This study evaluates the effectiveness of a twelve-week Inquiry-Based Science (IBS) program on enhancing science process skills and scientific knowledge among preschool children in Morocco. Conducted in a quasi-experimental setting, it involved 105 children (M = 60.46 months, SD = 4.32), with 37 in the IBS group and 68 in the control group. The program utilized the 5Es instructional model and the Engineering Design Process (EDP) to engage children in active, hands-on learning experiences. Statistical analysis demonstrated that the IBS group achieved substantial improvements in both science process skills and scientific knowledge relative to the control group, with between-group effect sizes (Cohen’s d) ranging from 1.02 and 2.31. These findings highlight the significant impact of structured inquiry-based approaches in early childhood education. The study underscores the need for integrating such methods into the preschool curriculum to foster scientific understanding and skills from a young age, thereby better preparing Moroccan children for future academic and professional challenges. The results advocate for educational stakeholders to consider adopting inquiry-based learning frameworks to enhance the overall quality of early childhood education in Morocco.

This article investigates the adoption, simplification, and usage recommendations of the Indonesian Index of Learning Style Short Form (ILS-SF). The aim is to refine the initial Indonesian ILS, compare the suitability between engineering/non-engineering and high school/university, and assess their learning styles. The participants were 678 students (413 females), with an average age of 19.4±1.92 years. The methods used in this study were adopting the existing Indonesian version of ILS, simplifying–reducing the number of items, empirical verification (validity and reliability), and Indonesia data assessment. The results show that the original ILS could be simplified without sacrificing the quality of the model. On the contrary, validity and reliability measures have increased. Confirmatory Factor Analysis (CFA) supports a reduction from 44 to 15 items. It confirms the validity with favorable indices such as CFI (0.972), TLI (0.966), RMSEA (0.021), SRMR (0.049), and GFI (0.999)—Active-Reflective Cronbach's alpha at 0.507, Sensing-Intuitive at 0.590, and Visual-Verbal at 0.553. Indonesian ILS-SF is faster, simpler, more suitable for engineering than non-engineering, and more ideal for undergraduate than high school students. The analysis revealed that sensory (40.2%), active (18%), and visual (10.2%) preferences dominate among Indonesian students. This study highlights assessment tools tailored to diverse educational contexts.

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.

This study examines the impact of digital tools on fraction comprehension among 5th-grade students with learning difficulties in mathematics. It assesses the effectiveness of three teaching methods: educational software, video tutorials, and their combination. The research involved 252 students from 8 state-funded elementary schools, employing a quantitative experimental design with pre- and post-test assessments. Grounded in Constructivist Learning Theory and Technological Pedagogical Content Knowledge (TPACK), this research explored how technology can enhance mathematical understanding. Results indicated that the combined method achieved the highest improvement (58%, p < .001, Cohen’s d = 3.03), significantly outperforming educational software alone (33%, p = .015, Cohen’s d = 2.52) and video tutorials alone (7%, p = .987, Cohen’s d = 0.14). These findings highlight the substantial benefits of integrating diverse digital tools to effectively support mathematics learning among students facing additional educational challenges.

In primary and middle schools in China, banzhuren is the teacher responsible for managing and overseeing a specific class of students. The lower job satisfaction of banzhurens has been a longstanding issue. This study employs a quantitative method to investigate the impact of banzhurens' self-efficacy and burnout on their job satisfaction. A total of 624 primary school banzhurens from G City (in Henan province, China) participated in an online survey assessing their perceived job satisfaction, self-efficacy, and burnout. The data were analysed using structural equation modelling analysis. The results revealed that (a) banzhurens' burnout negatively influenced their self-efficacy and job satisfaction; (b) banzhurens' job satisfaction was positively influenced by self-efficacy; (c) banzhurens' self-efficacy could mediate the adverse effects of burnout on job satisfaction. Therefore, we suggest that banzhurens' job satisfaction can be enhanced by increasing their self-efficacy, particularly in terms of communication with leaders, and by reducing their burnout.

This PRISMA-based systematic review analyzes how artificial intelligence (AI) and Machine Learning (ML) are integrated into educational institutions, examining the challenges and opportunities associated with their adoption. Through a structured selection process, 27 relevant studies published between 2019 and 2023 were analyzed. The results indicate that AI adoption in education remains uneven, with significant barriers such as limited teacher training, technological accessibility gaps, and ethical concerns. However, findings also highlight promising applications, including AI-driven adaptive learning systems, intelligent tutoring, and automated assessment tools that enhance personalized education. The geographical analysis reveals that most research on AI in education originates from North America, Europe, and East Asia, while developing regions remain underrepresented. Without strategic integration, the uneven implementation of AI in education may widen social inequalities, limiting access to innovative learning opportunities for disadvantaged populations. Consequently, this study underscores the urgent need for policies and teacher training programs to ensure equitable AI adoption in education, fostering an inclusive and technologically prepared learning environment.

