One of the ways to enhance and improve the quality of learning delivery is through the use of technology, particularly the Internet, which facilitates faster and easier access to information. This research aims to explore the degree to which factors such as digital literacy, metacognitive awareness, meaningful learning, habits of using smartphones, and personal learning competence are related to one another. Both the relationship between metacognitive awareness and personal learning competence, as well as the relationship between smartphone habits and personal learning competence, are moderated by a moderating variable known as the fear of missing out. Fear of missing out is a moderating variable. Structural equation modeling, specifically partial least squares, was employed to analyze data from 597 engineering students. SmartPLS version 4 was the tool used for this analysis. The study found that the moderating variable, fear of missing out, significantly impacts metacognitive awareness, learning personal competence, and smartphone habits, making it a crucial factor to investigate. This result is significant because it is a variable that influences the learning that students go through for their education and because it is an extremely important thing to investigate.

In an era where diversity and digitalization significantly influence higher education, understanding and adapting to various learning preferences is crucial. This study comprehensively analyzes 394 scholarly articles from 1984 to 2022 using bibliometric methods, providing a dynamic overview of the research patterns in learning styles within higher education. We identified four stages of development during this period: 1984–1995 (Low-interest), 1996–2005 (Early development), 2006–2018 (Development), and 2019–2022 (Intensification). Our analysis highlights that the United States, the United Kingdom, and Australia were the top three leading publishers of research on learning styles in higher education. The results reveal three main topics of publications: educational technology, learning environments, and subject behaviors. This research not only identifies emerging research topics but also underscores the importance of adapting instructional strategies to diverse learning styles to enhance educational outcomes in higher education.

Successfully solving reality-based tasks requires both mathematical and text comprehension skills. Previous research has shown that mathematical tasks requiring language proficiency have lower solution rates than those that do not, indicating increased difficulty through textual input. Therefore, it is plausible to assume that a lack of text comprehension skills leads to performance problems. Given that different sociodemographic characteristics and cognitive factors can influence task performance, this study aims to determine whether text comprehension mediates the relationship between these factors and competence in solving reality-based tasks. Additionally, it examines the impact of systematic linguistic variation in texts. Using an experimental design, 428 students completed three reality-based tasks (word count: M = 212.4, SD = 19.7) with different linguistic complexities as part of a paper-pencil test. First, students answered questions about the situation-related text comprehension of each text, followed by a mathematical question to measure their competence in solving reality-based tasks. The results indicate that: a) Tasks with texts of lower linguistic complexity have a significantly higher solution rate for both text comprehension (d = 0.189) and mathematical tasks (d = 0.119). b) Cognitive factors are significant predictors of mathematical solutions. c) Text comprehension mediates the relationship between the impact of students’ cultural resources and cognitive factors and their competence to solve reality-based tasks. These findings highlight the importance of linguistic complexity for mathematical outcomes and underscore the need to reinforce text comprehension practice in mathematical education owing to its mediating role.

This study aimed to optimize critical thinking by empowering reflective and impulsive students' collaboration, communication, and information literacy skills through information literacy-oriented e-books in STEM-integrated problem-based learning (PBL). The research method used was a descriptive explorative approach. The study subjects consisted of five reflective students and five impulsive students. The measurement of cognitive style used the Matching Familiar Figure Test (MFFT) instrument. Collaboration skills were assessed through observation sheets, critical thinking and communication skills were assessed through student worksheets based on problem-solving tasks, and information literacy was assessed through a questionnaire. The study found that reflective students excelled in critical thinking and information literacy, while impulsive students demonstrated superior collaboration skills. As for communication skills, reflective and impulsive students have different advantages for each indicator of communication skills. This study can conclude that implementing information literacy-oriented e-books through STEM-integrated PBL can optimize reflective and impulsive students' critical thinking, collaboration, communication, and information literacy skills. The implication of this study is the importance of integrating 21st century skills holistically in learning practices, especially in the digital era, to prepare the younger generation to face the challenges of the 21st century.

