Search Results (134)

Self-regulated learning (SRL) has been widely recognized as a critical skill for academic success in online and blended learning contexts. However, many students experience difficulty in effectively applying SRL strategies in the absence of structured instructional guidance. To address this challenge, this study developed and implemented a learning management system integrated with SRL support (SRL-LMS), specifically designed for the online component of a blended learning course. The SRL-LMS consisted of two sections: a conventional course content section and a SRL training section designed to support students in applying SRL strategies. A quasi-experimental design was adopted with 69 college students assigned to either an experimental group, with access to both course and SRL sections, or a control group, which accessed only the course section. Results indicated that students in the experimental group reported higher levels of self-regulation and showed more frequent and diverse application of SRL strategies compared to the control group. In terms of academic performance, the experimental group performed significantly better than the control group on the first exam, though no significant difference was observed on the second exam. These results highlight the effectiveness of structured SRL interventions within digital learning platforms for improving students’ self-regulatory behaviors. Future implementations should address cognitive load and incorporate strategic approaches to sustain student motivation. This study advances current SRL intervention designs and offers valuable insights for educators and instructional designers aiming to integrate targeted SRL supports in online and blended learning environments. Full article

Background and Objectives: Handgrip strength represents a critical indicator of physical fitness and nutritional status in adolescents, yet population-specific reference values remain limited in developing countries. Understanding age- and sex-specific variations is crucial for accurate clinical assessment and effective health monitoring. The objective of this study was to establish comprehensive reference values for handgrip strength in healthy Tunisian adolescents aged 13–19 years and examine sex and age group differences in these measures. Materials and Methods: This cross-sectional study was conducted between September 2024 and June 2025, involving a sample of 950 participants (482 males, 468 females) aged 13–19 years from northwest Tunisia. Handgrip strength was measured using standardized dynamometry protocols for both hands. Anthropometric measurements included height, weight, and body mass index. Percentile curves were generated using the LMS method, and correlations between handgrip strength and anthropometric variables were analyzed using Pearson correlation coefficients. Results: Males demonstrated significantly higher handgrip strength than females from age 13 onward (13 years: p = 0.021; 14–19 years: p ≤ 0.001). Effect sizes for sex differences were consistently large across age groups (Cohen’s d range: 0.53–2.09 for the dominant hand). Mean dominant handgrip strength ranged from 25.60 ± 7.73 kg to 47.60 ± 12.45 kg in males and 21.90 ± 6.13 kg to 28.40 ± 4.74 kg in females across age groups. After adjusting for body mass, sex differences remained significant between groups (13 years: p = 0.014; d= 1.5; 14–19 years: p ≤ 0.001; d: 1.71–3.12). Strong positive correlations emerged between handgrip strength and height (males: r = 0.748, females: r = 0.601), body mass (males: r = 0.659, females: r = 0.601), and body mass index (BMI) (males: r = 0.391, females: r = 0.461). Body mass and height emerged as the strongest predictors of handgrip strength in both sexes, while BMI showed a smaller but still significant contribution. Conclusions: This study provides the first comprehensive age- and sex-specific reference values for handgrip strength in Tunisian adolescents. Healthcare providers can utilize these percentile charts for the clinical assessment and identification of musculoskeletal fitness deficits. The results suggest its use in educational and clinical contexts. Full article

While prior work has explored learner behavior using learning management systems (LMS) data, few studies provide week-level clustering validated against external engagement labels. To understand and assist students in online learning platforms and environments, this study presents a week-level engagement profiling framework for e-learning environments, utilizing K-means clustering and label-based validation. Leveraging log data from 127 students over a 13-week course, 44 activity-based features were engineered to classify student engagement into high, moderate, and low levels. The optimal number of clusters (k = 3) was identified using the elbow method and assessed through internal metrics, including a silhouette score of 0.493 and R² of 0.80. External validation confirmed strong alignment with pre-labeled engagement levels based on activity frequency and weighting. The clustering approach successfully revealed distinct behavioral patterns across engagement tiers, enabling a nuanced understanding of student interaction dynamics over time. Regression analysis further demonstrated a significant association between engagement levels and academic performance, underscoring the model’s potential as an early warning system for identifying at-risk learners. These findings suggest that clustering based on LMS behavior offers a scalable, data-driven strategy for improving learner support, personalizing instruction, and enhancing retention and academic outcomes in digital education settings such as MOOCs. Full article

