Many education and learning experts currently recommend the flipped classroom model as an alternative to learning after the COVID-19 pandemic. This study aims to explore the impact of the flipped classroom model on social skills and problem-solving skills for cadets. This research used a sequential mix method involving 50 maritime students in semester 7 of the Engineering Study Program at the Maritime Sciences Polytechnic Makassar, South Sulawesi, Indonesia. Researchers used two main instruments, namely problem-solving skill tests and interviews. Furthermore, in the quantitative analysis, the researcher ran paired sample t-tests and one-way Multivariate Analysis of Covariance (MANCOVA) using the SPSS 25.00 program. In addition, researchers also analysed qualitative data from interviews using thematic analysis techniques. The results showed that the flipped classroom model proved to have a positive effect on the problem-solving skills of maritime students. Other findings state that the cadets also respond positively to the flipped classroom model. Researchers recommend that teachers use the flipped classroom model, especially in dealing with learning in the post-pandemic era, like today.

Based on institutional theory, this study explores the causal relationship between teacher professional identity, perceived institutional norms, and teachers’ conformity behavior in colleges and universities in Hunan Province. A questionnaire survey was conducted on 500 teachers from two universities in Hunan Province. The reliability and validity test results and structural equation modeling analysis show that the professional identity of college teachers positively affects their conformity behavior, and that perceived institutional norms partially mediate the relationship between teacher professional identity and teacher conformity behavior. The research enriches the application of institutional theory in university organization and provides an empirical basis for universities to improve teachers’ conformity behavior.

The purpose of this study was to classify the active job behaviors of Generation Z (Gen Z, born after 1995) elementary school teachers and investigate relevant variables that significantly affect such a classification. A total of 375 Gen Z elementary school teachers who passed the National Elementary Teacher Qualification Test and had worked in elementary schools in South Korea participated in this study. The data collected identified the types of active job behaviors among Gen Z elementary school teachers using cross-tabulation through Latent Profile Analysis (LPA). A multinomial logistic regression analysis was conducted to identify the predictors that influence the types of active job behaviors of Gen Z elementary school teachers. The results were as follows: First, there are four types of active job behaviors of Gen Z elementary school teachers: Ideal, relational, non-participatory, and passive job performance types. Second, teacher efficacy, learning agility, organizational commitment, and principals’ transformational leadership influenced the types of active job behaviors of Gen Z elementary school teachers. The results offer insights into the human resource management of Gen Z elementary school teachers and have significant implications for improving the active job behavior of Gen Z elementary school teachers.

The role of artificial intelligence (AI) in education remains incompletely understood, demanding further evaluation and the creation of robust assessment tools. Despite previous attempts to measure AI's impact in education, existing studies have limitations. This research aimed to develop and validate an assessment instrument for gauging AI effects in higher education. Employing various analytical methods, including Exploratory Factor Analysis, Confirmatory Factor Analysis, and Rasch Analysis, the initial 70-item instrument covered seven constructs. Administered to 635 students at Nueva Ecija University of Science and Technology – Gabaldon campus, content validity was assessed using the Lawshe method. After eliminating 19 items through EFA and CFA, Rasch analysis confirmed the construct validity and led to the removal of three more items. The final 48-item instrument, categorized into learning experiences, academic performance, career guidance, motivation, self-reliance, social interactions, and AI dependency, emerged as a valid and reliable tool for assessing AI's impact on higher education, especially among college students.

This research concentrated on observing academic student teachers’ teamwork, especially its effectiveness and their attitudes to collaboration and transformation, as well as their reflective, written self-assessments in relation to teamwork. Teams have been studied widely, but these kinds of special contexts are rare. The target group was higher education teachers, the context was an introduction of a new pedagogical program, and the teams acted under the guidance of mentors and occasional lecturers. Besides the content analysis, the study method was a deep analysis of the essays. The most effective positive influencers proved to be social exchanges, including mentoring, peer support, input for learning and the experiences of wellbeing. Individual distrust of working methods in teams, and emotional suspicions of their self-efficacy in transformational settings emerged as latent factors, revealing mainly the difficulty of changing traditional mindsets. The analyses offered individual and community-wide opportunities to steer education in the future.

This study is designed to illuminate the role of four key constructs—teacher-student relationships, peer networks, satisfaction with support services, and employability trust—in shaping the social capital within universities. Utilising a sample of 1902 working students derived from the Eurostudent VII survey data, this research applies both exploratory and confirmatory factor analysis to validate the proposed model. The findings indicate that all four constructs demonstrate statistically significant and positive associations with university social capital. Crucially, the measures of reliability and validity are within an acceptable range, lending credibility to the findings. The teacher-student relationship was found to be the most influential factor, highlighting its crucial value in the functioning of social capital inside universities. Along with providing a framework for future studies on the ever-changing nature of social capital in universities, the results highlight the significance of cultivating an interconnected academic community, which enriches the educational organisation as a whole.

This pilot study is part of a larger “Decolonization of Digital Learning Spaces” project, which aims to develop research tools for communities that are remote and/or excluded geographically, politically, economically, socially, culturally, and linguistically. The project’s ultimate goal is to work alongside these communities to design their own digital learning tools, networks, and online educational environments by accessing and leveraging their knowledge and skills. Testing the single-criterion card sorting method is the first step toward this goal. Card sorting is an easy, enjoyable, and cost-effective method for data collection and analysis, particularly for researchers working in remote areas with limited access to electricity or the Internet. The pilot explored single-criterion card sorting as a method to elicit knowledge from two diverse cultural and linguistic groups engaged in learning activities within their communities. These groups were from a Deaf and Hard of Hearing (DHH) community in Canada (engaged in a bow-making workshop) and a rural Kabyle community in Algeria (engaged in a traditional cooking lesson). Despite low participant numbers, distinct patterns emerged, indicating the method's effectiveness. The results, though anticipated, were non-random, demonstrating the potential of card sorting in producing patterns indicative of how individuals and/or communities categorize their world(s). Kabyle sortings focused on ingredients, highlighting older individuals as teachers passing along knowledge, while the DHH sortings emphasized face-to-face contact and hand movements in communication. The findings, though modest, established relationships, provided insights into the research context and offered logistical understanding, paving the way for further work with DHH and Kabyle communities towards the design of digital learning spaces.

