Search Results (318)

Medical literature is replete with diagrammatic representations of systems, yet lacks standardised nomenclature and consistent symbolic conventions. In an introductory system dynamics course for health science students, causal loop diagrams (CLDs) are used to support systems thinking. Notwithstanding recognised limitations, CLDs provide a coherent heuristic for representing multivariate systems with feedback. We studied 55 first-year volunteers enrolled in the course to compare understanding of systems presented as CLDs versus typical journal diagrams. Two endocrine systems were selected from open-access, peer-reviewed literature: calcium homeostasis and glucose homeostasis. Participants were shown either the original journal diagram for one system and a CLD for the other, or vice versa, and answered twelve true/false questions—six per system. A mixed-model, two-way repeated measures ANOVA revealed a significant interaction between Diagram (CLD vs. journal diagram) and System (Calcium vs. Glucose). Post hoc comparisons showed significantly higher performance with CLDs for both Calcium (0.84 vs. 0.38) and Glucose (0.83 vs. 0.63), p < 0.001. A Fisher’s Exact Test also indicated a higher proportion of questions favouring CLDs. These findings suggest that training in CLDs may enhance understanding of complex systems compared to standard journal diagrams. Further work is needed to address limitations including the small sample size, use of a single cohort, and a restricted set of diagrams. Full article

Peer feedback is widely used in English as a foreign language (EFL) higher education, yet its benefits are often limited by uneven feedback quality and learners’ difficulty in interpreting and using comments. This theoretical paper synthesizes research on peer feedback, student feedback literacy, and recent developments in generative artificial intelligence (GenAI) to propose a theory-informed design framework that positions GenAI as Trainer and Synthesizer in L2 speaking peer feedback. Building on feedback literacy as a set of capacities (appreciating feedback, making judgments, managing affect, and taking action), the paper argues that speaking tasks create distinct constraints, including time pressure, fleeting performance, and heightened affect, which make real-time peer feedback promising but pedagogically challenging. To address these challenges, here we introduce two complementary roles for GenAI in peer feedback workflows: a Trainer that supports feedback quality through calibration with exemplars, rubric-guided practice, and feedback-on-feedback; and a Synthesizer that aggregates peer input into concise, actionable guidance linked to criteria and learning goals. The conceptual proposal specifies key design principles (e.g., transparency, learner agency, teacher-in-the-loop oversight, and privacy-conscious data practices) and outlines researchable propositions for evaluating learning, engagement, and equity outcomes. The paper concludes with implications for task design, training sequences, and responsible classroom implementation. Full article

Engineering Materials courses are characterized by dense conceptual content, cumulative knowledge structures, and heterogeneous student learning trajectories. Existing teaching reform studies often focus on isolated instructional techniques or digital tools, while paying limited attention to the systemic organization of learning activities, assessment, feedback, and instructional decision-making. This study proposes a system-oriented teaching framework for an undergraduate Engineering Materials course within an urban underground space engineering program. The framework conceptualizes course instruction as a closed-loop process driven by continuous learning evidence and feedback regulation. The framework was implemented in an undergraduate Engineering Materials course with 50 students over a 16-week semester using a learning management platform. Multiple sources of process data were collected, including platform access records, assignment submissions, weekly quiz performance, pre- and post-course concept assessments, instructor feedback logs, and instructional adjustment records. The results indicate that the proposed framework supported timely instructional regulation and adaptive responses to heterogeneous learning states. Observable improvements were found in student engagement patterns and assessment outcomes across the semester. Mean concept test scores increased from 55.7 to 72.2. Students with lower initial scores gained an average of 22.3 points, compared to 11.8 points for their higher-performing peers. A total of 312 feedback messages were delivered, with a median latency of three days. These improvements were observed in association with the implementation of the framework, although causal attribution is limited by the non-experimental, single-cohort design. The study provides an exploratory case showing that system-oriented teaching design may offer a coherent and practically feasible approach for enhancing engineering education in data-rich instructional environments, while also contributing to the application of systems thinking in teaching reform. Full article

The SensaSea System is a responsive multisensory environment, specifically, a room-sized interactive installation that incorporates wearable devices, interactive visual floor projections and auditory and tactile modalities. SensaSea is designed as a physical environment for embodied interaction and free play suitable for multiple players; the system uses social proximity as the primary mechanism. Our objective is to promote active peer interaction and social connectedness among elementary school children through sensory-guided approaches which include digitized and projected interactive sea creatures. The multi-modal system also features an interactive soundscape and innovative real-time haptic feedback. We conducted eight group user studies (24 children in total). Our usability and feasibility tests demonstrated that the system results in positive emotions and elicits multiple pro-social behaviors. Full article

