The International Review of Research in Open and Distributed Learning (www.irrodl.org) is a refereed, open access e-journal that disseminates original research, theory, and best practice in open and distributed learning worldwide. This journal was formerly named the "International Review of Research in Open and Distance Learning", the name change from "distance" to "distributed" to emphasise the new focus on openness and particularly on open educational resources (OER).

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The Globethics.net library contains articles of the International Review of Research in Open and Distributed Learning as of vol. 1(2000) to current.

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  • Are K–12 Teachers Ready for E-learning?

    Polat, Elif; hopcan, sinan; Yahşi, Ömer (Athabasca University Press, 2022-05-02)
    Readiness is important for the success of the e-learning process. The purpose of this study was twofold: to develop a scale to measure K–12 teachers’ e-learning readiness, and to examine their readiness to teach online. The participants were 3,295 K–12 teachers working in Izmir, Turkey. First EFA, then CFA-SEM was performed. Additionally, teachers’ e-readiness in terms of gender, years of service, school level, and daily device usage time were examined. Teachers are ready for e-learning considering their overall scores. A significant difference was found in favor of males in the “technical competence” factor and in favor of females in the “colleague, content, and pedagogical and ethical competence factors”. The readiness of younger teachers is generally higher. On a factor basis, there is only a significant difference in the factors of computer self-efficacy and student readiness according to educational level. As the use of devices increases, technology-related readiness increases. The readiness of teachers plays an important role in determining future strategies, measures, and interventions that need to be taken to advance e-learning.
  • Mapping Network Structure and Diversity of Interdisciplinary Knowledge in Recommended MOOC Offerings

    Zhang, Jingjing; Yang, Yehong; Barberà, Elena; Lu, Yu (Athabasca University Press, 2022-05-01)
    In massive open online courses (MOOCs), recommendation relationships present a collection of associations that imply a new form of integration, such as an interdisciplinary synergy among diverse disciplines. This study took a computer science approach, using the susceptible-infected (SI) model to simulate the process of learners accessing courses within networks of MOOC offerings, and emphasized the potential effects of a network structure. The current low rate of access suggests that a ceiling effect influences learners’ access to learning online, given that there are thousands of courses freely available. Interdisciplinary networks were created by adding recommended courses into four disciplinary networks. The diversity of interdisciplinarity was measured by three attributes, namely variety, balance, and disparity. The results attest to interesting changes in how the diversity of interdisciplinary knowledge grows. Particularly remarkable is the degree to which the diversity of interdisciplinarity increased when new recommended courses were first added. However, changing diversity implied that neighbouring disciplines were more likely to come to the forefront to attach to the interdisciplinarity of MOOC offerings, and that the pace of synergy among disparate disciplines slowed as time passed. In the absence of domain experts, expert knowledge is not sufficient to support interdisciplinary curriculum design. More evidence-based analytics studies showing how interdisciplinarity evolves in course offerings could help us to better design online courses that prepare learners with 21st-century skills.
  • Cross Validating a Rubric for Automatic Classification of Cognitive Presence in MOOC Discussions

    Hu, Yuanyuan; Donald, Claire; Giacaman, Nasser (Athabasca University Press, 2021-11-15)
    As large-scale, sophisticated open and distance learning environments expand in higher education globally, so does the need to support learning at scale in real time. Valid, reliable rubrics of critical discourse are an essential foundation for developing artificial intelligence tools that automatically analyse learning in educator-student dialogue. This article reports on a validation study where discussion transcripts from a target massive open online course (MOOC) were categorised into phases of cognitive presence to cross validate the use of an adapted rubric with a larger dataset and with more coders involved. Our results indicate that the adapted rubric remains stable for categorising the target MOOC discussion transcripts to some extent. However, the proportion of disagreements between the coders increased compared to the previous experimental study with fewer data and coders. The informal writing styles in MOOC discussions, which are not as prevalent in for-credit courses, caused ambiguities for the coders. We also found most of the disagreements appeared at adjacent phases of cognitive presence, especially in the middle phases. The results suggest additional phases may exist adjacent to current categories of cognitive presence when the educational context changes from traditional, smaller-scale courses to MOOCs. Other researchers can use these findings to build automatic analysis applications to support online teaching and learning for broader educational contexts in open and distance learning. We propose refinements to methods of cognitive presence and suggest adaptations to certain elements of the Community of Inquiry (CoI) framework when it is used in the context of MOOCs.
  • Book Review: Exploratory Programming in the Arts and Humanities

