Search Results (169)

Background/Objectives: Health education plays a vital role in training health professionals and caregivers, supporting both prevention and the promotion of self-care. In this context, technology serves as a valuable ally by enabling continuous and flexible learning. Among the various domains of health education, nutrition stands out as a key element in the management of Amyotrophic Lateral Sclerosis (ALS), helping to prevent malnutrition and enhance patient well-being. Accordingly, this study aimed to evaluate the effectiveness of the teaching and learning processes within a learning pathway focused on food and nutrition in the context of ALS. Methods: This study adopted a longitudinal, quantitative design. The learning pathway, titled “Food and Nutrition in ALS,” consisted of four self-paced and self-instructional Massive Open Online Courses (MOOCs), offered through the Virtual Learning Environment of the Brazilian Health System (AVASUS). Participants included health professionals, caregivers, and patients from all five regions of Brazil. Participants had the autonomy to complete the courses in any order, with no prerequisites for enrollment. Results: Out of 14,263 participants enrolled nationwide, 182 were included in this study after signing the Informed Consent Form. Of these, 142 (78%) completed at least one course and participated in the educational intervention. A significant increase in knowledge was observed, with mean pre-test scores rising from 7.3 (SD = 1.8) to 9.6 (SD = 0.9) on the post-test across all courses (p < 0.001). Conclusions: The self-instructional, technology-mediated continuing education model proved effective in improving participants’ knowledge about nutrition in ALS. Future studies should explore knowledge retention, behavior change, and the impact of such interventions on clinical outcomes, especially in multidisciplinary care settings. Full article

Low achievement in mathematics across educational levels has long been a concern for researchers. Recent evidence points to equipping teachers with skills and competencies that align with the demands of the modern, technology-rich world. This systematic review explored how technology-facilitated heutagogical practices contribute to the professional development of preservice and in-service mathematics teachers. Drawing on 21 empirical studies published between 2017 and 2024, this review identified three major findings. First, technology-enhanced heutagogical practices promote teaching skills by fostering learner autonomy, self-reflection, and professional identity development. Second, tools such as mobile apps, Massive Open Online Courses (MOOCs), adaptive learning platforms, and collaborative digital environments support the integration of heutagogical principles. Third, implementation is challenged by limited digital access, institutional constraints, and the need for gradual adaptation to self-determined learning models. These findings prove the need for policy and institutional investment in digital infrastructure, blended learning models, and teacher support. Theoretically, this study affirms heutagogy as a relevant pedagogical approach for preparing mathematics teachers in dynamic learning contexts. There is also a need for more empirical studies to investigate scalable models for technology-driven heutagogy, especially in resource-constrained settings. Full article

High dropout rates remain a significant challenge in Massive Open Online Courses (MOOCs), making early identification of at-risk students crucial. This study introduces a novel approach called In-Session Stacked Ensemble Learning for Dropout Prediction (ISELDP), which predicts student dropout during course sessions by combining multiple base learners—Adaptive Boosting (AdaBoost), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting—into a stacked ensemble with a Multi-Layer Perceptron (MLP) serving as the meta-learner. To optimise model performance, hyperparameters were tuned using Grid Search. The proposed method was evaluated under two scenarios using in-session student interaction data, one with imbalanced data and another with balanced data. Results demonstrate that ISELDP achieves an average accuracy of 88%, outperforming individual baseline models with improvements of up to 2% in accuracy and 2.4% in F1-score. Full article

