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This field note examines the recent regulatory framework for distance higher education in Brazil, analyzing the implications of Decree 12,456/2025 and Ordinance 378/2025. Through critical analysis, we assessed the alignment of these measures and their potential impacts on educational quality, accessibility, and institutional accountability. We examined the measures designed to balance the expansion of access with the assurance of quality, including mandated percentages of in-person and synchronous activities, redefined faculty roles, and restrictions on institutional sharing. While acknowledging the potential to enhance academic rigor and curb low-quality programs, the analysis highlighted significant implementation challenges. These include increased operational costs, potential impacts on tuition, and concerns that restricting teacher education to blended or in-person modalities may exacerbate teacher shortages in remote areas. The study concluded that the new framework’s ability to reduce inequalities and improve employability depends on financial support, vigilant oversight, and further research, offering a valuable case study for global debates on regulating digital higher education.

The Handbook of Open Universities Around the World, edited by Sanjaya Mishra and Santosh Panda, offers both a panoramic survey and a reflective critique of what openness truly means in higher education today. Drawing together insights from more than 100 scholars and practitioners, the editors have curated an extraordinary compilation that maps the histories, organizational structures, and innovations of 47 open universities across Africa, the Americas, Asia, Europe, and Oceania. The result is not only a celebration of institutional achievement but also an invitation to confront difficult questions about equity, sustainability, and the future of open learning.

Open universities were originally conceived as democratic institutions designed to remove barriers of geography, class, gender, and prior schooling. They opened doors to learners traditionally excluded from mainstream education systems. In the current era of rapid digital transformation, when artificial intelligence (AI) and data-driven technologies are reshaping how education is delivered and experienced, the notion of openness demands fresh examination. The Handbook situates itself precisely at this critical juncture, bridging historical foundations with emerging digital realities.

This meta-analysis synthesized empirical findings on the influence of ChatGPT on learning achievement. An electronic database search using PRISMA guidelines was conducted with relevant keywords to identify eligible research studies published between November 2022 and December 2024. A total of 22 eligible publications that met our inclusion criteria were reviewed. The overall effect size of ChatGPT's influence on learning achievement was moderate (g= 0.573), suggesting that ChatGPT has the potential to improve learning outcomes. Most participants in the studies were undergraduates (70.9%). However, subgroup analysis revealed that the effect size for middle and high school students (g= 0.928) was larger than that for undergraduates (g= 0.538), although the difference was not statistically significant. This finding highlights the importance for instructors and educational practitioners to consider the applications of ChatGPT in middle and high school settings. No significant statistical differences were found among the three learning domains: cognitive (g= 0.612), affective (g= 0.481), and metacognitive (g= 0.619). Given that nearly half of the studies focused on the cognitive domain, it is important to diversify the application of generative AI across a variety of subjects in different learning domains. The most frequently used instructional approaches with ChatGPT applications were lectures (22.1%) and self-regulated learning (16.3%). The largest effect sizes were observed for self-regulated learning (g= 1.115) and case-based learning (g= 0.836), while the smallest effect size was for game-based learning (g= 0.092, ns). This study was conducted within two years of ChatGPT's emergence, limiting in our ability to analyze a large number of publications. Nevertheless, this study offers meaningful implications for future research on the application of ChatGPT for educational purposes.

Given the growing demand for flexible and accessible health education, massive open online courses (MOOCs) have been recognized as instrumental in expanding undergraduate learning. This systematic review was conducted to investigate the use of MOOCs in undergraduate health education, focusing on publication trends, geographic distribution, and key research variables. A total of 31 peer-reviewed articles were reviewed, and data were sourced from six international databases: Web of Science, Scopus, ERIC, EBSCOHost, ScienceDirect, and PubMed. It was found that MOOCs have been integrated into undergraduate health education since 2014, with a notable increase in publications observed after 2022. The highest number of studies was published in China. Student satisfaction was identified as the most frequently studied variable, and medical education was reported as the dominant field. Quantitative research were predominantly used, with sample sizes between 101 and 300 participants. Questionnaires were commonly employed as a data collection tool, and many studies were based on custom-developed MOOCs for their research. Courses were typically between 4 and 6 weeks duration. Improved clinical skills were frequently reported as outcomes, while the lack of practical experience in MOOC-based learning was identified as a major limitation. More practice-oriented teaching approaches were recommended by most studies. To enhance the effectiveness of MOOCs in health education, more innovative and practical implementation strategies are needed. Future research is encouraged to address these gaps and strengthen the impact of MOOCs on undergraduate health programs. The growing role of MOOCs in health education is highlighted, particularly the need to integrate practical components for greater educational impact.