The education of gifted students is often characterized by high expectations, ambitious goals, and significant effort invested in learning. Their experiences of success and failure are shaped by a variety of factors, including personal, family, school, cultural, and social influences. This article examines how gifted students perceive and experience their own successes and failures, as well as how these experiences are perceived and responded to by their peers. Using qualitative methods, the study involved semi-structured interviews with thirty gifted students from seventh to ninth grades across ten elementary schools in Slovenia. The findings indicate that gifted students experience a range of emotions in response to success, from satisfaction to anxiety, while their reactions to failure often involve frustration and self-criticism. Peer responses to their success and failure vary significantly, ranging from supportive encouragement to jealousy and social exclusion. These findings highlight the complex interpersonal dynamics at play within school environments. Understanding and addressing these dynamics is crucial for creating inclusive, supportive, and stimulating learning environments that nurture both the academic and social-emotional well-being of gifted students.

The integration of AI tools in education is reshaping how students view and interact with their learning experiences. As AI usage continues to grow, it becomes increasingly important to understand how students' perceptions of AI technology impact their academic performance and learning behaviours. To investigate these effects, we conducted a correlational study with a sample of 44 students to examine the relationship between students' perceptions of ChatGPT’s utility—focusing on usage frequency, perceived usefulness, accuracy, reliability, and time efficiency—and key academic outcomes, including content mastery, confidence in knowledge, and grade improvement. Additionally, we explored how these perceptions influence student behaviours, such as reliance on ChatGPT, procrastination tendencies, and the potential risk of plagiarism. The canonical correlation analysis revealed a statistically significant relationship between students' perceptions of ChatGPT's utility and their academic outcomes. Students who viewed ChatGPT as reliable and efficient tended to report higher grades, improved understanding of the material, and greater confidence in their knowledge. Furthermore, the bivariate correlation analysis revealed a significant relationship between dependency on ChatGPT and procrastination (r = 0.546, p < .001), indicating that a higher reliance on AI tools may contribute to increased procrastination. No statistically significant association was identified between ChatGPT dependency and the risk of plagiarism. Future research should prioritize the development of strategies that promote the effective use of AI while minimizing the risk of over-reliance. Such efforts can enhance academic integrity and support independent learning. Educators play a critical role in this process by guiding students to balance the advantages of AI with the cultivation of critical thinking skills and adherence to ethical academic practices.

This study aims to develop and validate a green behavior instrument based on local potential using structural equation modeling (SEM) with smart partial least squares (SmartPLS). The instrument consists of 40 statements covering five main indicators: environmental maintenance, waste reduction, saving natural resources, sustainable mobility and consumption, and community education. This study addresses a gap in existing research by creating a context-specific tool for assessing green behavior, incorporating local cultural and ecological factors. While prior studies emphasize global sustainability principles, they often overlook the significance of local practices and values, which are essential for effective environmental education. By integrating local potential, this instrument bridges global sustainability goals with regional contexts, enabling meaningful and practical student engagement. The instrument was validated through content validity testing, exploratory and confirmatory factor analyses, and construct validity and reliability testing using SEM with SmartPLS. The results indicate strong content validity, with content validity index (CVI) values ranging from .80 to .90. After analysis, 34 valid items were retained from the initial 40. This study contributes to the literature by developing an instrument that aligns with global sustainability goals while integrating local cultural practices and ecological contexts. It offers insights into how local knowledge enhances sustainability education, providing a holistic framework for assessing green behavior across diverse regions.

This study examines the factors influencing English language achievement among non-English major undergraduate students in Thailand, with a specific focus on the differences between high-achieving and low-achieving learners. Conducted at Rajamangala University of Technology Lanna, this research adopts a mixed-methods approach, combining quantitative data from questionnaires and qualitative insights from semi-structured interviews. Three primary influencing factors were identified: student-related factors (e.g., motivation and self-regulated learning), teacher-related factors (e.g., pedagogical practices and teacher-student interactions), and environmental factors (e.g., availability of learning resources). Student motivation and self-regulation emerged as the strongest predictors of success, while teacher-related factors unexpectedly showed a negative influence, suggesting a misalignment between teaching strategies and student needs. Environmental factors, though positively perceived, had a less direct impact on outcomes. Practical implications include enhancing intrinsic motivation, adopting tailored teaching strategies to meet diverse learner needs, and strengthening teacher-student relationships to support low-achieving students. Policymakers are encouraged to address resource disparities and develop targeted interventions to enhance English language proficiency among students.