This article carries out an interdisciplinary analysis of five MOOC courses developed by the University of Valladolid and offered on higher education platforms between 2020 and 2024. This research is based on the study of the lexical categories used by the informants participating in these courses, establishing a correlation with the theoretical and practical debates surrounding the definition of heritage and the frameworks of contemporary heritage education. Through a metalinguistic approach, the semantic limits of the emerging lexical categories are examined, paying attention to their ambiguity, polysemy and contexts of use, both from a formal linguistic perspective and from a hermeneutic approach. The analysis is based on natural language processing tools, complemented by qualitative techniques from applied linguistics and cultural studies. This dual approach, both scientific–statistical and humanistically nuanced, allows us to identify recurrent discursive patterns, as well as significant variations in the conceptualization of heritage according to the socio-cultural and geographical profiles of the participants. The results of the linguistic analysis are contrasted with the thematic lines investigated by our research group, focusing on cultural policy, legacy policies, narratives linked to the culture of depopulation, disputed scientific paradigms, and specific lexical categories in the Latin American context. In this sense, the article takes a critical look at discursive production in massive online learning environments, positioning language as a key indicator of the processes of cultural resignification and the construction of legacy knowledge in the Ibero-American context. The findings of my scientific article underscore the pressing need for a multiform liberation of the traditionally constrained concept of heritage, which has long been framed within rigid institutional, legal, and disciplinary boundaries. This normative framework, often centered on materiality, monumentalism, and expert-driven narratives, limits the full potential of heritage as a relational and socially embedded construct. My research reveals that diverse social agents—ranging from educators and local communities to cultural mediators and digital users—demand a more flexible, inclusive, and participatory understanding of heritage. This shift calls for redefining legacy not as a static legacy to be preserved but as a dynamic bond, deeply rooted in affective, symbolic, and intersubjective dimensions. The concept of “heritage as bond”, as developed in contemporary critical theory, provides a robust framework for this reconceptualization. Furthermore, the article highlights the need for a new vehiculation of access—one that expands heritage experience and appropriation beyond elite circles and institutionalized contexts into broader social ecosystems such as education, digital platforms, civil society, and everyday life. This approach promotes legacy democratization, fostering horizontal engagement and collective meaning-making. Ultimately, the findings advocate for a paradigm shift toward an open, polyphonic, and affective heritage model, capable of responding to contemporary socio-cultural complexities. Full article

The integration of artificial intelligence (AI) chatbots in online learning environments has transformed the way students engage with educational content, offering personalised learning experiences, instant feedback, and scalable support. This study investigates the role of AI-driven chatbots in the Pedagogical Information and Communication Technology (ICTPED) Massive Open Online Course (MOOC), a professional development course aimed at enhancing teachers’ Professional Digital Competence (PDC). The study pursues two connected aims: (1) to examine how chatbots support content comprehension, self-regulated learning, and engagement among pre- and in-service teachers, and (2) to explore, through a cultural-historical perspective, how chatbot use contributes to the development of students’ digital agency. Based on data from 46 students, collected through structured questionnaires and follow-up interviews, the findings show that chatbots functioned as interactive learning partners, helping students clarify complex concepts, generate learning resources, and engage in reflection—thereby supporting their PDC. At the same time, chatbot interactions mediated learners’ development of digital agency, enabling them to critically interact with digital tools and navigate online learning environments effectively. However, challenges such as over-reliance on AI-generated responses, inclusivity issues, and concerns regarding content accuracy were also identified. The results underscore the need for improved chatbot design, pedagogical scaffolding, and ethical considerations in AI-assisted learning. Future research should explore the long-term impact of chatbots on students’ learning and the implications of AI-driven tools for digital agency development in online education. Full article

