Search Results (155)

Concerns regarding poor air quality in communities experiencing disproportionately high levels of air pollution frequently motivate formation of community–academic partnerships. Low-cost air monitors can assess air pollution but require specific training. The RISE Communities program was created to support partnerships in air quality training and community-engaged research. The RISE Communities program in-person training took place in Cincinnati, OH, in summers 2023 and 2024. The training included hands-on workshops, lectures, and monthly webinars with continued expert guidance for one year following the in-person training. A mixed method evaluation included pre-, post-, and one-year follow-up surveys, webinar evaluation surveys, and focus groups at the conclusion of each cohort. Participants reported significantly increased confidence in describing the health impacts of air pollution and using low-cost air sensors. Subjective feedback commented on the need for more tailored and interactive webinars and data training. Participants agreed that the goals of the RISE Communities training program were met and skills were sustained. This mixed method evaluation study demonstrates that an immersive training program for academic and community partners resulted in significantly higher confidence levels related to community partnership, data analytics, data visualization, and achieving project planning outcomes. Full article

Written feedback on high-stakes assessment has the potential to provide students with valuable advice for future assessment episodes, but when feedback has a negative impact on students’ emotions, it can negate any benefits. This exploratory study reports on the perceptions of 14 lecturers and 19 students regarding their emotions toward written assessment feedback within a university preparatory program for students at a regional university in Australia. Focus groups were used to collect data from the participants. The data were analysed with NVivo Version 11 software. The data were further analysed by the authors, who identified and categorised phrases pertaining to students’ and lecturers’ emotions about feedback. The study found that lecturers and students had mixed feelings about written feedback, especially when personal connections between lecturers, other staff, and students were tenuous. It concluded that further research focusing on the impact of emotions on how students perceive written feedback and apply it in future assessments could provide insights into how to enhance contemporary tertiary teaching and assessment practices. Full article

As Generative Artificial Intelligence (GenAI) becomes integrated into professional and educational contexts, understanding its role in self-regulated learning (SRL) is essential. This study examined the engagement of 1265 adults from seven occupational sectors with GenAI for SRL, focusing on personal skills, cognitive perceptions, motivation, and contextual factors. The results indicated that the metacognitive application of GenAI is shaped by individual and contextual variables rather than solely on professional affiliation, with distinct patterns emerging across groups. Lecturers and high-tech professionals tend to use GenAI metacognitively when strong self-regulation skills are aligned with high perceived usefulness. Educators, despite high motivation, avoid GenAI unless its advantages are clear. Among healthcare professionals, concerns can either hinder or promote their use, depending on metacognitive readiness. For the general public, its use remains largely functional. This study extends the Technology Acceptance Model (TAM) by identifying perceived usefulness as a mediator between motivation and meaningful engagement, underscoring the need to address both skills and perceptions to foster equitable, informed, and strategic adoption of GenAI in diverse learning environments. Full article

Background/Objectives: Nutritional medicine remains underrepresented in medical education despite its relevance across specialties. Online learning offers a resource-efficient option to address this gap, yet evidence on the effectiveness and acceptability of online learning modules (OLMs) is limited. Methods: In this exploratory randomized controlled single post-test trial, medical students were assigned to either an OLM or an in-person lecture (IPL) on nutritional medicine (n = 91, no a priori sample size calculation performed). After course completion, students took a knowledge test and completed a questionnaire on their learning experience. Group differences were analyzed using permutation Welch t-tests, Wilcoxon–Mann–Whitney tests, or Fisher’s exact tests, depending on variable characteristics, with α = 0.05. Results: OLM students achieved significantly higher test scores than IPL students (mean difference: 2.4 points on a 0–40 scale), resulting in differences in grade classification (p < 0.05). OLM was further rated more favorably regarding content delivery, overall course evaluation, and exam preparation (all p < 0.05), while self-reported attention, concentration, and involvement did not differ between groups. Flexibility, time savings, and convenience were the most frequently reported advantages of OLM over IPL. Conclusions: This study suggests that OLM in nutritional medicine may be associated with higher test performance and more favorable student evaluations compared to IPL. These findings highlight the potential of online learning as a scalable, resource-efficient approach that may help address persistent gaps in nutritional medicine education. Building on this evidence, future work should examine how such modules can be optimally integrated into medical curricula to complement existing teaching structures. Full article

