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.

Recent studies identified that faculty research productivity increased when they felt competent in conducting research. Faculty level of research competency varies due to academic training, context, country, discipline, and experiences; however, what is faculty research competency? The core competencies of faculty research are unclear; thus, the current study systematically reviewed the literature. Researchers used Boolean searches of four popular databases to identify 553 articles for first-level screening. These yielded 46 peer-reviewed journal articles for full-text analysis, six of which focused on faculty populations (40 on non-faculty). Six core components of faculty research proficiency were identified: finding and reviewing literature, planning a study, collecting and analyzing data, writing research, disseminating research findings, and managing research projects. Compared to non-faculty populations, faculty are uniquely more engaged in research project management. Researchers also identified 18 sub-competencies that will help to measure faculty research competency more reliably in the future. Finally, as the identified studies relied on self-reported measurements that may carry self-representation bias, an aspirational implication is to develop a competency-based diagnostic test for measuring faculty research competence.

This scoping review comprehensively explores how artificial intelligence (AI) is being incorporated into accounting education, examining the evolving educational setting and its potentially transformative impact on the development of future accounting professionals. Following the Arksey and O'Malley (2005) methodology and PRISMA-ScR guidelines (Tricco et al., 2018), this review synthesizes systematically a diverse set of academic literature to determine major trends, new opportunities, and long-standing challenges of integrating AI into accounting pedagogical practices. Key findings demonstrate AI's transformative potential in enhancing student engagement, fostering deeper learning, aligning educational curricula with contemporary industry demands, and improving teaching efficiency through innovative tools and techniques. However, substantial challenges persist, including faculty preparedness, the complexity of curriculum redesign, resistance to change, and critical ethical considerations surrounding the use of AI in education. These findings emphasize the multifaceted nature of integrating AI into accounting pedagogy. The review emphasizes the need for cooperation between academia, industry practitioners, and policymakers to develop adaptive, forward-thinking pedagogical strategies and establish robust ethical frameworks. These efforts are essential to improve learners with the skills and competencies required to thrive in a dynamic, technology-driven professional environment.

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.

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.

Student dropouts led to a squandering of the education budget. The education system and society are significantly affected, particularly in terms of potential development. To ensure vocational students graduate and secure satisfactory employment in line with the field of study. Implementing a comprehensive system that encompasses promoting, supporting, preventing, and resolving various student issues is essential. This system includes close, meticulous care and support, timely and appropriate interventions, enhancement of life skills, guidance, and holistic student development. This research found that the risk factors in the teaching and learning process account for 90.78 percent of the reasons scholarship students drop out of the education system; there are instances of absenteeism, inappropriate behavior, and a dislike for the teacher and the subject they are teaching. Additionally, the care and support system for vocational students at risk of dropping out consists of four components: Component 1: living care; Component 2: dropout protection; Component 3: counseling and advising; and Component 4: transfer to support. The empirical evaluation of the care and support system for students concluded that the overall assessment was highly suitable. The information should be utilized for planning and policymaking in educational institutions.

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.

The personalization of learning and teaching processes represents an advanced approach to education that adapts content, pace, and teaching methods to the individual needs and preferences of students. This approach relies on analyzing diverse student characteristics, such as their knowledge level, progress, learning style, and interests. Achieving these goals is significantly supported by the use of information and communication technology, which facilitates and enhances the implementation of personalization in technology-enhanced learning (TEL). The primary objective of personalization is to increase student engagement, motivation, and support in achieving learning outcomes through individualized learning paths, real-time progress tracking, and feedback. This systematic literature review examines existing personalization approaches in secondary and higher education, supported by technology. The study investigates their effectiveness and provides recommendations for future research. Results reveal that personalized teaching methods—primarily through recommender systems, adaptive learning platforms, and algorithm-driven models—are effective in tailoring educational experiences by leveraging diverse student data, such as demographics, prior achievements, learning styles, and digital engagement. The review shows a predominant focus on higher education, particularly in subjects related to computer science and digital technologies. Quantitative evaluations complemented by qualitative insights, consistently indicate that personalization enhances content mastery, motivation, and overall satisfaction, with no significant negative effects identified.

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.

Analysis of student grades and demographic data to understand the effects of modality and Covid-19 on academic performance is important for universities to understand the impact these factors may have on course grades. This study analyzes all the 615,964 complete undergraduate student-course records from Kennesaw State University (KSU) spanning from 2015 to 2024 to examine the impact of course modality and the Covid-19 pandemic on academic performance. The population dataset includes student demographics (e.g., sex, age, ethnicity), prior GPA, and course characteristics (e.g., department, modality). Descriptive statistics and trend analyses were employed to evaluate grade outcomes across in-person, online, and hybrid modalities over the 9-year period. Results indicate a temporary increase in mean course grades during the Covid-19 period, followed by a return to the pre-pandemic upward trend. Hybrid courses consistently exhibited the highest mean grades throughout the study period. However, consistent patterns across modalities, demographics, and academic units suggest that these factors have limited influence on grade outcomes. These findings raise questions about the reliability of GPA and course grades as indicators of learning success across different instructional contexts and student populations.

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.