IRRODL

Since its founding in 2000 by Athabasca University, the International Review of Research in Open and Distributed Learning (IRRODL) has emerged as a leading platform for the dissemination of scholarly work in open and distributed learning. This article revisits IRRODL’s foundational goals, the institutional support that facilitated its creation, and its evolution over 25 years. Through this retrospective, we celebrate the journal’s achievements and examine its future as a freely accessible repository of information and knowledge for practitioners and researchers in open and distributed learning.

Literature inclusion and exclusion (E/I) criteria are a fundamental selection methodology in different applications. Mainly, the E/I criteria are identified and chosen with respect to the question for which the manuscript itself is produced, thus allowing the selection of the literature. This procedure is not always related to the economic availability of independent subjects (e.g., researchers, authors, students) or even institutions in low-income areas or with little willingness to cover the use of paid materials. The proposed criterion (free-full-text-or-next) aims to support independent subjects (without affiliations) or subjects belonging to economically disadvantaged areas.

This bibliometric analysis explores the rapidly growing field of open education, offering insight into its nature and the wide range of academic topics it covers. This study applies co-citation and co-word analyses approach to critically review 402 publications from the Web of Science database. The aim is to identify emerging topics, seminal works, and dominant trends in the literature on open education. The co-citation analysis identifies key publications and thematic clusters that define the field, including discussions on pedagogical innovations, equity and accessibility, quality assurance, and the global impact of open educational practices (OEP). Co-word analysis, on the other hand, highlights the recurrent and emerging keywords within the literature, revealing focal points such as digital transformation in education, the role of massive open online courses (MOOCs), and the significance of open educational resources (OER) in fostering inclusive and equitable learning environments. This study stands out for its quantitative approach in mapping the current academic conditions of open education, offering insights into the dynamic interplay between technology, policy, and pedagogy. It emphasizes the need for a collaborative, inclusive approach to education, employing open educational resources and methods to fulfill the different needs of learners globally. Through this analysis, the study contributes to a deeper understanding of the current state and future directions of open education, advocating for policies and practices that support sustainable, accessible, and high-quality educational experiences.

Learners’ emotional engagement in synchronous online learning (SOL) is critical for improving learning persistence and performance. Nevertheless, there is currently a lack of comprehensive and systematic reviews of emotional engagement in SOL. This review synthesizes the strategies to promote emotional engagement in SOL found in published empirical studies. A total of 32 articles were systematically analyzed by following the grounded theory approach. The primary themes were grouped into four categories: (a) instructor actions (e.g., interacting informally before and after class, encouraging the expression of ideas), (b) learner behaviors (e.g., building rapport with peers, recognizing individual accountability), (c) environment characteristics (e.g., creating a supportive atmosphere, selecting communication modes), and (d) activity design (e.g., using breakout rooms, embedding diverse elements). These findings offer comprehensive understanding and guidance for promoting emotional engagement in SOL for instructors, researchers, and course developers.

This study aimed to identify and compare the efficiency of different theoretical (competence-oriented) and practical (system-activity) approaches to labour education of college students in China using the Open edX online learning platform. The study was conducted at the Shengda College of Economics and Business Management. It involved 150 first-year college students and two full-time teacher-employees from the Labour Protection Department who had responsibility to ensure the quality of labour education and supervise the process of its introduction. The practical approach was 75% more effective when college students were allowed to choose work according to their preferences. The study showed that the practical approach decreased students’ motivation to work by 3%, while the average motivation with a theoretical approach was 45% higher than with a practical one, due to lack of physical work and exhaustion at work.

This study developed and validated the Turkish version of the Self-Directed Online Learning Scale (SDOLS-T) for assessing students’ perceptions of their self-directed learning (SDL) ability in an online environment. Specifically, this study conducted in two stages multiple categorical confirmatory factor analyses factoring in the ordered categorical structure of the SDOLS-T data. The data in this study came from a parent study which utilized the SDOLS-T and other instruments for data collection. From among the three competing models the literature recommends examining to explain the shared variance of items in a survey, the results at stage 1 showed that the correlated, two-factor structure, originally proposed for the SDOLS, was also the best-fit model for the SDOLS-T. At stage 2, using the best-fit model from stage 1, measurement invariance analyses were conducted to examine the extent to which SDL under the SDOLS-T was understood and measured equivalently across the groups specified by four dichotomous demographic variables: gender, network connection, online learning experience, and grade. The stage 2 results indicate the SDOLS-T reached scalar invariance at least for gender and network connection, thus allowing the comparison of latent or manifest means, or any other scores (e.g., total scores, Rasch scores), across the groups by these two demographic variables. In the end, the findings support the SDOLS-T for use in facilitating educational practice (e.g., improving instructional design), advancing scholarly literature (e.g., investigating SDL measurement and content area issues), and informing policy/decision-making (e.g., increasing retention rates and reducing dropout) in online education in Turkey.

