagiarism checking tools are needed to secure learners’ satisfaction of MOOCs.

In the twenty-first century, with the evolution of technologies, teaching and learning practices, especially in second language acquisition, have taken a significant leap. Numerous applications and software have been created to facilitate and boost learners’ L2 vocabulary. The goal of this study is to examine the effectiveness of Quizlet in enhancing lexical retention of EFL students at a private institution in Vietnam in two learning modes: at home and in class. Eighty-nine university students participated in the study and were separated into three groups. While the in-class group (n=31) had weekly sessions of vocabulary revision in class with their teacher, the at-home group (n=30) did the weekly reviews on Quizlet at home by themselves. The control group (n=28), however, did not utilize Quizlet for vocabulary review either in class or at home. During four weeks, the three groups learned and practiced 32 target words, divided into four sets (1 set=8 items), with each set utilized in one week. Via a quasi-experimental design including a pretest and a posttest, the quantitative data were analyzed with the employment of the Generalized Linear Mixed Model to generate reliable outcomes. Semistructured interviews were conducted after the posttest to gain insights into learners’ opinions on Quizlet use. The results indicated that Quizlet was effective in boosting lexical gains in second language acquisition and that most participants were interested in interacting with this tool for reviewing vocabulary. However, there was no statistically significant difference in lexical gains between the at-home and in-class groups. Pedagogical implications, as well as recommendations for future research, were also discussed.

Aerosol optical depth (AOD) can be retrieved accurately with sequential ground-based measurements of direct and diffuse solar radiance. However, spatial coverage and location frequency cause certain limitations. Hence, satellite image data are a proper tool for obtaining aerosol optical depth products with more spatial information and patterns of aerosol distribution. Currently, aerosol remote sensing may enhance our understanding of the optimal approach to AOD retrieval over urban and rural areas, and how it differs due to the characteristics of surface reflectivity. The article deals with the concepts of contrast reduction, and dark target approaches are examined using Landsat imaging and the observation of a sun photometer for integrating aerosol optical depth distribution over the city of Taipei in Taiwan. For areas with bright surfaces, such as urban areas, the above concepts were applied using the dispersion coefficient method with a sun photometer, in order to reduce errors considerably in the product. In contrast, a dark target algorithm with a relationship of surface reflectance between the blue (0.49 μm), red (0.66 μm), and infrared (2.1 μm) spectral bands is suitable for moist soils and vegetation areas. The retrieval of AOD spatial distribution is compared with MODIS AOD products and AERONET to verify the accuracy of the results. The RMSE ranged from 0.2 to 0.4, and about 50% of the data were within expected error margins (EE=± (0.05+0.15 AODsunphotometer).

This study conducted a comparative analysis of critical reading skills among engineering students at private universities in Malaysia and Vietnam, using the Programme for International Student Assessment (PISA) reading framework. Involving 182 undergraduates, the research included a quantitative critical reading test followed by statistical analysis (T-tests) to examine score variability among first-year and final-year students. The analysis revealed significant performance differences among first-year students but not among final-year students from both countries. To complement the quantitative data, semi-structured interviews with 16 participants across different critical reading levels were conducted. These interviews aimed to capture students' perspectives on the PISA test, highlighting challenges such as the Western orientation of texts, English vocabulary, and the format of open-ended questions. Integrating quantitative and qualitative methods, the study offers an in-depth understanding of the critical reading abilities of engineering students in Malaysia and Vietnam, underscoring the importance of considering cultural and linguistic factors in educational assessments.

Purpose. Mining-induced land subsidence is a significant concern in areas with extensive underground mining activities. Therefore, the prediction of land subsidence is crucial for effective land management and infrastructure planning. This research applies an artificial neural network (ANN) to predict land subsidence over the Mong Duong underground coal mine in Quang Ninh, Vietnam. Methods. In the ANN model proposed in this research, four features are used as the model inputs to predict land subsidence, i.e., model outputs. These features include the positions of ground points in the direction of the trough main crosssection, the distance from the chamber (goaf) center to the ground monitoring points, the accumulated exploitation volume of extraction space, and the measured/recorded time. The entire dataset of 12 measured epochs, covering 22 months with a 2-month repetition time period, is divided into the training set for the first 9 measured epochs and the test set for the last 3 measured epochs. k-fold cross-validation is first applied to the training set to determine the best model hyperparameters, which are then adopted to predict land subsidence in the test set. Findings. The best model hyperparameters are found to be 5 hidden layers, 64 hidden nodes and 240 iterated epochs. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of the predicted land subsidence depend on the time separated between the last measured epoch and the predicted epoch. Within 2 months from the last measurements, RMSE and MAE are at 22 and 13 mm for Epoch 10, which increase to 31 and 20 mm for Epoch 11 (4 months from the last measurement) and 37 and 24 mm for Epoch 12 (6 months from the last measurement). Originality. A new ANN model with associated "optimal" hyperparameters to predict underground mining-induced land subsidence is proposed in this research. Practical implications. The ANN model proposed in this research is a good and convenient tool for estimating mininginduced land subsidence, which can be applied to underground mines in Quang Ninh province, Vietnam. [ABSTRACT FROM AUTHOR]

Mining-induced road subsidence is a significant concern in areas with extensive underground mining activities. Therefore, the prediction of road subsidence is crucial for effective land management and infrastructure planning. This paper applies an artificial neural network (ANN) to predict road subsidence caused by underground mining activities in Vietnam. The ANN model proposed in this study is adopted relying on the recursive multistep prediction process, in which the predicted value in the previous step is appended to the time series to predict the next value. The entire dataset of 12 measured epochs covering 12 months with a 1-month repeat time is divided into the training set by the first 9 measured epochs and the test set by the last 3 measured epochs. K-fold cross validation is first applied to the training set to determine the best model’s hyperparameters, which are then adopted to predict land subsidence of the test set. Absolute errors of the predicted road subsidence depend on the separated time between the last measured epoch and the predicted epoch. Those errors at the 10th month of the three tested points are 3.0%, 0.1 %, and 0.1%, which increase to 4. 8%, 3.3%, and 1.5% at the 11th month, and 7.2%, 2.5% and 1.3% at the 12th month. The absolute errors are found to be small, which were all ranged with 0.5 mm and demonstrates that the proposed method utilizing ANN in this study can produce good prediction for road subsidence time series at mining areas. [ABSTRACT FROM AUTHOR]

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