Search Results (255)

Massive Open Online Courses (MOOCs) serve as catalysts for sustainable education by democratizing access to lifelong learning. While this potentially positions them as a key driver of the United Nations Sustainable Development Goal 4 (SDG 4), their long-term impact depends heavily on the implementation of inclusive design and ethical governance. This study evaluates the social sustainability of the AKADEMA platform—defined through equity of access, institutional trust, and long-term learner retention—using Badrul Khan’s e-learning framework. Employing a multi-layered mixed-methods design, the study triangulates subjective user perceptions—gathered via quantitative surveys (N = 209; a convenience sample of 6140 contacted users) and qualitative insights (n = 122)—with objective structural evidence from a technical accessibility audit. Although the results indicate high satisfaction with pedagogical quality, the findings reveal specific structural nuances regarding platform inclusivity and user diversity. Specifically, data triangulation highlights a notable ‘privacy awareness gap’—where working professionals demonstrate higher sensitivity regarding data governance than learners—alongside structural barriers hindering ‘Universal Design’ for learners with disabilities. Consequently, to strengthen the sustainability of open education models, future strategies should emphasize digital equity and institutional trust, ensuring that technical environments align with the promise of inclusive quality education. Full article

This study investigates how integrating the 4PADAFE instructional design methodology with generative artificial intelligence (GAI) tools helps develop innovative, pedagogically sound digital learning environments in higher education. To meet the demand for scalable and flexible instructional models, 4PADAFE offers a seven-phase, iterative framework that connects pedagogical goals with the creative use of AI-powered tools. Using a qualitative exploratory approach, 20 Systems Engineering students applied the methodology to collaboratively create a four-week Massive Open Online Course (MOOC) titled “Generative Artificial Intelligence Tools for University Teaching.” They utilized ChatGPT, DALL·E, and Gamma to produce educational materials without direct input from subject-matter experts. Data collection included semi-structured interviews, non-participant observation, and analysis of student-created artifacts. The findings revealed increased learner autonomy, creativity, and digital skills, along with more efficient instructional design processes supported by prompt engineering and real-time feedback. The structured 4PADAFE framework helped participants align AI-generated content with specific learning outcomes while maintaining ethical safeguards. This study concludes that, with proper guidance and a systematic framework, students with technical backgrounds can serve as effective instructional designers, demonstrating the potential of combining structured methodologies and GAI to democratize high-quality course development in digital higher education. Full article

Small Private Online Courses (SPOCs) have gained attention as a promising approach to blended learning in higher education, particularly within the Flipped Classroom framework. Unlike Massive Open Online Courses (MOOCs), SPOCs cater to a limited number of students, allowing for more personalized learning experiences and enhanced interaction with instructors. This case study examines the integration of a SPOC titled “Introduction to Quantum Physics” into the undergraduate course “Introduction to Modern Physics” at the University of Crete. The research employs a mixed-methods approach, combining quantitative and qualitative data collection methods. Quantitative data were obtained from a questionnaire distributed to students and an analysis of student grades, while qualitative insights were derived from interviews with the course instructors. The findings indicate that the SPOC was associated with positive student engagement and comprehension of complex physics concepts, aligning with previous research on blended learning effectiveness. However, challenges were identified, including the need for increased student–instructor interaction in the online component. Recommendations for improving the SPOC model include the development of interactive activities and enhanced instructor support. This study aims to contribute to the growing body of research on the Flipped Classroom framework in higher education, highlighting the potential utility of SPOCs to enrich learning experiences. Full article

This paper aims to study how heutagogy relates to multilingualism and distance education regarding augmenting learner autonomy, meta-linguistic awareness, and overall learning objectives. As such, in our modern age, pedagogical models have shifted their focus and promote self-regulation and flexible learning of students, focusing on broad principles such as andragogy and heutagogy. This means that the weight is shifted over the trainee to the trainer to actively co-create knowledge that aligns with his/her objectives while using modern tools and processes such as distance learning environments and other digital resources. Our article reviews international publications from 2020 to 2025 to provide a more recent and modern approach and highlights findings from approximately 40 key publications that explore the application of heutagogical and self-determining core values in multilingual online learning. The results of our study were generated based on some preset criteria that aimed to measure the degrees of autonomy and intrinsic motivation, evaluate metacognitive and metalinguistic development, and assess the contribution of technological advancements such as MALL tools, AI, and digital learning ecosystems. Finally, the challenges faced in our study suggested limitations in terms of digital inequality, learning readiness, and difficulties in educators’ training. All the above can be tackled by the heutagogy model in distance multilanguage education when and if supported by the necessary cultural awareness, pedagogical strategies, and most importantly, technological training and infrastructure of all participating parties. Full article