Applying language models (LMs) and generative artificial intelligence (GenAI) to the study of Ancient Greek offers promising opportunities. However, it faces substantial challenges due to the language’s morphological complexity and lack of annotated resources. Despite growing interest, no systematic overview of existing research currently exists. To address this gap, a systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 methodology. Twenty-seven peer-reviewed studies were identified and analyzed, focusing on application areas such as machine translation, morphological analysis, named entity recognition (NER), and emotion detection. The review reveals six key findings, highlighting both the technical advances and persistent limitations, particularly the scarcity of large, domain-specific corpora and the need for better integration into educational contexts. Future developments should focus on building richer resources and tailoring models to the unique features of Ancient Greek, thereby fully realizing the potential of these technologies in both research and teaching. Full article

Adaptive learning tailors learning to the specific needs and preferences of the learner. Although studies focusing on adaptive learning systems became popular decades ago, there is still a need for empirical evidence on the usability of adaptive learning in various educational environments. This study uses LMS log data to elucidate an adaptive course design explicitly developed for formal educational environments in higher education institutions. The framework utilizes learning analytics and machine learning techniques. Based on learners’ online engagement and tutors’ assessment of course activities, adaptive learning paths are presented to learners. To determine whether our system can increase learner engagement and prevent failures, learner success and engagement are measured during the learning process. The results show that the proposed adaptive course framework can increase course engagement and success. However, this potential depends on several factors, such as course organization, feedback, time constraints for activities, and the use of incentives. Full article

This study investigates consumer satisfaction in e-learning services by addressing a specific gap in the literature: the limited integration of sustainability principles and behavioral modeling in understanding satisfaction drivers in online education. While existing studies have explored engagement and usability, few have considered how sustainability-related factors influence satisfaction in digital learning environments. Based on a conceptual model involving system quality, service quality, motivation, and cognitive engagement, we applied structural equation modeling (WarpPLS) to a sample of 312 university students from Romania, using mainstream learning management systems (LMS). Data were collected from students at the Bucharest University of Economic Studies using a convenience sampling method. The results show that service quality and cognitive engagement are the strongest predictors of satisfaction. This study offers practical recommendations for improving sustainable digital marketing strategies in e-learning, such as enhancing support services and aligning platform features with eco-conscious consumer expectations. Full article

The current study examines the determinants influencing preparatory year (PY) students’ intentions to adopt AI-powered natural language processing (NLP) models, such as Copilot, ChatGPT, and Gemini, and how these intentions shape their conceptions of digital sustainability. Additionally, the extended unified theory of acceptance and use of technology (UTAUT) was integrated with a diversity of educational constructs, including content availability (CA), learning engagement (LE), learning motivation (LM), learner involvement (LI), and AI satisfaction (AS). Furthermore, responses of 274 PY students from Saudi Universities were analysed using partial least squares structural equation modelling (PLS-SEM) to evaluate both the measurement and structural models. Likewise, the findings indicated CA (β = 0.25), LE (β = 0.22), LM (β = 0.20), and LI (β = 0.18) significantly predicted user intention (UI), explaining 52.2% of its variance (R² = 0.522). In turn, UI significantly predicted students’ digital sustainability conceptions (DSC) (β = 0.35, R² = 0.451). However, AI satisfaction (AS) did not exhibit a moderating effect, suggesting uniformly high satisfaction levels among students. Hence, the study concluded that AI-powered NLP models are being adopted as learning assistant technologies and are also essential catalysts in promoting sustainable digital conceptions. Similarly, this study contributes both theoretically and practically by conceptualising digital sustainability as a learner-driven construct and linking educational technology adoption to its advancement. This aligns with global frameworks such as Sustainable Development Goals (SDGs) 4 and 9. The study highlights AI’s transformative potential in higher education by examining how user intention (UI) influences digital sustainability conceptions (DSC) among preparatory year students in Saudi Arabia. Given the demographic focus of the study, further research is recommended, particularly longitudinal studies, to track changes over time across diverse genders, academic specialisations, and cultural contexts. Full article