There are studies in the learning management literature examining the measure of system usage, but few explore how users apply the software tools to achieve specific work tasks, which in turn leads to perceived benefits. In the context of distance education, this study focuses on how Learning Management Systems (LMS) are fully used by faculty for their instructional needs. It extends existing research on LMS adoption by investigating how faculty members or instructors use the LMS tools for effective class teaching to achieve educational outcomes. Four usage patterns were identified: communication, content management, assessment, and class management. A model is presented to examine how these usage patterns interplay to achieve the perceived benefits. Data were collected from 544 instructors using LMS, such as Blackboard Learn, etc. Structural equation modeling using LISREL was employed to assess the research model. The results suggest that the usage for communication, content, and assessment activities positively impacts the usage for class management. In turn, the usage for class management influences the net benefits perceived by the instructors, and the usage for content also impacts perceived net benefits directly. These results provide practical guidelines for LMS developers’ design improvements and institutions’ policies, such as training instructors to fully utilize LMS features to achieve the maximum benefits of distance education.

Developing efficient and reliable tools for assessing early mathematical skills remains a critical priority in educational research. This study aimed to develop and validate a brief version of the Prueba Uruguaya de Matemática (Uruguayan Mathematics Test, PUMa), a digital tool to assess mathematical abilities in children aged 5 to 6. The original test included 144 items covering both symbolic (66%) and non-symbolic (34%) tasks, such as approximate number system, counting, numerical ordering (forward and backward), math fluency, composition and decomposition of numbers, and transcoding auditory-verbal stimuli into Arabic-visual symbols. Unlike most existing tools that require individual administration by trained professionals and lack cultural adaptation for Latin American contexts, PUMa is self-administered, culturally grounded, and suitable for large-scale assessments using tablets. Using a sample of 443 participants and applying parametric and non-parametric models within the framework of Item Response Theory (IRT), along with correlations with TEMA-3, preliminary evidence was generated showing that the brief version retained precision and validity. The resulting shortened tests included 69 and 73 items for the parametric and non-parametric versions, yielding a balanced representation of symbolic (56%) and non-symbolic (44%) tasks. Despite item reduction, ability scores remained highly correlated between original and brief versions (r > .90), and both brief versions demonstrated strong internal consistency (α = .94). PUMa improves upon existing assessments by combining cultural relevance, group-based digital administration, and real-time data collection, offering a scalable solution for early identification and intervention. These features support personalized educational strategies that foster cognitive and academic development from the earliest stages.

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.

This qualitative study explored the implementation of Differentiated Instruction (DI) in Philippine multigrade classrooms with the aim of understanding teachers’ experiences, strategies, and challenges, as well as developing a performance appraisal tool. Guided by its specific objectives, the research examined how teachers plan, deliver, and manage differentiated lessons while addressing the diverse learning needs of students across multiple grade levels. Findings revealed that effective DI is rooted in intentional instructional planning, including learner profiling, curriculum mapping, and flexible pacing. Instructional delivery was enriched through the use of thematic and multimodal strategies, ability-based groupings, and contextually relevant teaching aids, although technological access and training remained persistent barriers. Classroom management practices emphasized inclusive routines, peer collaboration, and adaptive learning spaces. Teachers also highlighted the importance of assessment tools and reflective teaching practices in continuously improving instruction. In response to these findings, the study developed the Multigrade Differentiated Instruction Performance Appraisal Tool (MDI-PAT), which synthesizes theoretical frameworks with authentic classroom practices. The MDI-PAT serves as both a self-assessment and professional development guide for multigrade educators, promoting ongoing improvement in DI competencies. The study concludes that enhancing teacher capacities in planning, delivery, assessment, classroom management, and reflective practice is essential for fostering inclusive and effective learning environments in multigrade contexts. The insights and tools presented provide a practical framework for educational stakeholders seeking to enhance multigrade instruction in resource-constrained settings.

This study aimed to develop and validate a comprehensive core competency assessment instrument specifically designed for undergraduate students at a research-focused university. Despite growing emphasis on competency-based education (CBE), there are limited psychometrically sound tools tailored to evaluate students’ level of core competencies in research-intensive universities. The current study proceeded in three phases: (a) development of a conceptual framework comprising six core competencies: Integrated Thinking, Knowledge Inquiry, Creative Integration, Global Citizenship, Communication & Collaboration, and Self-Management; (b) item generation and expert validation; and (c) validation through exploratory and confirmatory factor analyses. The final instrument included 77 items across the six competencies. CFA confirmed adequate model fit (CFI = .934–.957; RMSEA = .057–.088). The results showed that the validated instrument can provide a reliable and comprehensive assessment for students' core competencies in research-oriented university settings. This instrument can provide guidelines for developing competency-based education (CBE) curricula in higher education, as well as criteria for evaluating and refining existing CBE programs. This instrument functions as both a psychometrically robust assessment tool and a practical guide for institutional enhancement. It enables precise measurement of students’ core competencies, offering evidence that can inform curriculum design, academic advising, and policy development. In addition, the validated framework lays a strong groundwork for future research to investigate the long-term effects of competency-based education on student achievement, career readiness, and personal development across various higher education settings.

