Search Results (630)

Slow-oscillation neurofeedback (NF), encompassing slow cortical potential (SCP), infra-low-frequency (ILF), and infra-slow-fluctuation (ISF) protocols, has gained increasing interest as a non-pharmacological intervention in pediatric mental health and neurodevelopmental care. This narrative review synthesizes peer-reviewed literature on the clinical efficacy of slow-oscillation NF in children and adolescents across various conditions, including attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), epilepsy, tic disorders, and eating-related concerns. SCP NF is the most extensively studied protocol and shows preliminary efficacy in reducing ADHD symptoms, particularly among individuals capable of learning self-regulation. For ASD and other conditions, early evidence from primarily small-scale or uncontrolled studies suggests possible benefits in emotional regulation, impulsivity, and behavioral symptoms, though findings remain mixed and often non-specific. Methodological heterogeneity, including variation in control conditions, training protocols, and outcome measures, limits the comparability of results. ILF and ISF protocols, while promising, are still emerging and require further validation. Overall, slow-oscillation NF appears to offer potential as a personalized therapeutic option for pediatric populations, but robust, well-controlled trials are needed to clarify its clinical utility and optimize its integration into multimodal care. Full article

Children diagnosed with autism face many barriers to learning. Animal Assisted Services and Interventions (AAS/AAI) have been adopted to support children within schools. The Dog Mentor is a UK-based organisation that provides training for handlers and assesses dogs to be integrated within schools. It adopts a rigorous and continuous training package and ensures the safety and welfare of all involved by adopting a whole school approach. This research uses content analysis to understand the types of activities and outcomes in The Dog Mentor programme, as established by teachers and dog handlers, across 58 schools. Teachers and dog handlers perceived that The Dog Mentor successfully supported children with autism, using a variety of sessions. This variability is seen as a benefit as it enables the intervention to be tailored to meet the needs of the students. Handler-reported benefits include creating a calm environment, promoting engagement, and supporting learning. Improved self- and emotion regulation, mental health, and resilience were also noted by the handler reports. Future research needs to investigate these perceived benefits using quantitative data, as well as look into outcomes relating to the dogs supporting others with bereavement and trauma. This topic was briefly mentioned by two of the schools, but there was not enough data to understand the impact in depth. Full article

This paper provides new field-based evidence on loss aversion and short-run learning using high-frequency performance data from professional golf. Leveraging over 100,000 putts recorded during the 2020 Korea Professional Golfers’ Association (KPGA) Tour, I examine how professional golfers adjust their putting behavior in response to reference-dependent incentives and immediate feedback. The structure of golf creates a natural empirical setting to test behavioral predictions: scoring rules establish salient reference points (e.g., par), while putting decisions are discrete, individually executed, and financially consequential. I find that players are significantly more likely to convert par-saving putts than birdie attempts from equivalent distances, consistent with loss aversion and reference-dependent preferences. Par putts are also executed more aggressively, but players regulate pace to avoid costly three-putt errors, indicating strategic self-regulation under loss-framed incentives. In addition, I document robust evidence of within-hole learning: second putts—taken shortly after the first under near-identical conditions—exhibit substantially higher success rates. These patterns are confirmed in logistic regression models with nonlinear distance controls and player fixed effects. This performance gap persists across scoring frames and aligns with models of reinforcement learning and dynamic belief updating. The findings illustrate how behavioral biases and adaptive learning interact in high-stakes, real-world decisions and highlight the value of professional sports data for testing core theories in behavioral economics. Full article

Achieving precise sliding perception is crucial for enhancing human–machine interactions. Despite the extensive investigation of tactile sensors for static pressure detection, they still face challenges in detecting dynamic information such as sliding direction, speed, pressure and position in interactive touch scenarios. Herein, we propose a self-powered tactile interface that realizes motion-to-electricity generation by electrostatically regulating the carrier concentration and transport in the semiconductive layer with a top gate in sliding movement. This tactile sliding interface can distinguish various dynamic mechanical information by generating voltage signals related to the sliding direction, speed, pressure, and touch position without external bias voltage. By combining machine-learning algorithms, electrical signals of six representative sliding-touch interactions were accurately classified with a recognition accuracy of 98.33%. Furthermore, by integrating sensors into the smartphone’s side button, customizable functions such as volume control, screen unlocking, and music switching were achieved. This work provides an innovative mechanism for sliding sensing in interactive electronic and intelligent control systems. Full article

This study investigates the relationships among students’ socio-contextual ecosystems including interactions with parents, teachers, and society (i.e., participation in cocurricular activities), their dual-pathway self-regulatory strategies—emotional self-regulation (SR) and motivated learning strategies (MLS)—and their mathematics achievement emotions. Drawing on data from a sample of 1269 Chinese secondary school students, the findings indicate that all dimensions of parent–teacher–society interactions significantly predict students’ mathematics achievement emotions (i.e., enjoyment and anger) through the mediation of both emotional self-regulation strategies (i.e., positive reappraisal and rumination) and motivated learning strategies. Results in this study revealed a clear differential mediation pattern: teacher–student interactions exerted significant direct effects on students’ emotions alongside indirect effects through their self-regulation (partial mediation). In contrast, the impact of social cocurricular activities was fully mediated by students’ self-regulatory processes. Notably, parent–child interactions directly influenced enjoyment but only affected anger indirectly through self-regulation. These results unpack the “black box” of how socio-contextual ecosystems shape student well-being, highlighting the critical and distinct roles of dual self-regulation pathways. The study provides a novel theoretical framework for understanding achievement emotions and offers actionable insights for building supportive, sustainable learning environments that foster both emotional and academic resilience. Full article