    Hammond, Kelly (Athabasca University Press, 2022-05-02)
  • The Effects and Implications of Using Open Educational Resources in Secondary Schools

    Harvey, Paul; Bond, John (Athabasca University Press, 2022-05-02)
    Open educational resources (OER) constitute a curriculum innovation that is considered revolutionary and has the potential to change the landscape of curriculum at all levels and content areas. OER have gained attention and widespread acceptance by educators and policy makers since 2002.  The promise of OER is that they provide cost savings, promote collaboration, and are adaptable to the needs of teachers and students while providing a legitimate alternative to commercially produced print textbooks. Determining the relevance and viability of the movement to embrace OER requires an examination of theoretical foundations and empirical research to illuminate the effect of using OER as core curricula. While advocates promote the use of OER as a financially liberating model of curriculum and as a source of constructivist learning materials, more research is needed. The purpose of this study was to examine the relationship between OER and student learning. The study critically analyzed previous studies on OER and applied empirical analyses to the use of OER by a sample of middle schools. Twenty-eight middle schools from Washington State served as the subjects for the study. The study followed an ex post facto causal comparative model. Three research questions provided the focus for the study to investigate the effects of OER curriculum, duration of curriculum use, and other factors on student achievement in middle school mathematics. The results of the study found non-significant effects for OER use in relationship to school performance in mathematics, and significant effects on math scores for the variables of student poverty, curriculum duration, and cohort size.
  • Understanding the Relationship Among Self-efficacy, Utility Value, and the Community of Inquiry Framework in Preservice Teacher Education

    Akcaoglu, Mete; Akcaoglu, Mustafa Ozturk (Athabasca University Press, 2022-05-02)
    School closures during the COVID-19 pandemic have shown the importance of distance education, and teachers have been tasked with designing and delivering online courses in a short amount of time without much preparation or deliberation. As the future generation of teachers, preservice teachers need to be prepared to teach online, and their motivation to do so is a key factor in how successfully they do it. The community of inquiry framework provides researchers and practitioners with a framework for designing and delivering online courses, while self-efficacy and utility value are important motivational constructs predicting future engagement and success in tasks. In this cross-sectional survey study, we investigated preservice teachers’ (n = 344) perceptions of their self-efficacy, utility value, the importance of the three components of the community of inquiry framework: teaching presence, social presence, and cognitive presence. Our results show that overall, preservice teachers had high motivation to teach online and high perceptions of the three presences. Our regression analyses indicated that while preservice teachers’ self-efficacy was a significant predictor of teaching presence, utility value only significantly predicted social presence. We discuss the implications of these findings for teacher education programs, including a holistic approach to teaching online learning and instructional design.
  • Fine-tuned BERT Model for Large Scale and Cognitive Classification of MOOCs

    Sebbaq, Hanane; El Faddouli , Nour-eddine (Athabasca University Press, 2022-05-02)
    The quality assurance of MOOCs focuses on improving their pedagogical quality. However, the tools that allow reflection on and assistance regarding the pedagogical aspects of MOOCs are limited. The pedagogical classification of MOOCs is a difficult task, given the variability of MOOCs' content, structure, and designs. Pedagogical researchers have adopted several approaches to examine these variations and identify the pedagogical models of MOOCs, but these approaches are manual and operate on a small scale. Furthermore, MOOCs do not contain any metadata on their pedagogical aspects. Our objective in this research work was the automatic and large-scale classification of MOOCs based on their learning objectives and Bloom’s taxonomy. However, the main challenge of our work was the lack of annotated data. We created a dataset of 2,394 learning objectives. Due to the limited size of our dataset, we adopted transfer learning via bidirectional encoder representations from Transformers (BERT). The contributions of our approach are twofold. First, we automated the pedagogical annotation of MOOCs on a large scale and based on the cognitive levels of Bloom’s taxonomy. Second, we fine-tuned BERT via different architectures. In addition to applying a simple softmax classifier, we chose prevalent neural networks long short-term memory (LSTM) and Bi-directional long short-term memory (Bi-LSTM). The results of our experiments showed, on the one hand, that choosing a more complex classifier does not boost the performance of classification. On the other hand, using a model based on dense layers upon BERT in combination with dropout and the rectified linear unit (ReLU) activation function enabled us to reach the highest accuracy value.
  • Effects of Online Self-Regulated Learning on Learning Ineffectiveness in the Context of COVID-19