Work-based learning (WBL) is an important tool for enhancing students' employability skills in vocational education and training. Many studies have underlined the importance of a variable of WBL, self-efficacy, and vocational identity in developing vocational students' employability skills. Nonetheless, the research is limited and examined separately. Therefore, this study investigates how WBL, self-efficacy, and vocational identity influence employability skills and how self-efficacy moderates between WBL and employability skills. Four hundred and three state university students in Yogyakarta were involved in the data collection. This study used structural equation modeling (SEM) analysis to test its hypothesis. The results of the study revealed that the implementation of WBL did not have a direct effect on employability skills; however, self-efficacy was able to moderate the relationship between WBL and employability skills. However, WBL directly influences vocational identity, which in turn directly influences employability skills, while self-efficacy also directly influences employability skills. This research has important implications for improving learning that can improve students' self-efficacy skills in an effort to build students' employability skills in vocational education and training.

This study explores the impact of artificial intelligence (AI) integration on students' educational experiences. It investigates student perceptions of AI across various academic aspects, such as module outlines, learning outcomes, curriculum design, instructional activities, assessments, and feedback mechanisms. It evaluates the impact of AI on students' learning experiences, critical thinking, self-assessment, cognitive development, and academic integrity. This research used a structured survey distributed to 300 students through Microsoft Forms 365, yet the response rate was 29.67%. A structured survey and thematic analysis were employed to gather insights from 89 students. Thematic analysis is a qualitative method for identifying and analysing patterns or themes within data, providing insights into key ideas and trends. The limited response rate may be attributed to learners' cultural backgrounds, as not all students are interested in research or familiar with AI tools. The survey questions are about AI integration in different academic areas. Thematic analysis was used to identify patterns and themes within the data. Benefits such as enhanced critical thinking, timely feedback, and personalised learning experiences are prevalent. AI tools like Turnitin supported academic integrity, and platforms like ChatGPT and Grammarly were particularly valued for their utility in academic tasks. The study acknowledges limitations linked to the small sample size and a focus on undergraduate learners only. The findings suggest that AI can significantly improve educational experiences. AI provides tailored support and promotes ethical practices. This study recommends continued and expanded use of AI technologies in education while addressing potential implementation challenges.

This study addresses global concerns surrounding elementary students' science performance following the COVID-19, as a result of international tests such as Trends in International Mathematics and Science Study (TIMSS) highlight the ongoing challenges that urge the exploration of innovative educational approaches to improve science learning. This research employed gamification-assisted instruction and explored its impact on enhancing the understanding of science concepts and attitudes toward science class among fourth graders. The study adopted a quasi-experimental design and included an experimental group (ExG) that was taught using a gamification strategy and a control group (CoG) that was taught using a traditional method with a sample of 38 female elementary students from a public school in Jordan. Data were gathered using valid and reliable tools: the developed scientific concepts test and the Attitude Towards Science class measures. The ANCOVA analysis revealed that gamification significantly improves the acquisition of scientific concepts (η2=.208) and boosts a positive attitude toward science classes among elementary students (η2=.626). These findings encourage decision-makers to incorporate gamification into science teaching practices and methods.

This research aims to systematically review the integration of artificial intelligence (AI) in English language teaching and learning. It specifically seeks to analyze the current literature to identify how AI could be utilized in English language classrooms, the specific tools and pedagogical approaches employed, and the challenges faced by educators. Using the PRISMA-guided Systematic Literature Review (SLR) methodology, articles were selected from Scopus, Science Direct, and ERIC, and then analyzed thematically with NVivo software. Findings reveal that AI enhances English teaching through tools like grammar checkers, chatbots, and language learning apps, with writing assistance being the most common application (54.55% of studies). Despite its benefits, challenges such as academic dishonesty, over-reliance on AI (27.27% of studies), linguistic issues, and technical problems remain significant. The study emphasizes the need for ethical considerations and teacher training to maximize AI’s potential. It also highlights societal concerns, including the digital divide, underscoring the importance of equitable access to AI-powered education for learners of all socioeconomic backgrounds.

This study aims to evaluate the effectiveness of cooperative learning models in improving critical reading skills. This study uses a meta-analysis study method by analyzing 28 articles extracted from the databases of Scopus, Google Scholar, EBSCO, EmeraldInsight, Science & Direct, SpringerLink, Taylor & Francis, and ProQuest. The meta-analysis allows researchers to combine the results of previous research, providing a more comprehensive picture of how effective a particular approach is in teaching critical reading. The research findings show that cooperative learning models significantly improve essential skills of reading more effectively than traditional ones. This is shown by the effect sizes based on the fixed model, showing the overall standard difference in the mean is 0.784 (95% CI, 0.689 to 0.880) with p-values = 0.00 (<0.05). Using a cooperative learning model, The measure showed positive effect sizes on critical reading learning. Based on these results, it can be concluded that the cooperative learning model effectively improves essential reading skills. However, several factors, such as the quality of the facilitators and the teaching methods, influence the results. The implications of this study show the need for a broader application of cooperative learning models to improve critical reading skills in schools and other educational institutions, with adjustments to the needs and characteristics of students.