This study examines the differences in sports learning among Generation Z based on digital literacy, using the Unified Theory of Acceptance and Use of Technology (UTAUT) and Media Richness Theory (MRT). As non-face-to-face sports learning—including online lectures, remote coaching, and virtual reality—rapidly expands, digital literacy has become a key factor influencing learning outcomes and equity. Data were collected from Generation Z adults engaged in sports learning through platforms including YouTube, social networking services, online lecture platforms, and mobile applications. Participants were classified into low (n = 87)-, medium (n = 80)-, and high (n = 70)-digital-literacy groups. A 32-item questionnaire adapted from prior studies assessed digital literacy (4 items), four UTAUT constructs (performance expectancy, effort expectancy, social influence, and facilitating conditions; 16 items), and three media richness dimensions (multiple channels, immediacy of feedback, and personalness; 12 items). Confirmatory factor analysis demonstrated acceptable model fit (χ² = 779.013, df = 436, p < 0.001, NFI = 0.914, IFI = 0.960, TLI = 0.954, CFI = 0.960, SRMR = 0.037, RMSEA = 0.058), reliability (all ω and α > 0.70), and convergent/discriminant validity (all AVE > 0.50; C.R. > 0.70). Group comparisons indicated that higher digital literacy was linked to higher scores in technology acceptance and media richness perceptions (F = 40.364–64.150, p < 0.001, ηp² = 0.257–0.354) These findings indicate that intra-generational differences in digital literacy shape technology use and media experience in sports learning, highlighting the need to enhance media richness and systematically develop learners’ digital literacy to improve digital sports education’s effectiveness and equity. But causal inferences are limited by the cross-sectional design. Full article

This study aimed to evaluate the effectiveness of the CONGA game, an interactive and gamified digital tool that uses AI-generated or manually created questions with feedback, to improve colon cancer literacy among tenth- semester Software Engineering students at the University of Guayaquil. Grounded in Paulo Freire’s critical pedagogy, CONGA operationalizes the concept of “conscientização” (critical consciousness awakening) by engaging learners in dialogical reflection on medical myths and encouraging critical evaluation of health information sources. This work addresses an age group—emerging adulthood—that is often overlooked in cancer prevention campaigns despite increasing cancer incidence in this population. The game incorporates an adaptive engine that personalizes difficulty and scoring based on player performance, enhancing engagement and learning personalization. A controlled experiment compared the game-based intervention with traditional lecture-based instruction, using pre- and post-test assessments to measure knowledge gains and misconception reduction. Results demonstrated that the CONGA group achieved a significantly higher post-test correct response rate of 82%, compared to 57% in the traditional instruction group, and showed a 70.4% reduction in incorrect responses versus 42.4% in the control group. These findings indicate that CONGA’s adaptive, feedback-driven design was more effective in enhancing short-term knowledge acquisition and immediate conceptual clarification following a single session. The study concludes that, based on immediate post-intervention assessments, gamified learning represents a scalable and engaging pedagogical strategy for colon cancer literacy, particularly in our local younger population. However, these results reflect short-term learning gains measured immediately after a single session, and further research is needed to evaluate long-term knowledge acquisition. Full article

With the increasing complexity of power grid operations, operator training requires timely feedback and objective assessment. Traditional approaches based on lectures and scripted simulations provide limited personalization and weak explainability. This paper presents AI Instructors, an intelligent simulation training system for power-grid control and dispatching. The system is organized into learning, training, assessment, and analysis modules, and is built around two core technical components: (i) parameterized item generation from rule/knowledge bases using a phrase-enhanced transformer (PET), and (ii) solver-grounded, topology-aware grading with hierarchical feedback for both numeric and free-text responses. A voice interaction module is integrated to simulate telephone-based dispatch orders. We validate the system through a pilot deployment with licensed dispatch operators and scenario experiments on benchmark cases. Compared with a conventional scripted DTS workflow, AI Instructors achieves higher stepwise procedure accuracy (68%→90%), a lower topology-violation rate (32%→11%), and shorter response time (120 s→72 s), while increasing the proportion of parameterized questions and accelerating skill acquisition. These results suggest that combining adaptive sequencing with topology-safe, explainable evaluation can improve training effectiveness and operational safety. Full article

There is increasing pressure on South African Universities of Technology (UoTs) to incorporate cutting-edge Information and Communication Technologies (ICTs) to enhance teaching and learning. Adopting Learning Management Systems (LMSs), which offer adaptable, easily accessible, and data-driven education, is essential to this change. Nevertheless, many UoT lecturers still struggle to successfully adopt and use LMS platforms, even with investments in digital infrastructure and policy development. This study presents the equity-sensitive Technological Pedagogical Content Knowledge (TPACK) framework as a conceptual addition to inclusive digital pedagogy and investigates lecturers’ experiences with LMS integration at South African UoTs. This study aimed to explore how lecturers at South African UoT experience the adoption and integration of LMSs and to examine how institutional support, digital literacy, and infrastructural factors shape inclusive digital pedagogy. The results of an Interpretative Phenomenological Analysis (IPA) of six interviews with academic staff were structured into three thematic subsections (i.e., (1) lecturers’ experiences of LMS adoption, (2) institutional, infrastructural and personal factors and (3) institutional support, training and inclusivity) that directly align with the three study’s research questions. Rethinking TPACK from an equity-sensitive perspective advances theory by establishing access and equity as central mediating conditions of technology integration, especially in Global South higher education contexts with limited resources. In practical terms, the findings show that to promote inclusive and sustainable LMS adoption, specific capacity building, policy alignment, and institutional investment are required. The paper is relevant to policymakers, academic developers, and institutional leaders because of these implications, which align with the National Digital and Future Skills Strategy, the National Research Foundation (NRF) priorities, and the Department of Higher Education and Training’s (DHET) White Paper on Post-School Education. Full article