Digital literacy is central to collaborative teaching in technology-mediated environments, particularly open and distributed learning. Guided by the Community of Inquiry and TPACK (Technological Pedagogical Content Knowledge) frameworks, this systematic review examines how digital literacy enables educators to codesign instruction, sustain interaction, and support reflective practice while addressing structural and contextual barriers. Following PRISMA 2020, comprehensive searches in Scopus and the Web of Science identified 32 peer-reviewed articles published in 2024. Thematic synthesis produced three strands: (a) integration of digital literacy in education, highlighting links to teaching presence, professional development, and instructional design; (b) digital literacy in response to educational challenges, demonstrating its role in resilience, equity, and socio-emotional support across remote and hybrid contexts; and (c) advancing learning through digital competencies, detailing gains in collaboration, critical inquiry, and innovative use of augmented reality, virtual reality, data analytics, and emerging AI tools alongside ethical considerations. Evidence indicates that digital literacy functions as a pedagogical capacity rather than solely a technical skill and yields the strongest outcomes when aligned with institutional culture, curriculum design, and continuous professional learning. Policy recommendations include sustained investment in equitable infrastructure, structured capacity building aligned with UNESCO’s Digital Literacy Global Framework and ICT (Information and Communication Technology) Competency Framework for Teachers, and explicit attention to ethics and inclusion. Future research should adopt longitudinal and comparative designs to trace the impact on educator identity, collaboration, and learner outcomes.

While teachers worldwide rapidly switched to emergency remote teaching almost overnight owing to the unprecedented global pandemic, the rise of artificial intelligence (AI) has further transformed language education paradigms. Although previous research has explored foreign language teaching anxiety (FLTA), the self-perceived online L2 speaking anxiety of teachers remains underexplored. Accordingly, this study has been designed on a wide scale to address this lacuna by focusing on the perceptions of anxiety of 179 non-native EFL teachers at the Ministry of Education and instructors in higher education contexts. Moreover, it aimed to reveal its provoking reasons and finally the reported reflections of educators’ apprehension on virtual classes. To that end, qualitative and quantitative data were gathered in a complementary fashion through semi-structured interviews and an online survey developed by the researcher. The study identified the lack of perceived competence, troubles with online technologies, and learners’ English proficiency as factors leading EFL educators to experience online L2 speaking anxiety despite their self-confidence. Their reported reflections also disclosed that self-confidence without competence would be of almost no use in language teaching. Finally, some significant differences were detected between the participants’ demographic variables and their online L2 speaking anxiety.

Online learning environments tend not to provide the social and pedagogical cues of physical classrooms, so evaluating student engagement and emotional states in real time becomes challenging. Current methods depend mainly upon facial expression recognition or textual sentiment analysis, constraining the depth and accuracy of behavioral interpretation. This research suggests a multimodal learning analytics framework that combines visual and textual data to infer learner emotions and engagement for improving the interpretability, responsiveness, and pedagogical value of learning analytics systems in digital education. Two datasets were created: (a) a facial expression dataset of 10,000 grayscale images annotated over five emotion categories and (b) an engagement dataset of 4,000 images annotated according to behavioral indicators. Concurrently, 1,667 learner feedback responses from massive open online courses were prepared for sentiment analysis. Convolutional neural networks (CNNs) were used for emotion and engagement classification, and a fine-tuned BERT (bidirectional encoder representations from transformers) model for sentiment analysis. A rule-based integration engine combined outputs to create multidimensional behavioural typologies. The CNN models reached >92% validation accuracy for both emotion detection and engagement detection tasks, whereas the BERT sentiment classifier achieved F1 = 0.87 and 88.1% accuracy. The multimodal integration procedure identified four unique learner behavior typologies (e.g., students who were cognitively engaged but visually disengaged). The framework offers an accurate, interpretable, and scalable real-time learning analytics solution. Compared with previous methods, it overcomes significant limitations and offers a useful resource for facilitating adaptive, data-based instruction interventions, especially in online and health science education.