Massive Open Online Courses (MOOCs) have emerged as a popular choice for learners as accessible and flexible education across the globe. Micro -are short skill-focused certifications offered within MOOCs to online learners. The interplay between multiple stakeholders, including universities, MOOCs providers, policy makers and industrial leaders, plays a decisive role in MOOC adoption. This study employed Educational Data Mining techniques to extract patterns in learner behavior, course design, institutional collaboration, etc., from the determinants of adoption and completion of the micro-credentials within MOOCs. The determinants were extracted from major online MOOCs databases, whereas additional parameters not captured in these databases were collected through an online survey from learners, industry professionals, and higher education institutions. A data mining-based framework is proposed to support stakeholders in planning effective course offerings, guiding learners in selecting suitable courses and helping MOOCs providers to align course credentials with market demands. Classification and predictive analysis revealed that course-related attributes, such as course certification type, course organization, course rating, course difficulty level, and whether the course was free or paid, play decisive roles in determining MOOC adoption. The decision tree classifier, based on the information gain and Gini index, ranked these attributes by order of preference with high accuracy, whereas regression analysis predicted multiple independent variables yielding good performance, as reflected in the confusion matrix. Full article

Knowledge tracing (KT), which uses machine learning models to predict students’ future performance, has received a lot of attention in intelligence education. However, in Massive Open Online Courses (MOOCs), most of the existing KT methods can only track students’ performance in one course. In addition, when there is scant learning record data on new courses, training a new KT model becomes challenging. Furthermore, existing KT methods tend to excel in specific courses, but their generalization ability is inferior when faced with similar or distinct courses. To address these challenges, this paper proposes a MOOC-oriented cross-course knowledge tracing model (MCKT). In MCKT, we first construct two attribute relationship graphs to obtain the student and KC representations from the source course and the target course, respectively. Then, the element-wise attention mechanism is used to fuse the student representations from both courses. Next, MLPs are used to reconstruct the interaction between students and KCs in each course to enhance students’ cross-course representation. Finally, recurrent neural networks (RNNs) are used to predict the students’ performance in each course. Experiments show that our proposed approach outperforms existing KT methods in predicting students’ performance across diverse MOOCs, proving its effectiveness and superiority. Full article

With the rapid development of Massive Open Online Courses (MOOCs), high dropout rates have become a major challenge, limiting the quality of online education and the effectiveness of targeted interventions. Although existing MOOC dropout prediction methods have incorporated deep learning and attention mechanisms to improve predictive performance to some extent, they still face limitations in modeling differences in course difficulty and learning engagement, capturing multi-scale temporal learning behaviors, and controlling model complexity. To address these issues, this paper proposes a MOOC dropout prediction model that integrates multi-scale convolution with a symmetric dual-attention mechanism, termed SDA-Net. In the feature modeling stage, the model constructs a time allocation ratio matrix (MRatio), a resource utilization ratio matrix (SRatio), and a relative group-level ranking matrix (Rank) to characterize learners’ behavioral differences in terms of time investment, resource usage structure, and relative performance, thereby mitigating the impact of course difficulty and individual effort disparities on prediction outcomes. Structurally, SDA-Net extracts learning behavior features at different temporal scales through multi-scale convolution and incorporates a symmetric dual-attention mechanism composed of spatial and channel attention to adaptively focus on information highly correlated with dropout risk, enhancing feature representation while maintaining a relatively lightweight architecture. Experimental results on the KDD Cup 2015 and XuetangX public datasets demonstrate that SDA-Net achieves more competitive performance than traditional machine learning methods, mainstream deep learning models, and attention-based approaches on major evaluation metrics; in particular, it attains an accuracy of 93.7% on the KDD Cup 2015 dataset and achieves an absolute improvement of 0.2 percentage points in Accuracy and 0.4 percentage points in F1-Score on the XuetangX dataset, confirming that the proposed model effectively balances predictive performance and model complexity. Full article

