The project-based learning (PjBL) paradigm is often considered the most advanced in vocational education. The increasing use of the PjBL paradigm in vocational education is an intriguing topic of study. In line with the rapid growth of information technology, it enables PjBL in vocational education to help students develop problem-solving, critical thinking, and teamwork skills. In this study, a bibliometric method is used to provide insight into the structure of the subject, social networks, research trends, and issues reflecting project-based learning in vocational education. On November 27, 2022, the Scopus database was searched using project-based learning terms in the title. The second search field appears in the title, abstract, and keywords vocational education or TVET, restricted to journal articles or proceedings and in English to keep them current. This analysis revealed 60 articles in Scopus-indexed journals and proceedings between 2010 and 2022. Dwi Agus Sudjimat from Malang State University, Indonesia, was the most prolific author, having authored four articles on the subject. Indonesia is the nation investing the most in developing PjBL models. According to the thematic data, project-based learning is located in the first quadrant, has high centrality and density, and has well-developed questions related to the study topic. The results of this study show that the project-based learning model that is evolving in vocational education is likely to continue to be an important teaching approach in this field.

Predicting College placements based on academic performance is critical to supporting educational institutions and students in making informed decisions about future career paths. The present research investigates the use of Machine Learning (ML) algorithms to predict college students' placements using academic performance data. The study makes use of a dataset that includes a variety of academic markers, such as grades, test scores, and extracurricular activities, obtained from a varied sample of college students. To create predictive models, the study analyses numerous ML algorithms, including Logistic Regression, Gaussian Naive Bayes, Random Forest, Support Vector Machine, and K-Nearest Neighbour. The predictive models are evaluated using performance criteria such as accuracy, precision, recall, and F1-score. The most effective machine learning method for forecasting students' placements based on academic achievement is identified through a comparative study. The findings show that Random Forest approaches have the potential to effectively forecast college student placements. The findings show that academic factors such as grades and test scores have a considerable impact on prediction accuracy. The findings of this study could be beneficial to educational institutions, students, and career counsellors.

Technology has changed the way we teach and the way we learn. Many learning theories can be used to apply and integrate this technology more effectively. There is a close relationship between technology and constructivism, the implementation of each one benefiting the other. Constructivism states that learning takes place in contexts, while technology refers to the designs and environments that engage learners. Recent efforts to integrate technology in the classroom have been within the context of a constructivist framework. The purpose of this paper is to examine the definition of constructivism, incorporating technology into the classroom, successful technology integration into the classroom, factors contributing to teachers’ use of technology, role of technology in a constructivist classroom, teacher’s use of learning theories to enable more effective use of technology, learning with technology: constructivist perspective, and constructivism as a framework for educational technology. This paper explains whether technology by itself can make the education process more effective or if technology needs an appropriate instructional theory to indicate its positive effect on the learner.

Data mining is now commonly applied in the real estate market. Data mining's ability to extract relevant knowledge from raw data makes it very useful to predict house prices, key housing attributes, and many more. Research has stated that the fluctuations in house prices are often a concern for house owners and the real estate market. A survey of literature is carried out to analyze the relevant attributes and the most efficient models to forecast the house prices. The findings of this analysis verified the use of the Artificial Neural Network, Support Vector Regression and XGBoost as the most efficient models compared to others. Moreover, our findings also suggest that locational attributes and structural attributes are prominent factors in predicting house prices. This study will be of tremendous benefit, especially to housing developers and researchers, to ascertain the most significant attributes to determine house prices and to acknowledge the best machine learning model to be used to conduct a study in this field.