Learning Management Systems (LMSs) remain largely static and administrative, often failing to support personalization and inclusive access to learning resources. This paper presents AI for All, a practical approach to building an adaptive, accessible, and inclusive learning experience within a mainstream LMS, demonstrated through the PREPARE project (Personalized Education Framework for AI-Enabled Adaptive and AR-Enhanced Learning) implemented in Moodle. PREPARE operationalizes an end-to-end generative AI pipeline that transforms a single authoritative PDF textbook into multimodal learning assets, including chapter summaries, structured notes and slide decks, formative quiz items, video mini-lectures with captions, podcast-style audio, and chapter-level augmented reality (AR) activities. In parallel, the system maintains a hybrid learner model by combining an initial FSLSM/ILS questionnaire with continuous behavior-based profiling derived from Moodle logs. Learner profiles drive non-prescriptive personalization through resource prioritization and recommendations, while preserving learner agency and access to all modalities. We describe the system architecture, Moodle integration mechanisms, and adaptation logic, and report an ongoing mixed-methods evaluation focusing on engagement, interaction diversity, perceived usefulness, and accessibility benefits. The system-level validation and deployment readiness suggest that AI-augmented LMS workflows can reduce instructor authoring effort while improving flexibility and inclusivity, provided that human-in-the-loop validation and privacy-aware analytics are embedded from the outset. Full article

A pervasive trend across academia, social cognition, and general communication contexts is the interchangeable use of “information” and “knowledge”, particularly with reference to their forms—explicit knowledge, testimony, and expertise—conveyed by external sources. This raises a fundamental question: is the source perceived, considered, and validated as a reliable knowledge provider or merely as an information carrier? This study investigates seven academic and popular science sources by modelling their constructs of knowledge provision based on epistemological criteria and sociopsychological value, as manifested through the perspectives of university academics. The external sources examined include scientific journal articles, knowledge shared by university lecturers, scholarly monographs, textbooks and handbooks, popular science books and magazines, academic social networks and social media platforms. A quantitative investigation, supplemented by qualitative content analysis, collected assessments from sixty-six university academics in Latvia using the Epistemological Attitude Questionnaire towards Knowledge Sources. Statistical analysis, coupled with an examination and interpretation of academics’ perceptions, comprehension, use, and personal valuation of these sources, elucidated their profiles. The findings provide a holistic picture of these sources, detailing the value, qualities, functionality, and contributions of each type. Interpretations reveal that the designation of a form of “knowledge source” predominantly aligns with scientific and educational sources, whereas “information carriers” or socially functional sources primarily pertain to popular science and social media. Academic social networks, notably, occupy an intermediary position. This study offers critical academic insights into ongoing issues regarding these means of cognition. It prompts a scrutiny of both established traditional sources and contemporary mediums, both academic and popular, encouraging readers to evaluate these compiled images according to the delineated criteria of the theoretical framework. Full article

The mental health of students in university has become an increasingly pressing concern due to rising academic pressure, career uncertainty, and major life transitions. Identifying students’ psychological support needs requires an understanding of the challenges they face, as well as their expectations regarding support forms, intervention methods, and service providers. This study employed a mixed-methods cross-sectional design, combining large-scale questionnaire surveys (701 respondents) with qualitative interviews to assess the mental health status and psychological support needs of students at economics universities in Vietnam. The findings reveal that students commonly experience negative emotional states, particularly anxiety related to academic workload, financial instability, personal health, and future career orientation. A proportion of students reported depressive symptoms such as persistent sadness, prolonged stress, and physiological disturbances including insomnia and disordered eating. While severe behavioral disorders are uncommon, signs of declining academic motivation, social withdrawal, and weakened interactions with lecturers are evident. Students express a strong demand for mental health support, especially in career guidance, learning strategies, emotional regulation, and interpersonal problem-solving. Individual, professional, confidential counseling services are the most preferred forms of support, highlighting the need for a comprehensive mental health and psychological support system tailored to the context of Vietnamese universities. Full article