Despite progress made in recent years, women continue to be underrepresented in academic publishing. We aim to share insights from academic women researchers who participated in the Training Needs Assessment for developing their writing for publication skills in an Open Distance Learning institution in South Africa. The research questions that guided the study were: a) What specific challenges do academic women researchers face in developing research and publishing skills? b) What motivated academic women researchers to participate in a writing project? c) What type of support do academic women researchers identify as essential for advancing their research and publishing skills? The data were collected through an initial face-to-face meeting, followed by a Training Needs Assessment from eight purposively chosen participants in a case study design. The findings indicate participants’ challenges of time constraints, lack of confidence, and knowledge as obstacles that hindered their publishing. Despite their challenges, women researchers reported their motivation to participate in the writing project for career advancement, personal development, academic recognition, and their inspiration to publish their research work. The study found that women researchers required writing support, peer collaboration, mentorship, and institutional support to improve their writing for publishing skills. Supporting academic women researchers with focused training, engaging them in collaborative networks, and developing gender-sensitive policies is crucial for promoting equity, breaking down barriers, and ensuring their academic and professional success.

This study applies explainable machine learning to identify which vocal attributes in a lecturer’s speech influence students’ views of a lecturer’s charisma, a key contributor to teaching quality. It further explores whether vocal qualities differ between male and female lecturers and how students of different genders respond to these differences, offering insights into voice-related factors that influence the impact of educators. Speech segments from YouTube videos featuring 200 native-English lecturers were evaluated by 900 students using charisma rating scales. A set of attributes related to three primary prosodic dimensions of voice - pitch, rhythm, and loudness - was computed. A random forest classifier was employed to predict the charisma level based on the speech attributes and to list and rank the attributes that contributed most to the prediction. The findings revealed prominent vocal attributes that achieved higher charisma scores in the students' ratings. Same-gender evaluations of charisma were mainly based on pitch, while cross-gender evaluations rely mostly on loudness or rhythm. The automated, interpretable method provides a reliable and efficient way to measure vocal charisma in academic lecturers. It can be adapted to examine additional individual factors that influence the perception of a lecturer’s charismatic presence. It may also be integrated into practice-based tools, designed to support instructors in improving their presentation skills. Our research bridges the fields of applied psychology and computer science to contribute to the development of educational technology.

In recent years, significant developments have occurred in AI-based chatbots that have been effectively deployed in the educational field. However, given the novelty of this technology, descriptive analyses remain scarce. Although many review studies have focused on the effectiveness of chatbots, they generally present broad results, and only a few have addressed the impact of this technology on learning outcomes. The present study examines the educational implications of AI chatbots on various learning outcomes through a post hoc analysis conducted in accordance with PRISMA principles. It aims to aggregate and analyze findings from studies that examined the use of chatbots and their impact on specific learning outcomes. A total of 26 studies were selected from a pool of 6,721 published between 2021 and 2024 and indexed in the Scopus and Web of Science databases. Data analysis was conducted using the Newcastle-Ottawa Scale (NOS) for Education. The results revealed that AI-chatbot technology has a positive influence on several learning outcomes, including academic achievement, motivation, self-assessment, engagement in learning, self-efficacy, and language learning, among others. The studies also detailed the methodologies and tools employed in these investigations. The study also offers insights into how intelligent chatbots can be leveraged to enhance various learning outcomes.

Vocational education plays a pivotal role in nurturing talent and supporting national development. However, challenges such as outdated talent development concepts, insufficient teacher training, and a lack of attention to soft skills cultivation from both schools and students have hindered the comprehensive development of secondary vocational students. This study aims to explore the direct effect of perceived teachers’ transformational leadership on the soft skills of 324 secondary vocational students in China and to examine the mediating role of students’ self-efficacy in this relationship. Using Structural Equation Modeling (SEM), the results show that teachers’ transformational leadership has a significant positive effect on students’ soft skills (β = 0.33, p < .01). Moreover, self-efficacy partially mediates this relationship (indirect effect β = 0.07, p < .05), accounting for 22.6% of the total effect. These findings suggest that teachers’ inspirational motivation, individualized consideration, and intellectual stimulation directly foster students’ communication, teamwork, and problem-solving skills, while also indirectly strengthening them by enhancing students’ confidence. Practically, the study underscores the need for teacher training in transformational leadership and for policy initiatives that integrate soft skills into vocational curricula.