This study explores students’ perceptions of synchronous learning during a period of rapid pedagogical transformation in higher education. Triggered by a systemic shift from traditional face-to-face instruction to online formats, institutions were compelled to redesign their teaching methods, communication channels, and assessment practices. Drawing on qualitative data from 5,154 open-ended responses in Student Evaluations of Teaching (SET) across two Israeli colleges, the research investigates how students experienced and interpreted these abrupt instructional changes. Four overarching themes emerged from the analysis: (1) the development and importance of soft skills, (2) the role of course structure, (3) perceptions of instructional professionalism, and (4) the influence of the digital platform. These findings offer insights into students’ adaptive responses to institutional change and contribute to the understanding of synchronous online learning as a complex pedagogical process.
Generative artificial intelligence (GenAI) has shown potential in supporting academic writing, yet limited research addressed the intention and actual use of this technology by foreign language students. To address this gap, the study employs an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model by integrating AI self-efficacy and AI trust to investigate the factors influencing EFL students’ intention and actual use of GenAI in academic writing. An online survey using a cross-sectional design involving 481 purposively selected EFL students from Indonesian universities was analyzed using partial least squares structural equation modeling (PLS-SEM) with SmartPLS 4. The results revealed that performance expectancy, effort expectancy, social influence, habit, and AI self-efficacy significantly shaped EFL students’ intentions to use GenAI for academic writing, while behavioral intention, habit, and AI self-efficacy significantly influence actual use. In contrast, AI trust, facilitating conditions, and hedonic motivation show no significant effect on either intention or actual use. The model demonstrated strong explanatory power, with R² values of 0.806 for behavioral intention and 0.617 for actual use. The study enhances the explanatory power of UTAUT2 in GenAI-supported academic writing and offers practical insights for pedagogy and policy to promote GenAI literacy, critical evaluation skills, and responsible use in academic writing.
As artificial intelligence (AI) continues to reshape modern society, there is an urgent need to equip K–12 educators with the skills and confidence to integrate AI tools into their instructional practice. This pilot study examined the preliminary effectiveness of an asynchronous professional development course designed to increase educators’ comfort with AI, shift their perceptions, and expand access to instructional resources. A mixed-methods approach was used to analyze data from 30 participants, including K–12 teachers, preservice educators, and others in educational roles in K–12 schools, who completed pre- and post-course surveys. Quantitative results revealed statistically significant increases in comfort with AI, perceptions of AI, access to resources, and familiarity with AI tools. Correlational analyses found strong positive relationships between perceptions, resource access, and comfort. No significant differences were observed across demographic variables; while this does not establish equivalence, it suggests the course may be broadly accessible across participant groups. Qualitative findings, including participant reflections, highlighted the transformative impact of the course, with educators reporting a shift from skepticism to enthusiasm. The course’s modular design, grounded in best practices for professional learning, was well-received and scalable. Recommendations include expanding the course to a larger and more mixed sample, integrating AI training into preservice teacher education, and updating content regularly to reflect the rapidly evolving AI landscape. This research demonstrates that thoughtfully designed, flexible professional development can build AI literacy and empower educators to adopt emerging technologies with confidence and purpose. As data collection is ongoing, these findings represent early trends that will inform future iterations and expanded implementation.
As we bring you another issue of Online Learning, (the third issue of our 30th year of publication by the way), I wanted again to include some reflections on the topic of AI and academic integrity. As noted by many journal editors, we are seeing exponential growth in the number of AI generated submissions to journals. Recently, an essay in the Chronicle of Higher Education by Mya Poe (2026) noted that the influx of AI generated papers was degrading scholarship, making it more difficult for authors of papers to attract citations who were located in the “missing middle”.
Initial cognitive structures (schemata) play a crucial role in the success of Information Systems (IS) in online learning environments, particularly in efforts to support conditions associated with learning equity. However, the IS Success Model has yet to define the role of schemata clearly. This study investigates the influence and position of schemata within the DeLone and McLean framework in the context of online learning equity, with the focus on system-related benefits that may support equitable learning conditions. While learning equity is not measured directly, the model examines perceived benefits, such as accessibility, flexibility, and participation, that are theoretically linked to fair learning opportunities. Data were collected via questionnaires administered to 252 students from two state universities, all of whom had at least one year of online learning experience. The instrument was based on the IS Success Model, which includes system quality, information quality, service quality, intention to use, user satisfaction, net benefits, and schemata. Structural Equation Modeling (SEM) was used to analyze the model. The findings show that key components of the IS Success Model are positively and significantly related to perceived system-related benefits associated with conditions that may support equitable online learning, with schemata functioning as a moderating variable. It is suggested that IS implementations should incorporate support strategies such as scaffolding, personalized guidance, and introductory content to enhance the positive impact of schemata.