    He, Wei; Zhao, Li; Su, Yu-Sheng (Athabasca University Press, 2022-05-01)
    Within the COVID-19 pandemic and the new normal period, online learning has become one of the main options for learning. Previous studies on self-regulated learning have shown that it was a better predictor of online learning effectiveness. However, this discussion has not been extended to the situation of the COVID-19 pandemic. To address this gap, this study aims to explore the relationship between the three stages of self-regulated learning (SRL) and learning ineffectiveness (LI). Data of 370 high school students were collected during the period of COVID-19. Structural equation modeling was used to perform confirmatory factor analysis on the data. Findings show that the preparatory stage was positively related to the stages of performance and appraisal, and the performance stage was positively related to the appraisal stage; on the other hand, the stages of performance and appraisal were negatively related to learning ineffectiveness. In addition, the preparatory stage had no direct relation to learning ineffectiveness, but the preparatory stage was correlated with learning ineffectiveness, mediated by the stages of performance and appraisal. These results suggest that better performance in the three stages of self-regulated learning decrease learners’ perceived online learning ineffectiveness. This understanding can have implications for global education.
  • Design and Validation of the Virtual Classroom Management Questionnaire A Case Study: Iran

    Keshavarz, Mohsen; Mirmoghtadaie, Zohrehsadat; Nayyeri, Somayyeh (Athabasca University Press, 2022-05-02)
    Effective classroom management methods are well known, but effective ways of managing classes of beginner teachers remain elusive. Classroom management refers to the wide range of skills and techniques that teachers use to ensure that classes are conducted without destructive student behavior. The present study is applied nonexperimental research. The purpose of this study was to design a tool to measure the effective management of the virtual classroom from the perspective of professors and students in e-learning and evaluate its validity and reliability. The research sample was taken randomly from all universities that make use of e-learning in Tehran, Iran, during the 2019–2020 semesters. The results show that the professional development of online classroom management is necessary for preparing teachers to teach in digital environments. The results of this research in the form of a validated questionnaire can be considered as an indicator for educators and students working in online environments, and this tool can be used for effective teaching and learning in the digital age.
  • Designing Asynchronous Online Discussion Forum Interface and Interaction Based on the Community of Inquiry Framework

    Hasani, Lintang Matahari; Santoso, Harry Budi; Junus, Kasiyah (Athabasca University Press, 2022-05-02)
    The community of inquiry (CoI) framework describes a process for creating collaborative learning through three elements or presences: social, cognitive, and teaching. Despite its popularity among researchers and practitioners, use of the CoI model is limited to mapping instructional activities, which are yet to be developed into an interaction design for online collaborative learning intended to support the CoI presences. This study was aimed at developing the interaction design of an asynchronous online discussion forum employing a user-centered design method contextualized to the learning-centered design approach. Seven scenario and user interfaces were created to facilitate one introductory activity and four phases of inquiry. The design was evaluated through contextual interviews with ten students. The interviews revealed that the prototype encouraged and supported (a) introductory activity (social presence), (b) idea exploration (cognitive presence), (c) summarizing the discussion (cognitive presence), and (d) facilitating discussion (teaching presence). Future research could be aimed at improving the proposed design based on recommendations and developing a fully functional working system to be tested in real settings.
  • Editorial - Volume 23, Issue 2

    Blomgren, Constance (Athabasca University Press, 2022-05-01)
  • Using the Critical Incident Questionnaire as a Formative Evaluation Tool to Inform Online Course Design: A Qualitative Study

    Samuel, Anita; Conceição, Simone (Athabasca University Press, 2022-05-02)
    The online instructor plays a prominent role in influencing how students respond to an online course, from designing the course structure, course activities, and assignments to encouraging interaction. Therefore, to develop effective online courses, instructors need robust feedback on their design strategies. Student evaluation of teaching (SET) functions as a summative evaluation of the course design and delivery. Yet, the feedback from SETs can only be integrated into the next iteration of the course, thereby failing to benefit the students who provide the feedback. One suggestion is to use midsemester formative evaluation to inform course design in real time. A qualitative research study was conducted to explore whether the Critical Incident Questionnaire (CIQ) could be an effective formative evaluative tool to inform real-time online course design and delivery. Thematic analysis was conducted on the midcourse evaluations obtained from 70 students in six fully online master’s level courses. There are three key findings from this study. First, CIQ use can provide opportunities for real-time adjustments to online course design and inform future redesign of online courses. Second, responses received via the CIQ prioritize the student voice and experience by focusing on factors that are critical to them. Finally, this deep-dive analysis reinforces the enduring factors that contribute to effective online course design and delivery. A recommendation for practice is to use the CIQ as an effective tool to gather formative feedback from students. This feedback can then be used to adjust course design as needed.
  • Ukrainian E-Learning Platforms for Schools: Evaluation of Their Functionality