Sentiments expressed on massive open online course (MOOC) discussion forums significantly influence learning effectiveness and academic performance. The evolution of learning sentiments on MOOC discussion forums is a dynamic process; however, a gap exists in the current understanding of the interplay between evolving sentiments and their impact on MOOC efficacy. Consequently, to enhance MOOC effectiveness further empirical research is needed to uncover the underlying patterns and temporal dynamics of learning sentiments. This study collected online discussions from 158 MOOC participants and examined the discussions using epistemic network analysis to identify how learning sentiment patterns differed according to performance level and learning topics. The results showed that learning sentiment patterns were affected by both performance level and learning topics, with participants in the high-score group exhibiting stronger associations between engagement-neutral and neutral-frustration, and fewer connections between frustration-delight and frustration-boredom when compared to those in the low-score group. In addition, this study found that engagement was strongly linked to all learning topics in the high-score group, whereas for the low-score group, only engagement and experience showed strong connections. Based on these findings, we discuss the implications for learners and instructors in paving the way for the development of targeted interventions and instructional strategies tailored to optimize MOOC effectiveness.

Recent advancements in educational technology have enabled teachers to use learning analytics (LA) and flipped classrooms. The present study investigated the impact of a LA-based feedback system on students’ academic achievement and self-regulated learning (SRL) in a flipped learning (FL) environment. The study used a pretest-posttest control group quasi-experimental design with 71 pre-service teachers in the experimental group and 56 pre-service teachers in the control group, both enrolled in an information technology course. The experimental group received LA-based feedback during a 4-week training program in the FL classroom, while the control group did not receive this feedback. Data were collected using an achievement test, an online SRL questionnaire, and a student opinion form. The study found that the students’ SRL and academic achievement were not significantly affected by the LA-based feedback system in FL classrooms. In contrast, according to the qualitative research findings, students claimed the LA-based feedback helped them learn because it allowed them to monitor their learning processes.

This study aimed to evaluate the utility of artificial intelligence (AI) in improving the persuasive communication skills of online Master of Business Administration (MBA) students. In particular, this study investigated the influence of personalization through AI using the Google Gemini platform on conventional and online instructional approaches. This quasi-experimental study used a pretest and posttest design to compare two groups of MBA students pursuing persuasive online communication. The experimental group (n = 32) interacted with the AI-based personalized learning materials, whereas the control group (n = 32) used standard instructor-designed online modules. During the 12-week intervention period, the experimental group was provided with customized practice activities. Conversely, the control group was offered conventional online learning material. The effectiveness of both approaches was evaluated using pretests and posttests. The results of Tukey’s Honestly Significant Difference (HSD) test provided insight into the areas where AI-based personalized learning had a statistically significant impact. These results support the conclusions derived from an analysis of variance and further validate the study’s research hypotheses. This study demonstrates the advantages of incorporating AI into language development for remote learners and offers valuable insights for integrating AI-driven technologies into distance education.

The aim of this study was to implement on-the-job training (OJT) in a distance learning environment. To achieve this, job analyses were conducted on spring semester beekeeping activities at a vocational school in the eastern Anatolia region of Türkiye, and content and activities were designed with an OJT environment prepared. Participants received a task video and checklist to be completed weekly during the relevant week of the season. They were required to record and send videos while performing tasks, with feedback provided, and they were encouraged to participate in expert/peer communication activities. Initially, participants completed tasks correctly at an 80% rate, which increased to 85% after feedback. Task completion levels were examined, showing higher levels for short, simple, and less risky tasks. The application showed a positive effect on academic achievement in favor of the final test.

This study explored the impact of digital technologies on the development of musical skills among music students. A learning experiment was conducted with 66 students between the ages of 18 and 21 from China, France, Italy, and Spain. The study used the methods of a survey and online discussions. Participants verified that the present advancement of digital technologies allows artists to participate in a professional musical environment without formal schooling. Students in the experimental group had a more positive attitude toward learning and its significance for their personal and professional development. Most survey items were rated between 3 and 4 on a 4-point scale, indicating students’ overall satisfaction with the training. The results of the online discussion also indicated a high level of support for the use of digital technologies in music education, as well as highlighting the innovative nature of the training course and the advantages of traditional music education. Three quarters of participants supported the use of digital technologies in education. Students in the experimental group were able to acquire more advanced professional musical skills, which facilitated the creation of melodies (including the composition of musical fragments on specified themes, musical arrangements of varying complexity, and the development of principles for processing musical works) compared to students in the control group (focused on the development of musical ear and sense of rhythm), who were initially trained under the traditional system. The study’s findings support the effectiveness of an integrated strategy for nurturing musical creativity that involves collaboration between students, teachers, and cutting-edge technology.