This study examined how task characteristics (TC) and individual characteristics (IC) affect cognitive load (CL) and how artificial intelligence generated content (AIGC) moderates these effects in online learning. Participants included 435 undergraduate students (200 males and 235 females) enrolled in an introductory educational technology course. A structural model, conducted using Mplus software, was employed to test the relationships between each of TC and IC, and CL. Additional analyses explored the moderating role of AIGC on the relationship between TC and CL, the impact of AIGC on the relationship between IC and CL, as well as how these patterns differed by gender. Results revealed that TC positively affected CL, whereas IC exhibited a negative correlation. Moreover, AIGC negatively affected the relationship between TC and CL, but it enhanced the relationship between IC and CL. The moderating role of AIGC differed by gender. Specifically, AIGC positively influenced the connection between IC and CL among males but not females, and it weakened the relationship between TC and CL among females but not males. The implications and limitations are also discussed.

This study investigated middle school students’ experiences with emergency remote education during the COVID-19 pandemic using natural language processing (NLP), sentiment analysis, and topic modeling techniques. A total of 2,739 valid responses from Turkish students (ages 9–15) were collected through open-ended survey questions regarding the perceived advantages and disadvantages of distance learning. Sentiment classification was performed using a semi-supervised machine learning approach, combining TF-IDF, Word2Vec, and FastText vectorization with five classification algorithms. The TF-IDF + support vector machines (SVM) combination yielded the highest performance (F1 = 0.85). Results show a total of 1,867 positive and 2,542 negative opinions, indicating that students generally adopted a more critical view of distance education. To explore the thematic structure of opinions, topic modeling was applied with six topics. Positive sentiments clustered around themes such as educational continuity, health protection, time savings, flexible scheduling, self-regulated learning, and digital literacy. Negative sentiments were dominated by themes including limited interaction, screen fatigue, perceived low quality, technical barriers, and structural inequalities. Findings suggest that while students appreciated the safety and flexibility of remote learning, they also faced significant pedagogical, physical, and technological challenges. The study contributes methodologically by demonstrating the effectiveness of AI-based text analysis and offers practical implications for designing more equitable and student-centered digital education models. These results underscore the importance of integrating NLP and machine learning tools into educational research to uncover deeper insights from student-generated content at scale.

This study analyzed the role of open and distance education (ODE) in reducing the educational gap in Indonesia, particularly between urban and rural areas. The research method was a literature review that collected and analyzed various articles and reports related to ODE in Indonesia and other developing countries. The findings indicated that ODE had great potential to enhance access to education in remote areas; however, this potential has been constrained by uneven technological infrastructure, lack of teacher training, and educational policies that are not yet inclusive. This study recommended improvements in policies to support the development of digital infrastructure and continuous teacher training. Practical recommendations include providing subsidies for devices and Internet access for students in rural areas. While ODE has provided solutions to similar educational gaps in other developing countries, such as India and Nigeria, the implementation of more inclusive policies and enhanced teaching capacity is necessary to optimize its benefits in Indonesia.

This study focused on procedures for creating, testing, and developing a set of reusable online resources for use in English for academic purposes programmes. The aim of the materials was to help migrants and refugees develop the linguistic and cultural skills, knowledge, and understanding they would need to engage, interact, and collaborate effectively in a multicultural context. Development of the materials involved an iterative process using a three-stage approach:

1. Expert review: Experts in relevant fields worked through the first version of the materials and provided critical feedback, which guided initial revisions.
2. Usability testing groups: Small groups of target users (students and teachers) used the revised materials in workshop settings, and data were gathered from observations, interviews, and written comments.
3. Wider evaluation: Larger-scale use and evaluation of the materials (which is ongoing, beyond the scope of this paper).