Learner engagement is a pivotal factor affecting the effectiveness of Massive Open Online Courses (MOOCs), as it promotes collaborative learning environments. However, measuring the extent of learners’ contributions in MOOC learning forums presents challenges due to the complex nature of engagement and its variability. Given the limited research in this domain, further investigation is necessary. This study aims to address this gap by utilizing the Hidden Markov Model (HMM) to identify latent states of MOOC learners and improve their participation in learning forums. The study constructs a multidimensional observable signal sequence based on learner-generated post data from MOOC forums, with a particular focus on the widely attended course on a MOOC platform. To evaluate the predictive accuracy of HMM in forecasting learner contributions, the study employs several prominent prediction models for comparative analysis, including k-nearest neighbor, logistic regression, random forest, extreme gradient boosting tree, and the long short-term memory network. The results demonstrate that HMM provides superior accuracy in predicting learner contributions compared to other models. These findings not only validate the effectiveness of HMM but also offer significant insights and recommendations for enhancing forum management practices. This research represents a substantial advancement in addressing the challenges related to learner engagement in MOOC learning forums and underscores the potential benefits of employing the HMM approach in this context. Full article

This research develops and verifies a structural model of enrollment intention for Thai MOOC, the national learning platform, based on empirical data from a survey of 475 learners. Partial least squares structural equation modeling (PLS-SEM) was used to analyze the data, and the results indicated a strong model with high predictive capability. Among the seven dimensions of the service marketing mix, product, promotion, process and place had a significantly positive association with brand image perception. In contrast, perceived value, people, and physical evidence had no significant relation. Brand image perception was established as a mediator, representing the channel through which the significant marketing mix factors associated with the intention to enroll in Thai MOOC. These findings suggest that to induce enrollment, government-backed MOOCs should focus on content quality and platform accessibility ahead of conventional service aspects, while utilizing promotion to establish a strong brand image. Full article

Research on learner profiling in education primarily focuses on utilizing students’ personal characteristics and behavioral data to depict their learning status and traits. However, existing methods often face challenges such as incomplete data and difficulties in feature extraction, leading to incomplete and less accurate learner profiles. To address information gaps in learner profiles within educational datasets, this study proposes a profile completion technique based on relation-aware heterogeneous graph networks. Using the MOOCCube and MOOCCubeX datasets, we trained a relation-aware heterogeneous graph network model to predict students’ age and gender. The model achieved significant advancements in gender prediction. While age prediction performance remains relatively low—a common challenge in the field due to the subtle and multifaceted nature of age-related behavioral signals—ablation studies confirm the model’s robustness, demonstrating stable gender prediction accuracy even with significant data reduction. This work bridges information gaps in learner profiling within educational datasets, providing crucial support for personalized education and teaching quality improvement. It showcases the potential application of relation-aware heterogeneous graph networks in education and offers new ideas for research utilizing heterogeneous graph networks for learner profiling. Full article

This article investigates the use of digital technologies for visualizing educational content in programming education, with a particular focus on developing logical thinking skills. In the context of rapid GenAI development—where AI can both support and hinder cognitive engagement—the study proposes a new instructional model (MDLTP). The model integrates visualization-based learning, scaffolding, spiral learning, and GenAI-supported tools into a unified end-to-end digital ecosystem tailored to novice programmers. The purpose of this study is to develop and evaluate a digital instructional model that effectively fosters logical thinking in novice programmers through visualization technologies and GenAI-supported tools. The novelty of the study lies in the systematic alignment of Bloom’s taxonomy with digital tools, the structured integration of algorithm-visualization technologies into a MOOC + Moodle environment, and the defined pedagogical boundaries for appropriate GenAI use in early-stage cognitive development. Statistical analysis of learning outcomes and survey data from 329 students confirms the effectiveness of the proposed approach in enhancing motivation, comprehension, and logical reasoning. Full article