Authentic, situated learning experiences which mirror the collaborative nature of healthcare practice are essential in preparing students for their future professions. Feedback literacy may be thought of as the understanding, capacity, and disposition needed to make sense of information and use it to enhance work or learning strategies. This study explored how feedback literacy can be developed through situated, interprofessional peer-to-peer feedback within a community-based paediatric health screening programme. Using an exploratory Action Research qualitative design, the planning activities stage explored current practice, gathering student insights via interviews, reflections, and a workshop to co-design an Interprofessional Feedback Conversation Guide (IPFCG). The IPFCG was piloted, integrating structured feedback tools and protected time for peer exchange, within the community screening activity. Feedback regarding use of the IPFCG contributed to the gathering data stage, which was followed by the evaluation and reflection stage. Evaluation revealed four key themes: value, engagement, optimising relationships, and structuring conversations. Students valued receiving feedback from peers outside their discipline, actively engaged with the process, emphasised the importance of building rapport, and utilised structured dialogue. These findings highlight how authentic, field-based learning can foster feedback literacy, enhancing the development of professional identity. The interprofessional nature of the program reflects the complexity of modern healthcare and demonstrates how curriculum-integrated models of authentic learning can enhance student engagement and workplace readiness. This study contributes to the evolving conversation about embedding authenticity in higher education and offers a practical model for building collaborative communication within situated learning experiences at scale. Full article

Experimental Governance (EG) has emerged as a strategic framework for managing complexity in high-uncertainty environments. However, its application in the private sector remains fragmented, often conflated with purely operational tools. This study addresses this gap by performing a conceptual transfer of EG principles into the domain of business processes. Through an expanded Systematic Literature Review (SLR) of 41 peer-reviewed articles (covering the period 2004–2026), we identify what we term the ‘Internalization Paradox’: while firms rapidly adopt experimental methodologies like Agile or Lean, they often fail to embed them into formal governance structures that ensure long-term accountability and institutional learning. This updated review incorporates cutting-edge discussions on Artificial Intelligence (AI) governance, experimentalist metagovernance, and the strategic regulation of uncertainty. Our findings suggest that organizational resilience is not merely a byproduct of technological readiness, but an emergence of ‘Institutionalized Experimentalism’. We propose a Conceptual Framework that operationalizes EG through iterative feedback loops, corporate sandboxes, and adaptive decision rights, providing a robust roadmap for future empirical research in management and organizational theory. Full article

The Jordan Valley, a critical agro-ecosystem in Jordan, faces escalating challenges from chronic water scarcity compounded by environmental and socio-economic pressures, necessitating a systems perspective to understand cross-sector interactions beyond isolated sectoral issues. This study interprets socio-ecological interactions influencing sustainability outcomes in the region and identifies key feedback loops and adaptive responses under water scarcity through an integrated Socio-Ecological Systems (SES) and Water–Energy–Food–Ecosystems (WEFE) framework. Employing a qualitative document analysis (QDA) design, a purposive collection of peer-reviewed studies and institutional publications (n = 50) published between 2002 and 2025 was assembled and systematically coded using a structured deductive–inductive strategy grounded in SES components and WEFE domain interactions. Results reveal seven interconnected themes: water scarcity as a structural constraint, agricultural intensification and resource pressures, climate change as a stress multiplier, ecosystem degradation and service loss, pollution and environmental quality challenges, socio-economic vulnerability and livelihood constraints, and fragmented governance with coordination gaps. These themes highlight reinforcing loops where scarcity promotes groundwater reliance and non-conventional water use, intensification heightens salinity and contamination risks, climate variability escalates irrigation demands, and ecological degradation diminishes buffering capacity, while socio-economic limitations hinder adaptation and governance fragmentation impairs integrated planning and enforcement. While prior studies have examined water scarcity, agricultural intensification, or climate impacts in isolation, this study advances the literature by synthesizing these dynamics through an integrated SES–WEFE analytical lens, revealing reinforcing system feedbacks and governance constraints that are not visible within single-sector or descriptive syntheses. Full article