This study examines how self-efficacy moderates the relationship between student-content interaction and two key outcomes in Massive Open Online Courses: perceived course quality and sustained learning interest. Using survey data from 343 participants in the Learning How to Learn MOOC on Coursera, we employed structural equation modeling to test main and interaction effects. Participants were predominantly adult learners with diverse educational backgrounds, balanced gender representation, and varied language backgrounds. Results indicate that student-content interaction positively predicts perceived course quality and sustained learning interest. Moderation analyses further show that the positive effects of student-content interaction are strongest for learners with lower self-efficacy and weaker for learners with higher self-efficacy, suggesting a compensatory or ceiling effect. These findings extend prior MOOC research by empirically modeling self-efficacy as a moderating condition rather than only a direct predictor. Practical implications highlight the importance of designing interactive content and self-efficacy-supportive features to promote engagement in large-scale autonomous learning environments. Limitations include reliance on self-reported data and cross-sectional design. Future research should incorporate objective learning analytics and longitudinal approaches to examine how these relationships evolve over time.
This qualitative study explored 41 Arab-Israeli teachers’ perceptions of the transition to online learning during two crises: the COVID-19 pandemic and the Iron Swords War in Israel. Using the Technology Acceptance Model (TAM) as a conceptual framework, the study examined three central dimensions of teachers’ use of digital tools: perceived usefulness, perceived ease of use, and intention to integrate digital tools into future teaching. The findings revealed a marked shift in teachers’ attitudes and practices, from reactive and constrained adoption during the COVID-19 pandemic to more confident and pedagogically grounded use of digital tools during the subsequent crisis. During the pandemic, teachers experienced significant technological and pedagogical challenges, which limited their perceptions of both effectiveness and usability. In contrast, during the war, accumulated experience enabled more confident and effective implementation of digital tools. While initial adoption during COVID-19 was often reactive and fragmented, the subsequent crisis was characterized by greater pedagogical alignment, increased student engagement, and broader willingness among teachers to embed digital tools into their routine teaching. The study highlights the importance of contextual readiness, sustained professional development, and institutional support in fostering long-term technology acceptance in education.
This study sought to identify the significant predictors of students' persistence in blended and online courses in higher education. Drawing from Choi's model (2016), a structural model, comprising eight predictive and three moderating variables, was tested. The sample was composed of 348 students enrolled at two French-speaking Canadian universities who completed an online questionnaire. Data were analyzed using partial least squares structural equation modelling (PLS-SEM) and partial least squares multigroup analysis (PLS-MGA). The findings suggest that learner autonomy, student satisfaction, perception of a Community of Inquiry, and family responsibilities are the primary factors influencing student persistence in blended and online courses. Furthermore, the model explains between 9.3% and 46.4% of the variance in persistence, depending on whether we examine the moderating effect of age, gender or course modality. The paper presents recommendations for institutions and faculty seeking to enhance student persistence in blended or online courses.
Student engagement in online and blended learning environments depends on fostering social presence, yet institutional communication tools often fail to support the informal and dynamic interactions that build a sense of community. While Learning Management System discussion boards provide structured communication, students increasingly turn to external platforms such as Discord, which may offer more effective opportunities for collaboration and engagement. Despite growing interest in student-led communication tools, research comparing their effectiveness with institutional platforms remains limited. This study examines the role of Discord in fostering social presence in a first-year university game design course over six years, comparing its use from 2018 to 2022 with Canvas Discussions in 2023. A longitudinal content analysis of online interactions was conducted to investigate student engagement and the presence of social indicators across both platforms, with illustrative examples included to demonstrate how these indicators manifested in practice. Findings indicated that students posted more frequently and demonstrated significantly higher indicators of social presence when using Discord, whereas student posts on Learning Management System discussion boards showed limited to no indicators of social presence. The analysis also revealed how the nature of social presence shifted over time, shaped by platform features and influenced by external factors such as the pandemic. These results suggest that informal, student-oriented communication platforms may be more effective than institutionally controlled Learning Management System discussion boards in promoting interaction and community building in online learning. The study contributes to discussions on digital pedagogy by highlighting the role of platform selection in shaping student engagement and social presence in higher education.