Large Language Models (LLMs) have received significant attention due to their potential to transform the field of education and assessment through the provision of automated responses to a diverse range of inquiries. The objective of this research is to examine the efficacy of three LLMs - ChatGPT, BingChat, and Bard - in relation to their performance on the Vietnamese High School Biology Examination dataset. This dataset consists of a wide range of biology questions that vary in difficulty and context. By conducting a thorough analysis, we are able to reveal the merits and drawbacks of each LLM, thereby providing valuable insights for their successful incorporation into educational platforms. This study examines the proficiency of LLMs in various levels of questioning, namely Knowledge, Comprehension, Application, and High Application. The findings of the study reveal complex and subtle patterns in performance. The versatility of ChatGPT is evident as it showcases potential across multiple levels. Nevertheless, it encounters difficulties in maintaining consistency and effectively addressing complex application queries. BingChat and Bard demonstrate strong performance in tasks related to factual recall, comprehension, and interpretation, indicating their effectiveness in facilitating fundamental learning. Additional investigation encompasses educational environments. The analysis indicates that the utilization of BingChat and Bard has the potential to augment factual and comprehension learning experiences. However, it is crucial to acknowledge the indispensable significance of human expertise in tackling complex application inquiries. The research conducted emphasizes the importance of adopting a well-rounded approach to the integration of LLMs, taking into account their capabilities while also recognizing their limitations. The refinement of LLM capabilities and the resolution of challenges in addressing advanced application scenarios can be achieved through collaboration among educators, developers, and AI researchers.

Motivation has been called the “neglected heart” of language teaching. As teachers, we often forget that all of our learning activities are filtered through our students’ motivation. In this sense, students control the flow of the classroom. Without student motivation, there is no pulse, there is no life in the class. When we learn to incorporate direct approaches to generating student motivation in our teaching, we will become happier and more successful teachers. This paper is an attempt to look at EFL learners’ motivation in learning a foreign language from a theoretical approach. It includes a definition of the concept, the importance of motivation, specific approaches for generating motivation, difference between integrative and instrumental motivation, difference between intrinsic and extrinsic motivation, factors influencing motivation, and adopting motivational teaching practice.

Due to the COVID-19 situation, all activities, including education, were shifted to online platforms. Consequently, instructors encountered increased challenges in evaluating students. In traditional assessment methods, instructors often face ambiguous cases when evaluating students’ competencies. Recent research has focused on the effectiveness of fuzzy logic in assessing students’ competencies, considering the presence of uncertain factors or multiple variables. Additionally, demographic characteristics, which can potentially influence students’ performance, are not typically utilized as inputs in the fuzzy logic method. Therefore, analyzing students’ performance by incorporating these factors is crucial in suggesting adjustments to teaching and learning strategies. In this study, we employ a combination of fuzzy logic and hierarchical linear regression to analyze students’ performance. The experiment involved 318 students from various programs and showed that the hybrid approach assessed students’ performance with greater nuance and adaptability when compared to a traditional method. Moreover, the findings in this study revealed the following: 1) There are differences in students’ performance between traditional and fuzzy evaluation methods; 2) The learning method is an impact on students’ fuzzy grades; 3) Students studying online do not perform better than those studying onsite. These findings suggest that instructors and educators should explore effective strategies being fair and suitable in assessment and learning.

With the rapid and constant changes in computer and information technology, the content and learning methods in Computer Science related courses need to be continuously adapted and consistently aligned with the latest developments in the field. This paper proposes a learning approach called the Gallery-walk integrated Project-Based Learning (G-PBL) which can develop students’ lifelong learning skills that are extremely crucial for Computer Science students. The G-PBL was designed by incorporating the advantages of Project-Based Learning (PBL) and gallery walk learning strategy. In contrast to traditional PBL where students may present their project work to instructors only, students have to present their project work to their classmates as part of the G-PBL approach. All students are required to evaluate their peers’ project work and then give feedback and suggestions. For the research experiments, the G-PBL was implemented as an instructional approach in two Computer Science related courses. This study focuses on exploring the differences in knowledge gain, learning motivation, and perceived usefulness when learning by using the teacher-centered and G-PBL approach. Moreover, the impact of gender differences on learning outcomes is also investigated. The results reveal that using the G-PBL approach helps students to gain more knowledge significantly, for both male and female students. In terms of motivation, female students are more favorable toward the G-PBL approach. On the contrary, male students prefer learning via a teacher-centered approach. Regarding the perceived usefulness, female students strongly view the G-PBL as a highly effective learning approach, whereas male students are more prone to concur that the teacher-centered approach is a more effective learning method.