Massive Open Online Courses (MOOCs) have gained increasing popularity in recent years, highlighting the growing importance of effective course recommendation systems (CRS). However, the performance of existing CRS methods is often limited by data sparsity and suffers under cold-start scenarios. One promising solution is to leverage course-level conceptual information as side information to enhance recommendation performance. We propose a general framework for integrating LLM-generated concepts as side information into various classic recommendation algorithms. Our framework supports multiple integration strategies and is evaluated on two real-world MOOC datasets, with particular focus on the cold-start setting. The results show that incorporating LLM-generated concepts consistently improves recommendation quality across diverse models and datasets, demonstrating that automatically generated semantic information can serve as an effective, reusable, and scalable source of side knowledge for educational recommendations. This finding suggests that LLMs can function not merely as content generators but as practical data augmenters, offering a new direction for enhancing robustness and generalizability in course recommendation. Full article

Course recommendation is a critical service in Intelligent Tutoring Systems (ITS) that helps learners discover relevant courses from massive online educational platforms. Despite substantial progress in this field, two key challenges remain unresolved: (1) existing methods fail to leverage the differences in learners’ interests across different courses during knowledge propagation processes, and (2) while sequential relationships have been considered in course recommendations, there is still significant room for improvement in effectively integrating sequential patterns with knowledge-graph-based approaches. To overcome these limitations, we propose PGDB (Preference-aware Graph Diffusion network and Bi-LSTM), an innovative end-to-end framework for course recommendation. Our model consists of four key components: First, a course knowledge graph diffusion module recursively collects multiple knowledge triples related to learners to construct their knowledge background. Second, a preference-aware diffusion attention mechanism analyzes learners’ preferences for courses and relational paths using multi-head attention, effectively distinguishing semantic diversity across different contexts and capturing varying learner interests during knowledge transmission. Third, a temporal sequence modeling module utilizes bidirectional long short-term memory networks to identify learners’ interest evolution patterns, generating learner-dependent representations that efficiently leverage sequential relationships between courses. Finally, a prediction module combines the final representations of learners and courses to output selection probabilities for candidate courses. Extensive experimental results demonstrate that PGDB significantly outperforms state-of-the-art baseline models across multiple evaluation metrics, validating the effectiveness of our approach in addressing data sparsity and sequential modeling challenges in course recommendation systems. Full article

With the rapid advancement in smart city infrastructures, the demand for personalized and explainable educational services has become increasingly prominent. To address the challenges of information overload and the lack of interpretability in traditional learning resource recommendation, this paper proposes a Structure-aware and Attention-enhanced explainable learning resource Recommendation approach (StAR) for smart education. StAR constructs a reinforcement learning framework grounded in a knowledge graph to model learner–resource interactions. First, a multi-head attention mechanism encodes path states and extracts key semantic features, enhancing the model’s ability to represent complex learning contexts. Then, a dual-layer action pruning strategy compresses the action space and improves reasoning efficiency. Finally, a structure-aware reward function guides the generation of semantically coherent and interpretable recommendation paths. Experiments on two real-world educational datasets, COCO and MoocCube, demonstrate that StAR outperforms several baseline models, achieving improvements of 14.2% and 12.6% in NDCG and Recall on COCO, and 5.2% and 4.2% on MoocCube, respectively. The results validate the effectiveness of StAR in enhancing recommendation accuracy, reasoning efficiency, and interpretability, offering a promising AI-enhanced solution for personalized learning in smart cities. Full article

Massive open online courses (MOOCs) represent an innovative online learning paradigm that has garnered considerable popularity in recent years, attracting a multitude of learners to MOOC platforms due to their accessible and adaptable instructional structure. However, the elevated dropout rate in current MOOCs limits their advancement. Current dropout prediction models predominantly employ fixed-size convolutional kernels for feature extraction, which insufficiently address temporal dependencies and consequently demonstrate specific limitations. We propose a Lie Group-based feature context-local fusion attention model for predicting dropout in MOOCs. This model initially extracts shallow features using Lie Group machine learning techniques and subsequently integrates multiple parallel dilated convolutional modules to acquire high-level semantic representations. We design an attention mechanism that integrates contextual and local features, effectively capturing the temporal dependencies in the study behaviors of learners. We performed multiple experiments on the XuetangX dataset to evaluate the model’s efficacy. The results show that our method attains a precision score of 0.910, exceeding the previous state-of-the-art approach by 3.3%. Full article