This study examined the psychometric properties of two primary domains: perceived usefulness (PU) and perceived ease of use (PEOU) in the technology acceptance model (TAM), and their value in determining mobile learning (m-learning) usage among a sample of 220 students with disabilities (SwD) enrolled in a distance education (DE) institution in South Africa. This appears to be one of the first studies in the country to establish internal consistency of the domains among SwD and confirm the two-factor structure using confirmatory factor analysis (CFA; χ²/df = 2.26, CFI = .982, NFI = .969, IFI = .983; TLI = .971, SRMR = .039). These two constructs exhibited factor loadings (λ) ranging from .79 to .95 and produced a structure consistent with the original TAM (Davis, 1989). From a theoretical perspective, the CFA findings reinforce the construct validity of the TAM across various student populations while supporting its cross-cultural applicability in diverse educational settings. However, regression analyses suggested that PU and PEOU were not significant predictors of m-learning usage among SwD (β = .026, t = .300; p = .764; β = -.010, t = -.113, p = .910, respectively), warranting a critical review of the TAM among SwD in DE settings and giving credence to the notion that m-learning usage is deeply embedded in the structural realities of living with a disability and studying at a distance in South Africa. Nonetheless, this study contributes to the limited psychometric research on the TAM in the South African DE sector and establishes a foundation for understanding m-learning usage among SwD in developing contexts.
Disengagement among students learning online in higher education is an enduring challenge that can undermine learning outcomes, retention, and overall satisfaction. While engagement is widely recognized as essential to academic success, the unique conditions of online learning, such as reduced interpersonal interaction, feelings of isolation, and competing external demands, often hinder meaningful participation and enjoyable experiences. Using a hermeneutic phenomenological approach, this study applied flow theory (Csikszentmihalyi, 2008) to better understand the experiences of online students and the barriers to achieving an optimal state of learning. Data were collected through written reflections, interviews, and journaling with Interpretive Phenomenological Analysis (Smith et al., 2022) used to interpret findings of non-flow. The research highlighted how lack of interest, time, resistance to distractions, and prior experience in education prevents students from experiencing flow. This unique, reflective, and human-sensitive methodology encouraged a richer understanding of non-flow and offered insights into the stimuli and inhibitors to flow. The implications for students and higher education providers are encouraging better awareness of distractions and how to focus attention, as well as the potential for gaining experience in industry to nurture interest and personal connection to learning.
Gamification has become a new approach to improving learner motivation and achievement in asynchronous online learning environments. Drawing on the evidence from 28 empirical studies presence in the period 2010-2024, this research work tests the hypotheses testing the effects of gamification on motivation and academic achievement. Adopting the random-effects model, meta-regression and moderator analyses, we found evidence for a moderate positive effect on motivation (Hedges’ g = 0.52) and a small-to-moderate effect on achievement (Hedges’ g = 0.41). The outcomes of hypothesis testing state that narrative-based gamification has an impact significantly greater than points/badges, leaderboards, and that K-12 learners have a significantly greater benefit than higher education students. Year of publication and percentage of female participant are the best predictors of effect sizes. The robustness of findings is confirmed to publication bias and the sensitivity analyses. These findings underscore the utility of the use of gamification to promote engagement in asynchronous online learning and shed light in designing effective interventions.
Nonlinear learning, which aligns with natural thinking and addresses individual needs, has great potential for e-Learning. However, personalization in MOOCs through in-course recommenders remains limited, with only 18% adoption. Therefore, this research addresses on reducing learners’ disorientation by personalizing nonlinear learning paths in MOOCs using a hybrid recommender model, combining ontology and PrefixSpan-based sequential pattern mining. A Moodle-based prototype was tested in a quasi-online experiment with three groups: (1) a comparison group using sequential learning without recommendations, (2) a nonlinear learning group using an ontology- based recommender system, and (3) a nonlinear learning group using a hybrid recommender system. Of 209 registrants, 102 actively participated and provided data consent. The results show that the hybrid recommender group had a higher average efficiency than the ontology recommender group, achieved the highest effectiveness in two of three sub-learning outcomes, and scored best on 22 of 30 e-learning satisfaction variables. Hypothesis testing with analysis of covariance (ANCOVA) revealed significant differences between groups in e-Learning usability, Moodle preference, and learner behavior, though efficiency and effectiveness were not significantly different. Visualization using weighted directed graphs uncovered similar forward- linear learning path patterns across all groups, except for variations in the first three modules of the comparison group. These findings underscore the potential of hybrid recommender systems to improve learner satisfaction and usability in MOOCs. Future research should consider some confounding variables and configurations for hybrid recommender systems to maximize their benefits across various educational settings.