    Zhenchenko, Maryna; Melnyk, Oksana; Prykhoda, Yaroslava; Zhenchenko, Igor (Athabasca University Press, 2022-05-02)
    This article defines 27 criteria for evaluating the functionality of e-learning platforms, grouped into three macro groups: (a) learning management, (b) learning content management, and (c) communications and collaboration tools. The proposed criteria can be used to evaluate any e-learning platform’s functionality. They allow teachers and administrators to make conscious choices about the highest-quality e-learning platform for their schools and developers to improve e-learning platforms’ functionality. The developed criteria became the basis for rating the functionality of Ukrainian developers’ eight e-learning platforms' and determining the degree of support (in whole or partly) of e-learning components, categorized on the cognitive, social constructivist, motivation, and e-learning theories (CT, SCT, MT, and E-LT). The results indicate that the lack of communication and collaboration tools necessary to ensure quality distance learning is the main problem of Ukrainian e-leaning platforms. Comparative analysis of the functionality of e-learning platforms and components categorized on the learning theories helped determine that only three of the eight Ukrainian e-learning platforms (Accent [Mobischool], Class Assessment, My Class) fully follow the CT, SCT, and MT, but these platforms are all commercial products; therefore, they only partially support the E-LT. Solving this problem will be facilitated by developing e-learning platforms with open access, financed by the state budget in the context of the development of open and distance learning for Ukrainian students, as well as improving communication and collaboration tools in the context of conforming e-learning components to the social constructivist learning theory.
  • From Physical to Virtual: A New Learning Norm in Music Education for Gifted Students

    Ismail, Md Jais; Anuar, Azu Farhana; Loo, Fung Chiat (Athabasca University Press, 2022-05-02)
    Music education is a subject that is generally thought to have much physical activity involved. However, virtual learning has been mandatary applied to most schools worldwide due to the COVID-19 pandemic. The landscape of music learning has had to be switched to online distance learning (ODL), where students learn music virtually using technological tools. Gifted students are among those affected by the implementation of music ODL throughout 2020. Thus, the purpose of this study is to identify the effectiveness of music ODL on gifted students’ motivation. The researchers framed this quantitative study by involving 81 secondary gifted students, aged 13 years, from 13 states in Malaysia. The sample was selected through random sampling, and a preexperimental design was applied to conduct the study. Respondents had been exposed to the music ODL intervention for a month. Data were collected through an adapted questionnaire, namely, the MUSIC Inventory, with a five-point scale. Data were further analysed by descriptive and inferential statistics, integrating two-way MANOVA, using SPSS Statistics version 23. Results reveal that an ODL approach to music classes is significantly effective to enhance gifted students’ motivation domains of empowerment, usefulness, success, interest, and caring. Yet, no significant difference was found in gifted students’ genders and locations on the four domains. Different approaches in music teaching could be further explored for music ODL to gifted students in future studies.
  • “They Have to Combine the Future of the University and Their Own Future”: OpenCourseWare (OCW) Authoring as an Academic Practice in Spain

    Villar-Onrubia, Ph.D., Daniel (Athabasca University Press, 2022-05-02)
    This study looks at OpenCourseWare (OCW) in Spain, a country where most public universities have tried to promote that particular model of open educational resources (OER) provision among academics. Using three universities with varying levels of OCW activity as a case study, this article examines key drivers behind the implementation of OCW initiatives and unpacks what it means, as an academic practice, to engage in OCW authoring. Following a qualitative case study approach and a multi-methods design, this study offers a basis for theoretical generalisations that can be useful for understanding similar dynamics taking place within different organisational contexts in Spain and beyond. The findings reveal a major disconnect between the drive to implement OCW initiatives in Spain and actual opportunities for academics to engage with them as part of their work. The author concludes that the extrapolation of a highly prescriptive model of OER provision into institutional realities different from the context where it was originally devised—in this case, the Massachusetts Institute of Technology in the United States—is rather problematic. The article also provides some recommendations to university leaders and policy makers, encouraging the creation of alternative models that are mindful of the institutional and cultural specificities of their own contexts and also to take into consideration the social and material realities of the communities they aim to provide with lifelong learning opportunities.
  • Editorial - Volume 23, Issue 1