This article addresses the need for a comprehensive understanding of the rapidly evolving field of Artificial Intelligence (AI) in education, given its potential to transform teaching and learning practices. The study analyzed 1,234 articles from the Web of Science database, using bibliometric techniques and topic modeling. Quantitative analyses of publication trends, citation impacts, and collaboration patterns were conducted using the R programming language, and Latent Dirichlet Allocation (LDA) was employed to uncover latent themes and potential research gaps. The study reveals a dramatic growth in research output, with an annual growth rate of 47.9%. China and the United States emerge as dominant contributors, collectively accounting for 38% of publications. Key research themes include AI in language learning, AI ethics and policy, and AI literacy. The findings highlight the need for more inclusive and diverse research efforts to address the unique challenges and opportunities of AI in education in across socioeconomic contexts.

The use of multimedia in teaching and learning leads to higher learning. Multimedia refers to any computer-mediated software or interactive application that integrates text, color, graphical images, animation, audio sound, and full motion video in a single application. Multimedia learning systems offer a potentially venue for improving student understanding about language. Teachers try to find the most effective way to create a better foreign language teaching and learning environment through multimedia technologies. In this paper, the researcher defines multimedia, elaborates the rationale for using multimedia, identifies multimedia learning, mentions principles of multimedia, explains theoretical basis of multimedia English teaching, reviews roles of teachers and learners in multimedia environment, discusses the relationship between multimedia and learning, and states the strength of multimedia English teaching. The review of literature shows that teachers need to make full use of multimedia to create an authentic language teaching and learning environment where students can easily acquire a language naturally and effectively.

Predicting College placements based on academic performance is critical to supporting educational institutions and students in making informed decisions about future career paths. The present research investigates the use of Machine Learning (ML) algorithms to predict college students' placements using academic performance data. The study makes use of a dataset that includes a variety of academic markers, such as grades, test scores, and extracurricular activities, obtained from a varied sample of college students. To create predictive models, the study analyses numerous ML algorithms, including Logistic Regression, Gaussian Naive Bayes, Random Forest, Support Vector Machine, and K-Nearest Neighbour. The predictive models are evaluated using performance criteria such as accuracy, precision, recall, and F1-score. The most effective machine learning method for forecasting students' placements based on academic achievement is identified through a comparative study. The findings show that Random Forest approaches have the potential to effectively forecast college student placements. The findings show that academic factors such as grades and test scores have a considerable impact on prediction accuracy. The findings of this study could be beneficial to educational institutions, students, and career counsellors.

The project-based learning (PjBL) paradigm is often considered the most advanced in vocational education. The increasing use of the PjBL paradigm in vocational education is an intriguing topic of study. In line with the rapid growth of information technology, it enables PjBL in vocational education to help students develop problem-solving, critical thinking, and teamwork skills. In this study, a bibliometric method is used to provide insight into the structure of the subject, social networks, research trends, and issues reflecting project-based learning in vocational education. On November 27, 2022, the Scopus database was searched using project-based learning terms in the title. The second search field appears in the title, abstract, and keywords vocational education or TVET, restricted to journal articles or proceedings and in English to keep them current. This analysis revealed 60 articles in Scopus-indexed journals and proceedings between 2010 and 2022. Dwi Agus Sudjimat from Malang State University, Indonesia, was the most prolific author, having authored four articles on the subject. Indonesia is the nation investing the most in developing PjBL models. According to the thematic data, project-based learning is located in the first quadrant, has high centrality and density, and has well-developed questions related to the study topic. The results of this study show that the project-based learning model that is evolving in vocational education is likely to continue to be an important teaching approach in this field.