We propose the extension for Artificial Intelligence (AI)-supported learning recommendations within higher education, focusing on enhancing the widely-used Moodle Learning Management System (LMS) and extending it to the Learning eXperience Platform (LXP). The proposed LXP is an enhancement of Moodle, with an emphasis on learning support and learner motivation, incorporating various recommendation types such as content-based, collaborative, and session-based recommendations to provide the next learning resources given by lecturers and retrieved from the content curation of Open Educational Resources (OER) for the learners. In addition, we integrated a chatbot using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) with AI-based recommendations to provide an effective learning experience. Full article

Blended learning has introduced a more accessible and flexible teaching environment in higher education. However, ensuring that content is inclusive, particularly for students with learning difficulties, remains a challenge. This paper explores how Moodle, a widely adopted learning management system (LMS), can support inclusive and adaptive learning based on Universal Design for Learning (UDL) principles. A 16-week descriptive exploratory study was conducted with 70 undergraduate students during a software engineering fundamentals course at Philadelphia University in Jordan. The research combined weekly iterative focus groups, teaching reflections, and interviews with 16 educators to identify and address inclusion barriers. The findings highlight that the students responded positively to features such as conditional activities, flexible quizzes, and multimodal content. A UDL-based framework was developed to guide the design of inclusive Moodle content, and it was validated by experienced educators. To our knowledge, this is the first UDL-based framework designed for Moodle in Middle Eastern computing and engineering education. The findings indicate that Moodle features, such as conditional activities and flexible deadlines, can facilitate inclusive practices, but adoption remains hindered by institutional and workload constraints. This study contributes a replicable design model for inclusive blended learning and emphasizes the need for structured training, intentional course planning, and technological support for implementing inclusivity in blended learning environments. Moreover, this study provides a novel weekly iterative focus group methodology, which enables continuous course refinement based on evolving students’ feedback. Future work will look into generalizing the research findings and transferring the findings to other contexts. It will also explore AI-driven adaptive learning pathways within LMS platforms. This is an empirical study grounded in weekly student focus groups, educator interviews, and reflective teaching practice, offering evidence-based insights on the application of UDL in a real-world higher education setting. Full article

This study investigates the use of Google NotebookLM for the automatic generation of educational assessment items. A mixed-methods approach was adopted, combining quantitative psychometric evaluation with qualitative student feedback. Six tests, each composed of 15 multiple-choice questions generated from diverse sources such as PDFs, web slides, and YouTube videos, were administered to undergraduate students. Quantitative analysis involved calculating key indices which confirmed that many AI-generated items met acceptable psychometric criteria, though some items revealed reliability concerns and potential bias. Concurrently, a structured questionnaire assessed the clarity, relevance, and fairness of the test items. Students generally rated the AI-generated questions positively in terms of clarity and pedagogical alignment, while also noting areas for improvement. In conclusion, the findings suggest that generative AI can offer a scalable and efficient solution for test item creation; however, further methodological refinements are needed to ensure consistent validity, reliability, and ethical fairness in learning assessments. Full article

Given the increasing digitalization of education and the variety of available digital learning materials (dLMs) of differing quality, (pre-service) teachers must develop the ability to select appropriate dLMs. Objective, reliable, and valid assessment instruments are necessary to evaluate the effectiveness of that development. This study conceptualized and designed an economical four-item instrument for assessing “selecting dLMs” based on accepted frameworks and competence models. The scientific quality of the instrument was evaluated in Study 1 (n = 164) with four dLMs and empirically investigated in a subsequent Study 2 (n = 395) with pre-service mathematics teachers from two universities. The empirical results indicate that the instrument could objectively and reliably gauge different levels of “selecting dLMs”. Furthermore, the results are consistent with the widely accepted notion that the competence of “selecting dLMs” depends on (content) knowledge; however, that relation was not strong. In addition, the results for objectively assessing “selecting dLMs” paralleled the results of self-assessed TPACK in terms of the academic progression of participants. The proposed approach allows for variations and integration of diverse dLMs, and it has the potential to be adapted in other subject areas and contexts. Full article