The integration of information and communication technologies (ICTs) in higher education has transformed e-learning, however, challenges such as low course completion rates, limited learner engagement, and insufficient interaction persist. This study aims to develop a competency-based framework for teaching assistants (TAs) in fully online universities, focusing on their role in facilitating engagement and interaction within Learning Management Systems (LMS). Grounded in a constructivist and interpretivist paradigm, the study employed the Delphi method with 15 experienced TAs and 7 faculty members. Data analysis, guided by the Technology Acceptance Model, Social Interaction Theory, and Connectivism Learning Theory, revealed four main themes: technical-professional competencies, information and communication competencies, classroom management and teaching skills, and individual and general competencies, with 12 sub-themes. The framework emphasizes subject matter expertise, research skills, teaching skills, ICT thinking, knowledge, and skills, communication and collaboration, managing student behavior, strategic insight and intuition, creativity and innovation, and ethical and moral competencies. The study contributes to the understanding of the complex roles of TAs in digital education and provides actionable insights for optimizing their training and utilization in e-learning environments, ultimately enhancing the quality and accessibility of online education.
Over the past decade, evidence indicates more higher education (HE) students attracted to studying online. Important publications on this topic illustrate teachers’ and instructors’ growing awareness of the key conditions, elements, and dimensions that increase the likelihood of online student engagement and its impact on student learning and success. However, while studies are starting to emerge that address the online engagement strategies instructors employ and the impact of these practices, there is still a paucity of research that explores the most popular practices employed to support student engagement in HE online learning environments. Drawing on international survey data (N = 115), this study reports on insights from online instructors and learning designers regarding their preferred online engagement practices. Insights from the study provide a practice-informed account of how engagement is operationalised in online learning environments at scale. Rather than evaluating the effectiveness of individual strategies, the study maps dominant patterns of use and identifies three overarching pedagogical approaches—collaborative, cognitive, and humanising practices—that instructors most frequently employed. Importantly, the findings demonstrate that engagement strategies are rarely implemented in isolation; instead, instructors design activities that simultaneously support multiple dimensions of engagement. This paper advances knowledge in online and blended learning by documenting which student engagement strategies are most enacted by online teachers, and how these engagement strategies function collectively in contemporary higher education practice.
This study examines the impact of a Project Management MOOC on self-perceived employability and career self-perception, comparing academic cohorts (AC) who enroll as part of formal education with free auditors (FA) who participate voluntarily. Drawing on a competence-based approach, we assess skill-based employability through dimensions of occupational expertise, anticipation and optimization, and personal flexibility, and evaluate career self-perception via measures of external marketability, career ambition, and motivation. Prior research tends to show that the active population puts importance on skills and social networks, whereas students view MOOC micro-credentials as a means to access the labor market. However, a research gap remains regarding how different learner groups leverage MOOCs for professional development. Data was collected during the course via two surveys administered at pretest (N = 3,059) and posttest (N = 1,357), with paired analyses on 1,301 participants. Results indicate that FA exhibit significantly higher scores in skill-based employability and career motivation at both pre- and post-test, whereas AC demonstrate better scores in external marketability and career ambition, with no significant differences observed in personal flexibility for both groups. These findings highlight that the subdimensions of employability, whether approached from a skill-based or dispositional perspective, do not behave homogeneously but vary depending on contextual factors such as learner group and course subject matter. The study contributes to the integrative employability research agenda and underscores the importance of contextualizing MOOC effects according to learner status. These results highlight the need for further investigation about the mechanisms through which MOOCs enhance practical competencies and influence career self-conception across diverse learner populations, paving the way for future research to better understand how distance learning settings can better foster employability.