Adolescents increasingly engage with digital technologies, yet understanding patterns of smartphone use and fostering reflective awareness remain challenging. Traditional assessments in clinical or school settings may limit participation and self-reflection. This study evaluated the feasibility and impact of a Computer-Assisted Web Interviewing (CAWI) approach to monitor smartphone use, provide immediate individualized feedback, and support peer-mediated dissemination in a public science engagement context. Across three editions of the European Researchers’ Night in Rome (2023–2025), 807 adolescents aged 10–19 completed the SAS-SV questionnaire via on-site tablets or personal devices using QR codes. Smartphone use was categorized into Low Involvement, At-Risk, or Problematic. Participants were encouraged to share the survey link with peers, enabling snowball-mediated recruitment. Participant acceptance was assessed through the Net Promoter Score (NPS). Snowball participation accounted for the majority of responses, highlighting the effectiveness of peer-mediated diffusion. SAS-SV categorization indicated 46% Low Involvement, 39% At-Risk, and 15% Problematic use, with minimal gender differences. NPS values ranged from +69 to +79, with snowball participants reporting slightly higher satisfaction than on-site attendees. These results underscore high engagement, perceived value, and the role of peer networks in promoting reflective digital behavior. Integrating CAWI assessment, immediate feedback, and peer-mediated diffusion created a socially situated environment supporting self-reflection and voluntary dissemination. Peer networks extended both the temporal and social reach of the initiative beyond the public event, demonstrating a scalable and non-stigmatizing model. CAWI-based monitoring combined with feedback and peer-driven diffusion is feasible and effective for adolescent digital wellbeing interventions. This approach fosters reflective digital citizenship, supports self-awareness, and leverages social networks to enhance the reach and impact of youth-centered health promotion initiatives. Full article

This study investigates how parents and students interpret and form continued engagement intentions with a radar visualisation tool designed to present multi-subject academic performance. While data visualisation is increasingly used in education, limited empirical attention has been given to whether parents and students, who share the same performance information but hold distinct roles, respond to visualised reports through similar behaviours. To address this gap, an interactive radar visualisation was developed to present secondary school students’ achievement across subjects with peer reference points. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) as an analytical framework, this study examines the determinants of continued intention to use the visualisation tool. Questionnaire data were collected from 706 parents and 264 students in a Macao secondary school. Structural equation modelling (SEM) revealed fundamentally different ideas of continued engagement. For parents, continued intention was significantly associated with performance expectancy (PE) and effort expectancy (EE), social influence (SI) and facilitating conditions (FC), suggesting the tool functioned as a decision support system for academic planning. For students, only social influence (SI) and facilitating conditions (FC) emerged as significant predictors, indicating that peer comparison and external expectations may not fit their needs. Parents also reported significantly higher continued intention than students. The finding extended UTAUT by demonstrating that core acceptance relationships are moderated by different roles, reframing technology acceptance in educational visualisation from system adoption to information interpretation. The study provides empirical evidence that visualised performance reporting functions not merely as a data display but also as a communication medium whose meaning is actively constructed by users. These insights highlight the need for role-sensitive design, emphasising actionable planning support for parents and personally meaningful, agency-oriented feedback for students, in order to foster productive home–school communication and sustained engagement with learning information. Full article

The future of innovation and economic growth depends on our ability to nurture the next generation of scientists. The global shortage of qualified STEM (Science, Technology, engineering, Mathematics) teachers has led many countries to expedite the transition of subject-matter experts from industry and academia into teaching roles. These second-career science student teachers typically participate in accelerated training programs designed to address urgent shortages. This study addresses a gap in the literature regarding effective pedagogical interventions for career-changing professionals in STEM fields, focusing on the experience and transformation of second-career science student teachers. This qualitative case study explores how a Challenge-Based Learning (CBL) course fosters the development of pedagogical competences via developing an instructional unit collaboratively, among five second-career science student teachers enrolled in an accelerated teacher education program. Drawing on data collected through instructors’ field notes, iterative work-in-progress lesson drafts, and reflective final papers, the study employs qualitative content analysis to trace changes in participants’ instructional approaches and professional identity. Findings reveal that engagement with the CBL framework promoted a significant shift from teacher-centered to learner-centered instruction, as participants increasingly integrated collaborative learning, inquiry-based activities, and reflective practices into their lesson planning and classroom teaching. The iterative nature of CBL, which emphasizes real-world problem-solving and structured opportunities for reflection and peer feedback, was instrumental in supporting participants’ adaptive expertise and confidence as novice teachers. Moreover, the course experience contributed to the emergence of a professional teaching identity, with participants reporting greater self-efficacy, a stronger sense of belonging to the teaching community, and increased motivation to persist in the profession. The results underscore the potential of integrating CBL and learning sciences principles into accelerated teacher preparation programs to enhance both cognitive and affective dimensions of teacher development. Full article