Introduction: The educational landscape has been expanded to disadvantaged and distant areas through online courses. These online courses have gained extensive interest yet there are limited studies available in the literature. The emergence of massive open online courses (MOOCs) has allowed sustainability educators to glimpse the light. Online education is gaining popularity and, with the introduction of MOOCs, would be beneficial for knowledge building and sharing, and the development of learned society. Objective: This study investigated the mediating (indirect) effects of media richness and user-based use motives on the extended UTAUT model, use behavior (UB), and actual use (AU) of MOOCs on health informatics and administration sustainability education among educators and students in Saudi higher learning institutions (HLIs). A theoretical model based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and Channel Expansion Theory (CET) was used to investigate the factors that affect the adoption of MOOCs in health informatics and administration education. Methodology: A survey design approach was applied. Cross-sectional data were collected from health informatics educators and students from HLIs in Saudi Arabia. A non-probability convenience sampling technique was used for sampling. Data were collected online using Google Forms. A total of 145 completed questionnaires were used in the analysis. PLS-SEM(Version 4.1.1.2) was used for statistical analysis. To investigate the reliability and validity, a measurement model was developed and confirmatory factor analysis (CFA) was conducted. To test the hypotheses, a structural model was run using bootstrapping, coefficients, standard errors (SE) t-values, p values, and lower and upper-level confidence intervals. Results/Findings: The findings were that system quality and user satisfaction is an important factor in the UTAUT model and the inclusion of media richness and user-based use motives significantly mediated between the expanded UTAUT model and the UB and AU of MOOCs. Moreover, media richness and user-based use motives were found to be dominant factors in the overall study model to predict use behavior and actual use of health informaticians in Saudi Arabia. Conclusions: The combination of these two theories i.e., the UTAUT and CET, can effectively enhance the adoption, use behavior, and actual use of MOOCs in the emerging field of health informatics in Saudi Arabia. Full article

Massive Open Online Courses (MOOCs) are a relatively new educational model that provides free access to educational content regardless of location or time. Despite these benefits, MOOCs encounter significant challenges, such as low completion rates, high dropout rates, and inconsistent participant comprehension, often due to the absence of simulations and practical activities. Incorporating sustainable education principles into MOOCs could bring benefits to the long-term effectiveness of the learning process, ensuring conscious learning practices. To address the issue of MOOC dropout rates and assess the scientific interest in this area, a bibliometric analysis was conducted on a dataset of 193 papers sourced from the ISI Web of Science database, spanning from 2013 to 2023. Papers were selected based on relevant keywords for the study. The analysis highlights key academic institutions, leading authors, and publication trends within this field. It reveals a strong and growing interest in MOOC dropout rates, with an annual growth rate of 40.04%. Research trends were identified by analyzing n-grams from keywords, titles, abstracts, and keywords plus, supplemented by a detailed review of the most cited papers globally. A collaborative network analysis was performed to explore author collaborations, their global distribution, institutional affiliations, research objectives, and study directions. The findings from the most cited papers show an increasing focus on understanding the factors contributing to MOOC dropout rates and developing strategies to address these issues. Full article

Massive Online Open Courses (MOOCs) have been growing rapidly in the past few years. Video content is an important carrier for cultural exchange and education popularization, and needs to be translated into multiple language versions to meet the needs of learners from different countries and regions. However, current MOOC video processing solutions rely excessively on manual operations, resulting in low efficiency and difficulty in meeting the urgent requirement for large-scale content translation. Key technical challenges include the accurate localization of embedded text in complex video frames, maintaining style consistency across languages, and preserving text readability and visual quality during translation. Existing methods often struggle with handling diverse text styles, background interference, and language-specific typographic variations. In view of this, this paper proposes an innovative cross-language style transfer algorithm that integrates advanced techniques such as attention mechanisms, latent space mapping, and adaptive instance normalization. Specifically, the algorithm first utilizes attention mechanisms to accurately locate the position of each text in the image, ensuring that subsequent processing can be targeted at specific text areas. Subsequently, by extracting features corresponding to this location information, the algorithm can ensure accurate matching of styles and text features, achieving an effective style transfer. Additionally, this paper introduces a new color loss function aimed at ensuring the consistency of text colors before and after style transfer, further enhancing the visual quality of edited images. Through extensive experimental verification, the algorithm proposed in this paper demonstrated excellent performance on both synthetic and real-world datasets. Compared with existing methods, the algorithm exhibited significant advantages in multiple image evaluation metrics, and the proposed method achieved a 2% improvement in the FID metric and a 20% improvement in the IS metric on relevant datasets compared to SOTA methods. Additionally, both the proposed method and the introduced dataset, PTTEXT, will be made publicly available upon the acceptance of the paper. For additional details, please refer to the project URL, which will be made public after the paper has been accepted. Full article