The process e-portfolio is a type of e-portfolio that helps students construct knowledge and familiarise themselves with their learning process through self-and peer assessment. Lecturers and students experienced difficulties when using the e-portfolio because Mahara 2019 was not updated. This qualitative research study proposes how lecturers should design e-portfolios for learning through formative assessment activities. Interpretivism was the researcher's standpoint, aiming to interpret how the study participants used e-portfolios for teaching and learning through formative assessment activities. This exploratory case study used semi-structured interviews and an e-portfolio checklist for data collection. It explored the use of the e-portfolio for formative assessment through the experiences of ten purposefully sampled lecturers. The findings suggest that an e-portfolio facilitates teaching and learning in open distance e-learning because it enables online delivery of the content and administering of assessments that afford students' learning of the module content through completing formative assessment activities. The e-portfolio facilitates co-teaching and co-learning because students become knowledge creators and active users instead of passive learners. This study recommends the use of process e-portfolios to facilitate assessment and learning in open-distance e-learning institutions.

Ensuring the trustworthiness of qualitative research remains a critical challenge in educational research. However, early career researchers often lack structured guidance on enhancing the credibility of qualitative data analysis. A key issue is the limited discussion on inductive approaches that support systematic theme generation and theory development. To address this gap, this study examines how two early-career researchers employed a three-level inductive methodology during their PhD studies to strengthen the trustworthiness of their findings. Using an autoethnographic approach, the study finds that this methodology deepened their understanding of participants’ experiences, facilitated the emergence of valid themes, and reinforced credibility, transferability, dependability, and confirmability. These findings offer concrete strategies for researchers undertaking similar approaches to ensure trustworthiness in their qualitative inquiry. This study also highlights the importance of equipping PhD researchers in education with strategies to navigate qualitative research rigorously, ultimately enhancing the quality of their studies.

This study delved into Terry Borton’s reflective model and 7E instructional model to produce comprehensive and guided tools that fit as observation and reflective tools for enhancing learners’ engagement and outcomes in Mathematics lessons. The aim was to gather insights that can inform strategies to adapt Borton’s model to produce tools to be used to observe and analyse 7E model-based Mathematics lessons to contribute to improved student outcomes. Literature information was used to explore, analyse, and synthesise the study’s related existing theories and models to provide a deeper understanding of constraints and prompt question descriptors to produce 21st-century tools to observe and reflect on a Mathematics lesson. After comparing different prompt question descriptors from different literature and Borton’s model, concise descriptors were retained for educational purposes to be analysed, considering the 7E model phases, to produce the guided tools. As a result, two products. “Classroom Observation-Guided Tool”, which includes a guided tool table with a last column for the observer to write comments during class. This column is used to identify gaps in student engagement and learning practices across the 7E phases, which may have been misused. The “Post-Lesson Discussion Guided Tool”, to make a positive post-lesson discussion session, enabling teachers to identify areas for improvement in student engagement to achieve better outcomes next time. Other researchers can study the applicability of 21st-century observation and reflection-guided tools to other subjects, exploring their long-term impact on teacher professional development to improve overall student achievement across all school subjects.

Artificial Intelligence (AI) is reshaping education across the Asia-Pacific, yet its integration depends on teachers’ readiness and perspectives. This study explores AI adoption among Vietnamese teachers, a critical lens for the region’s digital education reforms, using the Unified Theory of Acceptance and Use of Technology (UTAUT). Through Structural Equation Modeling (SEM) and Latent Dirichlet Allocation (LDA), we analyzed responses from 246 teachers nationwide. Results show attitude strongly predicts adoption intention, with privacy and ethical concerns shaping acceptance, though fears of AI dependence hinder uptake. Uniform challenges across urban-rural and STEM-non-STEM contexts suggest systemic barriers in Vietnam’s education system. Teachers foresee AI as a pedagogical assistant but highlight insufficient training and privacy risks as key obstacles. These findings underscore the need for Asia-Pacific-relevant policies—AI literacy programs, ethical governance, and equitable access—to foster sustainable integration. This research informs regional educational policy by offering a Vietnam-centric model for balancing technological innovation with pedagogical integrity, addressing shared challenges in the Asia-Pacific’s digital transformation.

Self-report surveys are extensively utilized in educational research to understand students’ perceptions and experiences. However, younger children, particularly those in elementary school, may exhibit a tendency to provide socially desirable responses, potentially compromising the data quality. This study examined the prevalence and impact of socially desirable responses in self-report surveys administered to elementary school students. A total of 1,024 students from grades 4 and 5 in five elementary schools participated in the study. Socially desirable responses were measured using detection items embedded within questionnaires. The findings indicate that (a) more than 20% of elementary school students demonstrated socially desirable responses; (b) female students and those with higher academic achievement were more likely to provide socially desirable responses; (c) socially desirable responses skewed the sample distribution by inflating mean scores and reducing standard deviations; and (d) while internal correlations within scales remained relatively stable, external validity, as reflected in correlations between self-reports and academic performance metrics, was significantly affected after adjusting for socially desirable responses. These results underscore the importance of addressing socially desirable responses when interpreting self-report data from young students. The study concludes with practical recommendations for improving the validity of self-report surveys in educational research.