The COVID-19 pandemic has significantly changed the global educational landscape, prompting a need to explore emerging literature on home learning, literacy development, and parental involvement. This study aims to contribute to Sustainable Development Goals (SDG) 4: Quality Education, and SDG 10: Reduced Inequalities, by examining these aspects in the context of the pandemic and beyond through a bibliometric analysis. The analysis depicts 416 publications from the Web of Science Database between 2014–2023. The study utilized co-citation and co-word analysis techniques to identify key research clusters and trends related to home learning and literacy development. The analysis revealed that parental involvement can help mitigate learning loss, supporting SDG targets for equitable and inclusive education. Key research clusters identified include the influence of socio-economic status on literacy outcomes, continuity of literacy practices, and the long-term effects of traditional versus digital home learning environments. The findings highlighted a consensus on the importance of a supportive home literacy environment for reading skills and overall academic success. The need for intervention programs targeting low-income groups to ensure equitable access to learning resources, aligning with SDG 10, was also identified through the study. The findings have practical implications for enhancing the home literacy environment, increasing parental involvement, and supporting early literacy interventions, providing valuable insights for education stakeholders, policymakers, and researchers in the post-pandemic era.

Integrating generative artificial intelligence (GenAI) in education has gained significant attention, particularly in flexible learning environments (FLE). This study investigates how students’ voluntary adoption of GenAI influences their perceived usefulness (PU), perceived ease of use (PEU), learning engagement (LE), and student-teacher interaction (STI). This study employed a structural equation modeling (SEM) approach, using data from 480 students across multiple academic levels. The findings confirm that voluntary GenAI adoption significantly enhances PU and PEU, reinforcing established technology acceptance models (TAM). However, PU did not directly impact LE at the latent level—an unexpected finding that underscores students’ engagement’s complex and multidimensional nature in AI-enriched settings. Conversely, PEU positively influenced LE, which in turn significantly predicted STI. These findings suggest that usability, rather than perceived utility alone, drives deeper engagement and interaction in autonomous learning contexts. This research advances existing knowledge of GenAI adoption by proposing a structural model that integrates voluntary use, learner engagement, and teacher presence. Future research should incorporate variables such as digital literacy, self-regulation, and trust and apply longitudinal approaches to better understand the evolving role of GenAI inequitable, human-centered education.

The proliferating field of writing education increasingly intersects with technological innovations, particularly generative artificial intelligence (GenAI) resources. Despite extensive research on automated writing evaluation systems, no empirical investigation has been reported so far on GenAI’s potential in cultivating intermedial writing skills within first language contexts. The present study explored the impact of ChatGPT as a writing assistant on university literature students’ intermedial writing proficiency. Employing a quasi-experimental design with a non-equivalent control group, researchers examined 52 undergraduate students’ essay writings over a 12-week intervention. Participants in the treatment group harnessed the conversational agent for iterative essay refinement, while the reference group followed traditional writing processes. Utilizing a comprehensive four-dimensional assessment rubric, researchers analyzed essays in terms of relevance, integration, specificity, and balance of intermedial references. Quantitative analyses revealed significant improvements in the AI-assisted group, particularly in relevance and insight facets. The findings add to the research on technology-empowered writing learning.

This study investigates how undergraduate art majors at the National University of Art Education in Vietnam perceive the cultural integration into their English curriculum. A quantitative design was employed using a researcher-developed questionnaire administered to 214 students. Data were analysed using descriptive statistics, independent-samples t-tests, and multiple regression. Findings indicated that students valued culturally relevant content, particularly materials connected to both Vietnamese and international art as well as experiential and student-centered instructional strategies. Reported challenges included limited cultural background knowledge, cognitive overload, and reduced confidence when discussing culture in English. Crucially, results from multiple regression revealed that how culture is taught may have a greater impact on students’ experiences than the content itself. Therefore, these findings underscore the importance of aligning instructional approaches with learners’ disciplinary identities and offer implications for culturally responsive curriculum design, professional development, and the implementation of context-specific teaching strategies in English language instruction for art students.