This study sought to identify key determinants influencing teachers' satisfaction with the use of a game-based learning application in pre-university education. To achieve this objective, a comprehensive research methodology was employed, integrating components from three theoretical frameworks: the Information Systems Success Model (ISSM), the Technology Acceptance Model (TAM), and the Unified Theory of Acceptance and Use of Technology (UTAUT). The analysis utilized a triangulated approach, leveraging structural equation modeling through SmartPLS 2.0.M3. Empirical data were gathered from a sample of 212 Spanish educators. Results showed that participants were well disposed towards the app and positive about the use of a game-based learning app when its design was clearly curriculum oriented and delivered on the essential curricular elements of content, competences, and education in values. As a predictor of efficacy, participants also appreciated the connection between the challenges students overcame to use the app and the assessment criteria established in the curriculum. Finally, the results for factor invariance were met for the variable of gender but not for age in teachers aged over 45.
Online learning enrollment has grown rapidly worldwide, yet challenges persist in ensuring instruction is effective, engaging, relevant, and accessible for learners from diverse cultural backgrounds. This study systematically reviewed 72 peer-reviewed articles on culturally inclusive online learning published between 2016 and 2023. Using a systematic search, retrieval, coding, and synthesis process, the review found limited attention to underrepresented student populations and emphasized the need for community-based approaches that involve stakeholders in culturally inclusive online learning (CIOL) design and evaluation. The selected literature was informed by educational, cultural, intercultural, critical, and social justice theories, with culturally relevant pedagogy, universal design, community of inquiry, and social constructivism most frequently applied. Learning management systems were shown to support inclusion when paired with practices such as culturally responsive assessments, intercultural learning spaces, and holistic pedagogies, though application remains uneven across disciplines, particularly in STEM. Recommendations highlight expanding research on overlooked populations and advancing consistent integration of CIOL strategies across online course design elements, including objectives, materials, assessments, and interactions.
Blended learning has become central to higher education, yet its capacity to promote deep learning depends on how cognitive, self-regulatory, and social factors are designed across online and face-to-face components. This study examined the relationships among cognitive load, self-regulated learning (SRL), social presence, deep learning, and learning outcomes among 450 Indonesian pre-service teachers in a structured blended learning environment. Data from survey responses and course-performance indicators were analyzed using partial least squares structural equation modeling (PLS-SEM) to test direct and mediated relationships. The results showed that cognitive load positively predicted deep learning but did not directly predict learning outcomes, indicating that cognitive demands can support achievement when they stimulate meaningful processing rather than overload learners. SRL also positively predicted deep learning but had no direct effect on outcomes, suggesting that regulation strategies improve performance mainly when translated into higher-order engagement. Deep learning strongly predicted learning outcomes and mediated the effects of cognitive load and SRL on achievement. Social presence did not significantly predict deep learning and negatively predicted learning outcomes, implying that interaction may be insufficient or distracting when not pedagogically structured. These findings identify deep learning as the central mechanism linking cognitive, self-regulatory, and social factors to academic performance. The study recommends blended learning designs that optimize cognitive challenge, scaffold SRL, and structure social interaction around purposeful inquiry, feedback, and knowledge construction.
With the growing prevalence of online, flexible-delivery higher education programs, supporting students in developing higher-order thinking and the ability to apply theoretical knowledge in practical work situations poses a significant challenge for educators in health disciplines. This exploratory study aims to address the gap in empirical research on how higher-order thinking skills—cognitive presence, self-regulated learning, and learning transfer—interact to influence student learning in online environments. To investigate this, three survey instruments—measuring cognitive presence, self-regulated learning, and learning transfer—were combined into a single online survey questionnaire to evaluate their relationship. Participants are students from multiple courses in a medical sonography postgraduate program, with an online delivery. Their perspectives of their online learning environment are evaluated through quantitative and qualitative data. Responses from 89 out of 262 students (34% response rate) were analyzed using descriptive and inferential statistics and qualitative deductive analysis. Results suggest that students perceived stronger development in cognitive presence and learning transfer skills compared to self-regulated learning. Students perceived real-life case studies and presentations as the most relevant summative assessments for applying knowledge in the workplace. Qualitative data identified four themes related to the development of higher-order thinking skills that support learning transfer. Results show a statistically significant correlation between cognitive presence, self-regulated learning, and learning transfer. No significant differences were found across demographic groups in relation to the study variables, except for training hours per week. The key contribution of this study lies in offering an initial understanding of student perspectives on cognitive presence, self-regulated learning, and learning transfer in online environments. The limitations of this study point to areas requiring further research.