    Hsu, Ting-Chia; Abelson, Hal; Lao, Natalie (Athabasca University Press, 2022-02-01)
  • An Internet Articles Retrieval Agent Combined With Dynamic Associative Concept Maps to Implement Online Learning in an Artificial Intelligence Course

    Cheng, Yu-Ping; Cheng, Shu-Chen; Huang, Yueh-Min (Athabasca University Press, 2022-02-01)
    Online learning has been widely discussed in education research, and open educational resources have become an increasingly popular way to help learners acquire knowledge. However, these resources contain massive amounts of information, making it difficult for learners to identify Web articles that refer to computer science knowledge. This study developed an Internet articles retrieval agent combined with dynamic associative concept maps (DACMs). The system used text mining technology to analyze keywords to filter computer science articles. In previous research, concept maps were manually constructed; in this study, such maps can be automatically and dynamically generated in real time. In a case study of a fundamental course of artificial intelligence, this study designed two experiments to compare students’ learning behaviors while using this system and the Google search engine. The results of the first experiment showed that the experimental group searched for more knowledge articles on computer science using this agent, compared to the control group using the Google search engine. The learning performance of the experimental group was significantly better than that of the control group, while the cognitive load of the experimental group was significantly lower than that of the control group. Furthermore, the results of the second experiment showed that the learning progress of students using the agent was significantly greater than that of students who used the Google search engine. This illustrates that the agent effectively filtered computer science articles, and DACMs helped students gain a deeper understanding of academic concepts and knowledge related to artificial intelligence.
  • AI in Online-Learning Research: Visualizing and Interpreting the Journal Publications from 1997 to 2019

    Hwang, Gwo-Jen; Tu, Yun-Fang; Tang, Kai-Yu (Athabasca University Press, 2022-02-01)
    This study reviews the journal publications of artificial intelligence-supported online learning (AIoL) in the Web of Science (WOS) database from 1997 to 2019 taking into account the contributing countries/areas, leading journals, highly cited papers, authors, research areas, research topics, roles of AIoL, and adopted artificial intelligence (AI) algorithms. Results indicate that, from 1997 to 2009, AIoL research focused on the combination of intelligent tutoring systems and distance learning. In 2010–2014, AIoL research emphasized learner-oriented learning. In 2015–2019, learner-system interactions to facilitate personalized, adaptive, and collaborative learning became the main focus. “Intelligent tutoring systems” have played the most important role in AIoL, followed by “profiling and prediction,” and “adaptive systems with personalization.” Accordingly, the roles and research trends as well as several suggestions for future research in the field of AIoL are provided as a reference for researchers and policy makers.
  • MOOC Evaluation System Based on Deep Learning

    Tzeng, Jian-Wei; Lee, Chia-An; Huang, Nen-Fu; Huang, Hao-Hsuan; Lai, Chin-Feng (Athabasca University Press, 2022-02-01)
    Massive open online courses (MOOCs) are open access, Web-based courses that enroll thousands of students. MOOCs deliver content through recorded video lectures, online readings, assessments, and both student–student and student–instructor interactions. Course designers have attempted to evaluate the experiences of MOOC participants, though due to large class sizes, have had difficulty tracking and analyzing the online actions and interactions of students. Within the broader context of the discourse surrounding big data, educational providers are increasingly collecting, analyzing, and utilizing student information. Additionally, big data and artificial intelligence (AI) technology have been applied to better understand students’ learning processes. Questionnaire response rates are also too low for MOOCs to be credibly evaluated. This study explored the use of deep learning techniques to assess MOOC student experiences. We analyzed students’ learning behavior and constructed a deep learning model that predicted student course satisfaction scores. The results indicated that this approach yielded reliable predictions. In conclusion, our system can accurately predict student satisfaction even when questionnaire response rates are low. Accordingly, teachers could use this system to better understand student satisfaction both during and after the course.

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