Massive Open Online Courses (MOOCs) have become increasingly prevalent in higher education, with the COVID-19 pandemic further accelerating their integration, particularly in developing countries. While MOOCs offered a vital solution for educational continuity during the pandemic, factors influencing students’ sustained engagement with them remain understudied. This longitudinal study examines the factors influencing learners’ sustained engagement with ThaiMOOC, incorporating demographic characteristics, usage log data, and key predictors of adoption and completion. Our research collected primary data from 841 university students who enrolled in ThaiMOOC as a mandatory curriculum component, using online surveys with open-ended questions and post-course usage log analysis. Logistic regression analysis indicates that adoption intention, course content, and perceived effectiveness significantly predict students’ Actual Continued Usage (ACU). Moreover, gender, prior MOOC experience, and specific usage behaviors emerge as influential factors. Content analysis highlights the importance of local language support and the desire for safety during the COVID-19 pandemic. Key elements driving ACU include video design, course content, assessment, and learner-to-learner interaction. Full article

Introduction: Massive Open Online Courses (MOOCs) are an agile context for workplace training, which can provide physicians with needed knowledge and skills related to their clinical practice. From an organisational standpoint, their effectiveness can be assessed on physicians’ intention to transfer what they learn through them in the workplace. Despite the Theory of Planned Behaviour (TPB) standing among the more solid models in explaining individuals’ behavioural intention, its adoption in investigating the training transfer process among physicians is notably underdeveloped, limiting its contribution to enhancing the transfer rates of MOOCs content. Method: Based on such a consideration, the present study adopted an extended TPB model to investigate the potential psychosocial factors affecting the intention to transfer knowledge and skills learned through MOOCs in the workplace among 217 Italian physicians who completed an online self-report questionnaire. Results: Hierarchical regression analyses were performed, showing that among the TPB variables, perceived behavioural control and attitude significantly predicted transfer intention. The inclusion of training satisfaction and job autonomy in the model significantly increased the explained variance in intention. Conclusions: These findings have valuable implications for human resource development (HRD) in healthcare as they suggest the need to design MOOCs based on a flexible competency model in order to promote physicians’ engagement and commitment to adopting new knowledge and skills. Finally, interventions aimed at recognising physicians’ efforts in transferring learned content as well as empowering their ability to shape how they perform their professional duties could further enhance the impact of workplace training. Full article

Massive open online courses have revolutionised the learning environment, but their effectiveness is undermined by low completion rates. Traditional dropout prediction models in MOOCs often overlook complex factors like temporal dependencies and context-specific variables. These models are not adaptive enough to manage the dynamic nature of MOOC learning environments, resulting in inaccurate predictions and ineffective interventions. Accordingly, MOOCs dropout prediction models require more sophisticated artificial intelligence models that can address these limitations. Moreover, incorporating feature selection methods and explainable AI techniques can enhance the interpretability of these models, making them more actionable for educators and course designers. This paper provides a comprehensive review of various MOOCs dropout prediction methodologies, focusing on their strategies and research gaps. It highlights the growing MOOC environment and the potential for technology-driven gains in outcome accuracy. This review also discusses the use of advanced models based on machine learning, deep learning, and meta-heuristics approaches to improve course completion rates, optimise learning outcomes, and provide personalised educational experiences. Full article

The log files of Massive Open Online Courses (MOOCs) reveal useful information that can help interpret student behavior. In this study, we focus on student performance based on their access to course resources and the grades they achieve. We define states as the Moodle resources and quiz grades for each student ID, considering participation in resources such as wikis and forums. We use efficient Hidden Markov Models to interpret the abundance of information provided in the Moodle log files. The transitions among certain resources for each student or groups of students are determined as behaviors. Other studies employ Machine Learning and Pattern Classification algorithms to recognize these behaviors. As an example, we visualize these transitions for individual learners. Additionally, we have created row and column charts to present our findings in a comprehensible manner. For implementing the proposed methodology, we use the R programming language. The dataset that we use was obtained from Kaggle and pertains to a MOOC of 4037 students. Full article