In online microteaching, pre-service teachers (PSTs) deliver lessons through online platforms, thus acquiring valuable experience in effective use of technological tools. In refining these experiences, it is crucial for the PSTs to undergo self-, peer, and tutor assessments. This study examined the concordance among self-, peer, and tutor assessments in online microteaching practices, along with students’ views on their online microteaching experiences. A case study model was adopted, involving doctoral students enrolled in the Planning and Evaluation in Instruction course. The findings indicated alignment between students’ self-assessment and peer assessment, albeit with lower scores compared to those provided by the course tutor. Overall, students expressed positive views regarding online microteaching. They highlighted the benefits of critical thinking, self-reflection, and peer feedback in refining their teaching strategies. However, challenges such as time management, communication, and planning were noted by the students.

Nursing students faced learning losses during the COVID-19 pandemic due to the transition to virtual classes, inadequate communication, and reliance on virtual clinical training as a prerequisite for clinical practice. This study aimed to investigate the extent of learning loss experienced by nursing students and examine the impact of simulation-based learning (SBL) on mitigating this learning loss and on students’ confidence, satisfaction, and performance before and after the SBL program. This quasi-experimental study used a within- and between-subjects design. Data were collected from January 2022 to May 2023 from 177 nursing students before and after the SBL program. The Learning Loss scale and Simulation Training Evaluation Questionnaire were used. Substantial learning losses were observed in nurses’ knowledge, professional attitude, and skills before the intervention. The intervention group had significantly higher knowledge, professional attitude, and professional skills than the control group. The intervention significantly improved nursing students’ confidence, expectations/satisfaction, and performance. The regression model revealed that age and weeks in internship were significant predictors of learning loss. Prior distance education experience did not show any significant association with learning loss. Thus, SBL is useful in crisis situations; it enhances nursing students’ knowledge, professional attitudes, and professional skills. Course designers should consider integrating SBL into nursing curricula as an innovative teaching strategy to compensate for possible learning losses. This approach will help prepare graduates to enter the workforce with the ability to quickly adapt and practice confidently in clinical settings to ensure patient safety.

Massive open online courses (MOOCs) offer rich opportunities to comprehend learners’ learning experiences by examining their self-generated course evaluation content. This study investigated the effectiveness of fine-tuned BERT models for the automated classification of topics in online course reviews and explored the variations of these topics across different disciplines and course rating groups. Based on 364,660 course review sentences across 13 disciplines from Class Central, 10 topic categories were identified automatically by a BERT-BiLSTM-Attention model, highlighting the potential of fine-tuned BERTs in analysing large-scale MOOC reviews. Topic distribution analyses across disciplines showed that learners in technical fields were engaged with assessment-related issues. Significant differences in topic frequencies between high- and low-star rating courses indicated the critical role of course quality and instructor support in shaping learner satisfaction. This study also provided implications for improving learner satisfaction through interventions in course design and implementation to monitor learners’ evolving needs effectively.

In response to the COVID-19 pandemic, many educational institutions switched to online learning to maintain learning activities. With the global pandemic, the educational environment was forced to shift from traditional face-to-face teaching or blended learning to a fully online learning model. In February 2020, China took the lead in announcing the implementation of online learning, encouraging most teachers to use it. Exploring the potential of online learning to replace traditional face-to-face teaching is a topic deserving consideration. This study explored the factors that influenced teachers’ intention to switch to online learning during the pandemic, using a push-pull-mooring model. The study analyzed 283 valid responses gathered through an online questionnaire and found that push effects, pull effects, and habits significantly impact teachers’ intention to switch from offline to online teaching. The findings provide additional insights into the future of higher education after the pandemic.

This systematic review sheds light on the role of ontologies in predicting achievement among online learners, in order to promote their academic success. In particular, it looks at the available literature on predicting online learners’ performance through ontological machine-learning techniques and, using a systematic approach, identifies the existing methodologies and tools used to forecast students’ performance. In addition, the environment for generating ontologies, as considered by academics in the field, is likewise identified. Based on the inclusion criteria and by adopting PRISMA as a research methodology, seven studies and two systematic reviews were selected. The findings reveal a scarcity of research devoted to ontologies in the prediction of learners’ achievement. However, the research outcomes suggest that building an ontological model to harness machine-learning capabilities could help accurately predict students’ academic performance. The results of this systematic review are useful for higher education institutes and curriculum planners. This is especially pertinent in online learning settings to avoid dropout or failure. Also highlighted in this study are numerous possible directions for future research.