There appears to be a tendency for the strategies and methods that have been offered in OOP course learning to affect the improvement of individual skills only. There is a significant need for learning strategies which are relevant and able of improving collaborative working skills. The purpose of this study is to develop a Collaborative Learning and Programming model suitable for Object-Oriented Programming courses and assess its validity, practicality, and effectiveness. The implementation of the CLP model was conducted using the ADDIE development procedure by involving 7 experts, 35 experimental class students, 23 control class students and 4 lecturers of the Object-Oriented Programming course. The survey results showed that the CLP model was valid, practical, and effective in achieving these goals. The validity test results were verified based on experts' assessment, indicating that the aspects contained in the CLP model were valid with an Aiken's value V =0.89. The practicality test results indicated that the model was highly practical with the practicality value of 89.95% from students and 89.67% from lecturers. Finally, using the CLP model demonstrated its effectiveness in reducing the abstraction and complexity of OOP courses and improving student collaboration, particularly in programming tasks. The significance of conducting this survey is that it provides evidence for the effectiveness of the CLP model in achieving its intended goals and can inform the development of future OOP courses and programming tasks. The survey was conducted well, as it used both expert assessment and student and lecturer feedback to assess the validity, practicality, and effectiveness of the CLP model.

With the rapid and constant changes in computer and information technology, the content and learning methods in Computer Science related courses need to be continuously adapted and consistently aligned with the latest developments in the field. This paper proposes a learning approach called the Gallery-walk integrated Project-Based Learning (G-PBL) which can develop students’ lifelong learning skills that are extremely crucial for Computer Science students. The G-PBL was designed by incorporating the advantages of Project-Based Learning (PBL) and gallery walk learning strategy. In contrast to traditional PBL where students may present their project work to instructors only, students have to present their project work to their classmates as part of the G-PBL approach. All students are required to evaluate their peers’ project work and then give feedback and suggestions. For the research experiments, the G-PBL was implemented as an instructional approach in two Computer Science related courses. This study focuses on exploring the differences in knowledge gain, learning motivation, and perceived usefulness when learning by using the teacher-centered and G-PBL approach. Moreover, the impact of gender differences on learning outcomes is also investigated. The results reveal that using the G-PBL approach helps students to gain more knowledge significantly, for both male and female students. In terms of motivation, female students are more favorable toward the G-PBL approach. On the contrary, male students prefer learning via a teacher-centered approach. Regarding the perceived usefulness, female students strongly view the G-PBL as a highly effective learning approach, whereas male students are more prone to concur that the teacher-centered approach is a more effective learning method.

Due to the COVID-19 situation, all activities, including education, were shifted to online platforms. Consequently, instructors encountered increased challenges in evaluating students. In traditional assessment methods, instructors often face ambiguous cases when evaluating students’ competencies. Recent research has focused on the effectiveness of fuzzy logic in assessing students’ competencies, considering the presence of uncertain factors or multiple variables. Additionally, demographic characteristics, which can potentially influence students’ performance, are not typically utilized as inputs in the fuzzy logic method. Therefore, analyzing students’ performance by incorporating these factors is crucial in suggesting adjustments to teaching and learning strategies. In this study, we employ a combination of fuzzy logic and hierarchical linear regression to analyze students’ performance. The experiment involved 318 students from various programs and showed that the hybrid approach assessed students’ performance with greater nuance and adaptability when compared to a traditional method. Moreover, the findings in this study revealed the following: 1) There are differences in students’ performance between traditional and fuzzy evaluation methods; 2) The learning method is an impact on students’ fuzzy grades; 3) Students studying online do not perform better than those studying onsite. These findings suggest that instructors and educators should explore effective strategies being fair and suitable in assessment and learning.

Data mining is now commonly applied in the real estate market. Data mining's ability to extract relevant knowledge from raw data makes it very useful to predict house prices, key housing attributes, and many more. Research has stated that the fluctuations in house prices are often a concern for house owners and the real estate market. A survey of literature is carried out to analyze the relevant attributes and the most efficient models to forecast the house prices. The findings of this analysis verified the use of the Artificial Neural Network, Support Vector Regression and XGBoost as the most efficient models compared to others. Moreover, our findings also suggest that locational attributes and structural attributes are prominent factors in predicting house prices. This study will be of tremendous benefit, especially to housing developers and researchers, to ascertain the most significant attributes to determine house prices and to acknowledge the best machine learning model to be used to conduct a study in this field.