Point-of-care ultrasound (POCUS) is portable and radiation-free, but its clinical reliability is constrained by operator-dependent image acquisition and the limited scalability of expert quality assurance (QA) review. As handheld devices proliferate faster than mentorship capacity, trainees increasingly rely on heterogeneous free open access medical education (FOAMed) resources that rarely provide real-time psychomotor feedback. We conducted a structured narrative review (MEDLINE, Embase, Scopus, and Web of Science; last searched on 23 February 2026), with searches performed by H.H. and independently checked by J.J.P. (both POCUS-trained clinicians). After screening, 31 studies were included. We synthesized evidence on artificial intelligence (AI) systems that support bedside image acquisition and automate QA. The primary synthesis centered on key prospective or comparative clinical evaluations of AI-guided acquisition across echocardiography, focused assessment with sonography in trauma, abdominal aortic aneurysm screening, and lung ultrasound, complemented by peer-reviewed studies of FOAMed appraisal tools and online resource quality. These evaluations suggest that real-time probe guidance, view recognition, anatomy labeling, and automated capture may enable novices, after brief training, to acquire diagnostically adequate images for narrowly defined tasks. Early reports of automated QA scoring and program-level triage for expert review suggest potential to reduce expert workload and shorten feedback cycles, but external validation, generalizability across devices and patient habitus, and patient-centered outcomes remain limited. Acquisition-focused AI may therefore serve as an upstream “digital mentor” to improve novice image acquisition. We propose a practical pathway that integrates curated FOAMed resources and simulation with AI-guided bedside acquisition and continuous QA governance for safe deployment. Full article

Groundwater management increasingly relies on participatory governance, yet most existing participatory frameworks lack mechanisms for iterative learning and continuous improvement and further lack structured operational indicators, systematic monitoring–feedback integration, and institutionalized mechanisms that embed participation within measurable governance cycles rather than treating it as a one-time procedural input. Conversely, Lean thinking, particularly the Plan–Do–Check–Act (PDCA)-based continuous improvement principles, offers systematic methods for feedback and adaptation, but remains underexplored in environmental governance contexts. This paper bridges these traditions by conceptualizing participatory groundwater governance as a continuous improvement system, thus aligning community participation with PDCA logic in order to enhance adaptive management and sustainability outcomes. This study introduces a novel conceptual synthesis that integrates Lean management principles into participatory groundwater governance. In the current research, a methodological framework is proposed for integrating Lean thinking, particularly the Plan–Do–Check–Act cycle, with participatory groundwater governance, thus producing a Lean–participatory groundwater governance (Lean–PGG) framework. To conceptualize the framework, a set of eight rubric-based indicators was developed from a literature matrix of 54 peer-reviewed case studies selected through predefined inclusion criteria and multi-stage screening procedures, in order to evaluate participation, governance readiness, tool application, data use, monitoring, learning, and institutionalization. Each variable indicator was then scored on a three-point scale and categorized into the PDCA maturity levels The findings suggest a consistent heuristic trend across cases, characterized by comparatively stronger performance in the planning and implementation stages. A clear majority of studies scored in the moderate-to-high range (≥2.5/3) for the Plan and Do dimensions, whereas only a limited proportion demonstrated structured Check mechanisms and fewer still exhibited institutionalized Act processes. This asymmetry indicates persistent gaps in the consolidation of evaluation and feedback within participatory groundwater governance systems. This Lean–PGG framework thus demonstrates how continuous improvement mechanisms, i.e., feedback loops, reflection, and adaptive standardization, can strengthen participatory groundwater governance. The proposed framework offers a replicable and practical model for integrating continuous improvement into environmental and groundwater governance, fostering adaptive management, resource efficiency, and sustainability outcomes. Full article