The transition from high school to university-level mathematics is often accompanied by significant challenges. During the COVID-19 pandemic, these difficulties were further exacerbated by the abrupt shift to online learning. In response, educators increasingly turned to gamification—“a process of enhancing a service with affordances for gameful experiences in order to support users’ overall value creation”—as a strategy to address the limitations of remote instruction. In this study, we designed a gamified environment for a first-year Number Theory course. The system was constructed using targeted game elements such as leaderboards, optional challenge exams, and recognition for elegant solutions. These features were then integrated into a comprehensive point-based assessment system, which accounted for weekly quizzes and active participation. Following a quasi-experimental design, this study compared two groups of pre-service mathematics teachers: the class of 2017 (N = 62), which received traditional in-person instruction (control group), and the class of 2020 (N = 61), which participated in an online, gamified version of the course (experimental group). Both groups were taught by the same lecturer, using identical content, concepts, and similar tasks throughout the course. Academic performance was measured using midterm exam results. While no significant difference emerged on the first midterm in week 6 (their average percentages were 50% and 51%), the experimental group significantly outperformed the control group on the second midterm at the end of the term (their average percentages were 65% and 49%). These results suggest that a thoughtfully designed, gamified approach can enhance learning outcomes in an online mathematics course. Full article

Emotion recognition through facial expressions is crucial in fields like healthcare, entertainment, and education, offering insights into user experiences. In online learning, traditional methods fail to capture students’ emotions effectively. This research introduces a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model to recognize learning emotions (interest, boredom, and confusion) during online lectures. A custom dataset was constructed by mapping action units from FER2013, CK+48, and JAFFE datasets into three learning-related categories. Images were preprocessed (grayscale conversion, resizing, normalization) and divided into training and testing sets. The CNN layers extract spatial facial features, while the LSTM layers capture temporal dependencies across video frames. Evaluation metrics included accuracy, precision, recall, and F1-score. The model achieved 98.0% accuracy, 97% precision, 98% recall, and 98% F1-score, surpassing existing CNN-only methods. This advancement enhances online learning by enabling personalized support and has applications in education, psychology, and human–computer interaction, contributing to affective computing development. Full article

Undergraduate life science education faces high attrition rates, especially among students from underrepresented groups. These disparities are often linked to differences in prior knowledge, self-efficacy, and interest, which are rarely addressed in traditional lecture-based instruction. This work explores the use of machine learning-based Intelligent Tutoring Systems (ITSs) to support personalized instruction in biology education by examining stochasticity in molecular systems. Accordingly, we developed and validated a Random Forest classification model and used it to assign instructional materials based on students’ prior knowledge and interests. We then applied the model in an introductory biology classroom and individually estimated the most promising instructional format. Results show that the most effective instruction can be reliably predicted from student performance and interest profiles, and model-based assignments may help reduce pre-existing opportunity gaps. Thus, machine-learning-driven instruction holds promise for enhancing equity in life science education by aligning materials with students’ needs, potentially reducing differences in achievement, self-efficacy, and cognitive load, which might be relevant to promoting underrepresented students. To facilitate a straightforward implementation for educators facing similar challenges associated with teaching molecular stochasticity, we developed an open-access ITS tool and provided a scalable approach for developing similar personalized learning tools. Full article

Attention and brain focus are essential in human activities that require learning. In higher education, a popular means of acquiring knowledge and information is through serious games. The need for integrating digital learning tools, including serious games, into university curricula has been demonstrated by the students’ preferences that are oriented more towards engaging and interactive alternatives than traditional education. This study examines real-time attention measurement in serious games using wearable brain–computer interfaces (BCIs). By capturing electroencephalography (EEG) signals non-invasively, the system continuously monitors players’ cognitive states to assess attention levels during gameplay. The novel approach proposes adaptive attention measurements to investigate the ability to maintain attention during cognitive tasks of different durations and intensities, using a single-channel EEG system—NeuroSky Mindwave Mobile 2. The measures have been achieved on ten volunteer master’s students in Computer Science. Attention levels during short and intense tasks were compared with those recorded during moderate and long-term activities like watching an educational lecture. The aim was to highlight differences in mental concentration and consistency depending on the type of cognitive task. The experiment was designed following a unique protocol applied to all ten students. Data were acquired using the NeuroExperimenter software 6.6, and analytics were performed in RStudio Desktop for Windows 11. Data is available at request for further investigations and analytics. Experimental results demonstrate that wearable BCIs can reliably detect attention fluctuations and that integrating this neuroadaptive feedback significantly enhances player focus and immersion. Thus, integrating real-time cognitive monitoring in serious game design is an efficient method to optimize cognitive load and create personalized, engaging, and effective learning or training experiences. Beta and attention brain waves, associated with concentration and mental processing, had higher values during the gameplay phase than in the lecture phase. At the same time, there are significant differences between participants—some react better to reading, while others react better to interactive games. The outcomes of this study contribute to the design of personalized learning experiences by customizing learning paths. Integrating NeuroSky or similar EEG tools can be a significant step toward more data-driven, learner-aware environments when designing or evaluating educational games. Full article