Researchers have extensively explored learning analytics in online courses, primarily focusing on linear course structures where students progress sequentially through lessons and assessments. However, non-linear courses, which allow students to complete tasks in any order, present unique challenges for learning analytics due to the variability in course progression among students. This study proposes a method for applying learning analytics to non-linear, self-paced MOOC-style courses, addressing early performance prediction and online learning pattern detection. The novelty of our approach lies in introducing a personalized feature aggregation that adapts to each student’s progress rather than being defined at fixed timelines. We evaluated three types of features—engagement, behavior, and performance—using data from a non-linear large-scale Moodle course designed to prepare high school students for a public university entrance exam. Our approach predicted early student performance, achieving an F1-score of 0.73 at a 20% cumulative weight assessment. Feature importance analysis revealed that performance and behavior were the strongest predictors, while engagement features, such as time spent on educational resources, also played a significant role. In addition to performance prediction, we conducted a clustering analysis that identified four distinct online learning patterns recurring across various cumulative weight assessments. These patterns provide valuable insights into student behavior and performance and have practical implications, enabling educators to deliver more personalized feedback and targeted interventions to meet individual student needs. Full article

The rapid growth of online education has raised significant concerns about identifying and addressing academic dishonesty in online exams. Although existing solutions aim to prevent and detect such misconduct, they often face limitations that make them impractical for many educational institutions. This paper introduces a novel online education integrity system utilizing well-established statistical methods to identify academic dishonesty. The system has been developed and integrated as an open-source Moodle plug-in. The evaluation involved utilizing an open-source Moodle quiz log database and creating synthetic benchmarks that represented diverse forms of academic dishonesty. The findings indicate that the system accurately identifies instances of academic dishonesty. The anticipated deployment includes institutions that rely on the Moodle Learning Management System (LMS) as their primary platform for administering online exams. Full article

The purpose of this study is to thoroughly examine the technical competencies of university websites in Northern Cyprus, as well as the contributions of Learning Management System (LMS) platforms integrated with these websites to user experience, with a focus on efficiency and sustainability in online education. This study adopts a “mixed-method” research model, which combines both quantitative and qualitative research methods for data collection and analysis, with the results being evaluated together. The participants of the research consist of the websites of universities in the Turkish Republic of Northern Cyprus (TRNC) and the IT department managers who will be consulted for their opinions on these websites. A total of 15 university websites were analyzed within the scope of the study, and interviews were conducted with the IT managers of these universities. During this process, analysis tools such as SimilarWeb, Alexa, Ahrefs, Moz, and SEMrush were utilized. For qualitative analysis, a semi-structured interview method was chosen to gather the views of IT managers on the websites. The findings emphasize the need for universities to optimize their digital strategies. Differences in website performance directly affect the strength of universities’ digital presence, while technological infrastructure, user experience, and the integration of educational tools emerge as key factors in improving this performance. Based on the results of this study, some universal solutions are proposed to optimize the digital strategies of universities in Cyprus. Full article

The development and use of conversational agents in education has become widespread in recent years because of their ability to facilitate student interaction with the learning material, improve engagement and provide academic support, while at the same time reducing the teachers’ workload. This is especially important in the case of distance and asynchronous education, where the availability of academic support must be ensured at any time. This paper reports on the implementation and evaluation of a conversational agent called AIMT Agent, developed for the purposes of supporting postgraduate students during their studies. The conversational agent is based on an open-source framework, namely Rasa, which provides the tools for building natural language understanding into the agent. The agent is fully integrated with the Moodle Learning Management System (LMS). The agent was assessed through a questionnaire according to the Technology Acceptance Model (TAM) in terms of perceived usefulness and perceived ease of use by 24 postgraduate students. The results show that users of the AIMT agent have assessed the conversational agent favorably in both of these aspects. This confirms the validity of the approach and is a motivation for refinements and further development. Full article