Entrepreneurship is the key driver of economic progress in many countries; thus, many countries have introduced policies to promote a more entrepreneurial environment. This study assesses the impact of factors affecting entrepreneurial intention of university students. The data was collected through a survey of 341 students at 09 leading universities in Hanoi, Vietnam and analyzed using structural equation modeling (SEM) with SPSS and Amos software. The research results show that entrepreneurial skills, entrepreneurial environment and subjective norms either directly or indirectly affect business motivation and entrepreneurial intention of university students. Thus, it is suggested that university and other educational institutions should provide more activities and taught courses that help students acquire the knowledge and skills necessary for entrepreneurship.

Private tutoring was a non-formal education, it was used as an alternative by parents to help support and maximize the learning process that students get at school. Sometimes parents have difficulty in adjusting the desired and needed criteria with available alternatives or teachers. To overcome these obstacles, this research used the MADM approach in providing alternative recommendations, based on the criteria used as the basis for decision making. MADM consists of SAW, WP, TOPSIS, and AHP. The advantages of the SAW, WP, and TOPSIS methods in managing cost and benefit data were used in the ranking process. While the weaknesses of the three methods in the weighting process can be overcome by the AHP method, which was able to provide more objective weighting results. Therefore, this research aimed to analyze the comparison of the combination of AHP-SAW, AHP-WP, and AHP-TOPSIS methods in the selection of private tutors. The combination of these methods was compared based on accuracy, ranking, and preference to get the best combination of MADM methods in determining the selection of private tutors. The criteria used in this research were education, experience, cost, duration, rating, and distance. The comparison of the three combinations of methods showed the AHP-SAW method has an accuracy rate of 88.14%, AHP-WP of 68.64%, and AHP-TOPSIS of 66.95%. The average ranking showed the AHP-SAW method gave results of 91%, AHP-WP of 88%, and AHP-TOPSIS of 89%. In addition, the average preference showed the AHP-SAW method gave a value of 0.771, AHP-WP of 0.073, and AHP-TOPSIS of 0.564. Thus, it showed the AHP-SAW gave better results in the case of private tutor selection than the AHP-WP and AHP-TOPSIS.

There is a growing interest in integrating active learning and computer-based approaches in the teaching and learning of mathematics in elementary schools. In this study, we introduce Digital Game-Based Learning (DGBL) of Mathematics targeting students in the 5th and 6th grades following a design-to-implementation strategy. We first developed an edutainment Mathematics game and then tested it with 196 pupils from 9 public elementary schools in Morocco. The rationale of the study is to probe the effect of DGBL in lessening pupils’ mathematical anxiety and improving classroom experience.
Students in our study were more engaged and less anxious towards learning Mathematics. Our designed pedagogical edutainment game made students more comfortable when dealing with numerical arithmetic assignments. The study suggests that edutainment games lead to positive individual attitudes towards mathematics and to a better math classroom experience, thus more effective teaching and learning of mathematics.

Assessing pre-service teachers’ digital literacy is challenging, particularly in inclusive education. Reliable and valid testing instruments are required to measure the digital literacy pre-service teachers possess in inclusive education. The entire research process comprises three phases. The first stage was to develop the assessment instrument, the second stage was to validate its content validity, and a pilot study was then conducted to test the reliability and construct validity of the instrument. The results of this study showed that item-level and scale-level content validity scores were both 1.0. The Kaiser-Meyer-Olkin is equal to 0.865. Five factors were extracted, explaining 54.40% of the total variance. The model fits were also all satisfactory. Standardized factor loadings of the instrument’ s 28 items were above 0.5. The values of Cronbach’s are higher than 0.7 for the five factors and the whole instrument. It can be summarized that the instrument had good reliability and validity and can be used to assess the digital literacy of pre-service teachers in inclusive education. There has been research into developing tools to evaluate the digital literacy of pre-service teachers. Still, few studies have addressed pre-service teachers of inclusive education, and this study fills this research gap. The subsequent phase involves evaluating it using a more extensive sample.