The rapid expansion of artificial intelligence (AI) in digital education has transformed gamification from a motivational strategy into a data-driven, adaptive learning paradigm. This systematic review conceptualizes AI-supported gamification as an information-centered ecosystem integrating learning analytics, behavioral modeling, adaptive algorithms, and intelligent feedback mechanisms to enhance cognitive development and critical thinking. Following PRISMA 2020 guidelines, a systematic search was conducted across Scopus, Web of Science, ScienceDirect, Google Scholar, and ResearchGate. Peer-reviewed empirical studies published between 2020 and 2025 were considered. Studies were included if they examined gamification in educational contexts with AI-driven or adaptive system components, while non-educational contexts, duplicates, and non-English publications were excluded. After screening and eligibility assessment, 100 studies were included in the final synthesis. The review examines how AI-driven personalization, neurotechnology, predictive modeling, and generative systems reshape the design and effectiveness of gamified e-learning environments. Architectural patterns identified include recommender systems, real-time behavioral adaptation, affect-aware feedback loops, and algorithmic content generation. Across the reviewed studies, AI-supported gamified systems were frequently associated with increased engagement and moderate improvements in executive functions, higher-order reasoning, and adaptive learning pathways. However, challenges related to system transparency, data governance, algorithmic bias, cognitive load management, and equitable access remain significant. The review was not registered. By framing gamification as an adaptive information system rather than solely a pedagogical intervention, this study proposes a structured taxonomy of AI-driven gamified architectures—including data acquisition, user modeling, predictive analytics, and adaptive feedback layers—and outlines research priorities for scalable, ethically grounded, and data-informed e-learning ecosystems. Full article

Background: Nudging has become a prominent approach for influencing secure digital behavior, yet its theoretical coherence and long-term effectiveness remain unclear. Objectives: This systematic review examines how the “nudging paradigm” is interpreted and operationalized in cybersecurity research and evaluates the short-term effectiveness and durability of nudging interventions on user behavior. Methods: Following PRISMA 2020 guidelines, we searched Scopus, APA PsycInfo, IEEE Xplore, and the ACM Digital Library using the query “(cybersecurity OR security) AND nudg*”. We included peer-reviewed empirical studies that explicitly self-identified their intervention as a “nudge” within a digital security context. Two reviewers independently screened records and extracted data. Studies were classified by cybersecurity domain, nudge type, and functional mechanism. Risk of bias was assessed descriptively using a behavior-analytic quality lens focusing on ecological validity, outcome type, and temporal scope. Results were synthesized narratively and descriptively at the study level. Results: Sixty-five studies (2012–December 2025) met inclusion criteria. Most focused on password security and privacy/data security. Informational/feedback nudges predominated, followed by default/design, framing, and social nudges. Many studies reported significant short-term behavioral effects, particularly when interventions embedded response-contingent feedback or altered response effort via protective defaults. Effects were most consistent for direct behavioral outcomes (e.g., password strength, clicks, configuration choices). Only one study met the criterion of post-withdrawal follow-up (≥2 weeks), and it did not demonstrate sustained maintenance of secure behavior once prompts and interface support were fully removed. Limitations: Evidence is constrained by heterogeneous designs, frequent reliance on short online experiments, limited ecological validity, and the near absence of post-withdrawal follow-ups. Interpretation: Nudging in cybersecurity reliably produces short-term improvements when interventions modify immediate contingencies of action. However, the literature does not currently provide empirical tests of durable behavior change under full withdrawal conditions. Future research should integrate consequence-based design, assess maintenance after withdrawal, and test generalization across contexts. Full article

The integration of artificial intelligence (AI) into online education has transformed the digital learning space, offering new ways to enhance learner satisfaction and engagement. This systematic literature review, covering a five-year span from 2020 to 2025, explores how AI technologies, such as chatbots, intelligent tutoring systems (ITS), sentiment analysis, gaze tracking and predictive analytics, support learner engagement across cognitive, emotional, behavioral, and social dimensions. Drawing from 30 peer-reviewed studies, the current review addresses three central research questions: (1) What aspects of AI positively influence learner satisfaction and engagement in online courses within higher education institutions; (2) What potential challenges from using these technologies may arise; and (3) What research approaches are most commonly used to assess AI’s impact in such learning contexts? The findings highlight that adaptive learning, real-time feedback, and emotion-aware systems contribute positively to personalized learning and motivation. However, concerns persist around data privacy, algorithmic bias, over-reliance on automation, and system usability. Experimental and quasi-experimental designs, as well as machine learning, mixed methods, and survey-based approaches are found to dominate in reviewed studies. Based on these insights, this work offers a foundation for future AI-enhanced learning management systems designed primarily to enhance learner engagement across cognitive, emotional, behavioral, and social domains. Full article