In the context of 21st-century educational transformation, Massive Open Online Courses have emerged as a global and flexible learning opportunity. This study explores the impact of MOOC implemented at Instituto Super Técnico on student engagement and attendance, focusing on their effectiveness in higher education. A descriptive approach was applied, selecting three MOOC as the empirical field, combining quantitative data from participation logs and qualitative feedback from student surveys. The key characteristics, the challenges, and the effectiveness of educational resources were evaluated, particularly concerning student interaction and perceptions of course quality. The pedagogical analysis highlighted both successful strategies and areas for improvement. The findings offer valuable insights into the influence of MOOC on students and the institution, contributing to the broader understanding of Portugal’s educational landscape. This study also proposes a framework for enhancing MOOC strategies in higher education globally, considering the potential of technological innovations in the 21st century. Full article

Massive Open Online Courses (MOOCs) offer highly specialized online courses and have attracted nearly 10 million learners worldwide to participate in various educational programs. These platforms provide discussion forums that allow learners to engage with both their peers and instructors, facilitating idea exchange and seeking assistance, respectively. However, due to the substantial participant-to-instructor ratio, certain posts may go unanswered. Addressing learners’ confusion is crucial. This emotional state, often experienced during the learning journey, necessitates prompt support to prevent potential dropouts. This paper proposes the application of a deep transfer learning method to automate the classification of online discussion posts based on indicators of confusion utilizing the Stanford MOOCPost dataset. The approach involves creating an explainable and adaptable deep learning model through network-based transfer learning across multiple educational domains. This model outperforms baseline methods, achieving an average accuracy of 91%. Additionally, employing data augmentation techniques enhances the model’s generalizability, resulting in an 11% improvement in the F1 score. To mitigate the inherent opacity of the implemented models, Local Interpretable Model-Agnostic Explanation and Shapley Additive Explanation techniques are integrated. These explanations assess the reliability of features and provide supplementary insights into the confusion detection. By pinpointing confused posts, this work assists instructors in delivering timely responses, resolving learner confusion, providing accurate visualization of key contributing words, and reducing the dropout rate. This proactive approach ensures a smoother continuation of the learning process, consequently enhancing learner satisfaction with the educational experience. Full article

Massive Open Online Courses (MOOCs) provide borderless opportunities to engage with content and ideas, with prospective participants from around the globe being able to easily register. The course featured in this study focused on the social and emotional well-being of adolescents, selected because of a recognized need for professional learning related to this topic. It was purposely designed for delivery as a MOOC and was designed as a 6h program around four topics to be completed over two weeks in asynchronous mode. It was delivered seven times from 2019 to 2023, with 32,969 individuals enrolled across these deliveries. The effectiveness of engaging in the course for professional learning purposes is of particular interest to this study. To that end, a convergent mixed methods study was conducted. First, quantitative and qualitative survey data collected at various course stages were examined to reveal the demographic characteristics of participants and their experiences in the course using data from surveys and comments about their experiences. The findings revealed, among other factors, that 65% were female, with just under half (47%) aged 45 years or less, nearly half (44%) held a bachelor’s degree as their highest level of qualification, and 48% were employed within the teaching and education sector. The most active learners were from Europe (48%) and Asia (27%), with active learners from a total of 178 countries. The course has a high course retention index, with 51% of learners completing 51% of the course and 8383 learners completing 90% or more of the course. The qualitative findings reveal the strongly positive experiences reported by the active participants. Secondly, we examined the effectiveness of the MOOC for participants’ professional learning needs by assessing the course using a framework with ten domains related to its core design features, modified for use by the course designers as a self-reflective tool. We found that the domains that scored the lowest were collaboration, interactivity, and, to a lesser extent, pedagogy. The study’s limitations include the incomplete data provided as part of the MOOC protocols, and the use of a self-reflection tool, which may inadvertently incorporate bias. This study points to these gaps in the data, including the need to access longitudinal data that go beyond a focus on the design of courses to extend to the impact and outcomes of the experience. Full article