This article reports on the second stage.

This study investigated how the community of inquiry (CoI) framework can inform digital platform design for interprofessional continuing professional development (ICPD) in healthcare. Using a three-stage comparative methodology, we analyzed technological tools from foundational CoI literature (stage 1), conducted a rapid review of current digital ICPD practices (stage 2), and synthesized findings through matrix-based comparison (stage 3). The analysis of 10 foundational CoI studies and 11 digital interprofessional education studies revealed four distinct adaptation patterns: (a) technological convergence in core communication tools (asynchronous forums, Learning Management System (LMS) platforms); (b) evolutionary divergence in collaborative technologies (video conferencing, real-time document sharing); (c) implementation gaps in reflective and scaffolding tools; and (d) professional context adaptations addressing healthcare-specific needs. While current ICPD practices have demonstrated strong alignment with CoI principles in communication and collaboration tools, significant gaps exist in structured reflection mechanisms, automated feedback systems, and adaptive facilitation features. Critically, systematic CoI framework application in authentic ICPD contexts with practicing professionals has remained largely unexplored, with studies predominantly focused on pre-licensure interprofessional education. Current implementations have used CoI retrospectively as an analytical framework rather than proactively for design guidance. These findings suggest selective rather than comprehensive CoI integration in professional continuing education contexts. The study provided preliminary theoretical guidance for enhancing digital ICPD through CoI-informed design while highlighting the urgent need for empirical validation with practicing healthcare professionals.

Collaborative knowledge-building is an important mode of learning in which students’ cognitive presence has a significant impact on learning outcomes. To better understand how cognitive presence influences collaborative learning, this study applied three complementary analytic approaches: epistemic network analysis, which maps how ideas are connected in discussions; sequential pattern mining, which identifies temporal sequences; and social network analysis, which examines the interaction patterns and roles among group members. Using data from 37 students divided into 8 groups in a university course on academic reading and writing, we compared high-performing groups (HPGs) and low-performing groups (LPGs). The results showed that HPGs demonstrated stronger exploratory, integrative, and problem-solving abilities in their cognitive networks, with members actively exchanging ideas, questioning, and summarizing. In contrast, LPGs relied more on encouragement and reminders to sustain discussions. Furthermore, HPGs displayed more complex and varied behavioral sequences and clearer leadership and facilitation roles within their social networks, whereas LPGs showed simpler and less developed interaction patterns and lacked core members in their networks. These findings provide insights for instructors on how to better design and guide group knowledge-building to enhance online collaborative learning outcomes.

The effectiveness of technical education may vary depending on the delivery method. This study compared the effects of online, face-to-face (F2F), and hybrid learning on engineering students’ academic performance. The study involved 450 second-year students pursuing an engineering degree at a technical university in China. The pre-test and post-test scores for the five core academic subjects (i.e., computer programming, further mathematics, physics, electrical engineering, and analytical mechanics) revealed a statistically significant improvement in academic performance across all subjects after use of hybrid learning (p < 0.000). The average gains were 3.46 points in computer programming, 4.07 points in further mathematics, 3.24 points in physics, 2.5 points in electrical engineering, and 3.06 points in analytical mechanics. The online and F2F delivery groups exhibited a statistically significant improvement with respect to scores for electrical engineering (p < 0.000) and physics (p < 0.002) only. The one-way ANOVA and Scheffe’s test results revealed that the hybrid model had the strongest learning effects compared to online and F2F. A SWOT analysis helped to further explore students’ perceptions of the three delivery formats. The present findings, which highlighted the effectiveness of hybrid learning, can be helpful in creating adaptive learning programs for engineering students.

Editorial – Volume 27, Issue 1