Despite mandatory cardiopulmonary resuscitation (CPR) training for elementary school personnel in South Korea, current programs are often delivered as uniform, compliance-oriented sessions that prioritize procedural completion over sustained engagement and self-regulated practice. However, little empirical evidence explains why some participants remain actively engaged and capable of self-managing CPR skills after mandatory training, while others do not. In this study, immersion in CPR education refers to learners’ cognitive and behavioral engagement during training, reflecting their concentration and active participation in learning activities. Accordingly, this study aimed to examine how achievement goal orientation influences CPR education immersion and self-management among elementary school educational officials. A survey was conducted from March to June 2024 with 150 teachers and administrative staff in Gangwon Province, South Korea. Data were analyzed using correlation and multiple regression analyses. The results showed significant positive correlations among achievement goal orientation, CPR education immersion, and self-management. Both self-goal and task goal orientations significantly increased CPR education immersion, whereas only task goal orientation positively influenced self-management. In addition, both cognitive and behavioral immersion significantly predicted self-management These findings suggest that mastery-oriented motivation is associated with deeper engagement during CPR training and stronger self-management, supporting motivationally informed instructional design rather than compliance-focused delivery. Full article

Artificial intelligence (AI) has reshaped contemporary approaches to teaching, assessment, and feedback. Most AI systems provide reactive feedback, offering instant answers that reduce learners’ cognitive engagement and sense of agency. In contrast, Mili was developed as a proactive pedagogical intelligence that asks guiding questions and encourages learners to construct their own responses. Through this design, feedback becomes a process of learning rather than an evaluative mechanism. Mili is a Hebrew-language educational chatbot grounded in principles of dialogic feedback, pedagogical mediation, and literacy resilience. Its goal is to create a metacognitive literacy dialogue in which questions replace answers and learning becomes an act of reflection and self-inquiry. The development followed a Design-Based Research approach involving iterative cycles of design, training, and testing. At each stage, pedagogical prompts were crafted to simulate authentic teacher–learner dialogue, including clarifying questions, pedagogical delay, and emotional reinforcement. This process enabled an exploration of how AI can mediate feedback that stimulates deeper cognitive engagement. The resulting model demonstrates proactive dialogic feedback in which AI does not simply respond but initiates reflective dialogue. Simulated interactions with Mili reveal how such feedback supports the three dimensions of literacy resilience: linguistic-cognitive, metacognitive, and emotional. Mili represents a conceptual shift in AI-based feedback, moving from response to process, from outcome to mediation, and from reactive AI to learning-generative AI. The study makes a theoretical contribution by articulating a model of pedagogically mediated AI and a practical contribution by developing a feedback tool that fosters inquiry, reflection, and literacy resilience in learners and teachers. Full article

This study investigates how holistic teaching practices and institutional support at community colleges shape science, technology, engineering, and mathematics (STEM) students’ momentum and self-efficacy in career-relevant competencies. Using survey data from three community colleges, we apply structural equation modeling (SEM) to assess these relationships while accounting for institutional variation using multi-group analysis. Our findings demonstrate that holistic teaching practices are positively associated with students’ curricular, cognitive, and meta-cognitive momentum, indicating that integrated, supportive classroom instruction contributes to sustained engagement and self-regulated learning in STEM pathways. Holistic teaching practices also show a marginal positive relationship with career readiness self-efficacy and professional and interpersonal self-efficacy, with cognitive and meta-cognitive momentum mediating these associations. In contrast, institutional support is not related to students’ momentum but is positively associated with professional and interpersonal self-efficacy, which may point to its role in shaping broader skill development independent of short-term academic engagement. These findings suggest that holistic teaching practices and institutional support differentially contribute to students’ academic momentum and career-related self-efficacy, which highlights the importance of coordinated efforts across classroom and institutional levels within the broader STEM ecosystem in fostering both short-term engagement and long-term professional competencies among diverse community college STEM learners. Full article

Distinguishing mood from emotion has long posed challenges for psychology, with persistent definitional ambiguity limiting both theoretical precision and applied effectiveness. Our early work, identified duration and cause attribution as the most reliable markers differentiating short-lived, event-linked emotions from more diffuse, enduring moods. Researchers further advanced understanding by conceptualising emotions as feedback signals that support learning and adaptation, while the 4Rs model translated these insights into applied practice by embedding cause attribution within affect regulation. This paper integrates these conceptual, functional, and applied perspectives to demonstrate why accurate classification of affective states is a functional necessity in high-performance contexts. I propose that misclassifying moods and emotions may contribute to inefficient deployment of self-regulatory resources, whereas distinguishing states based on cause attribution may support more targeted and efficient regulation. Drawing on examples from sport, healthcare, performing arts, military operations, and corporate leadership, this paper synthesizes existing work to highlight the practical implications of the mood–emotion distinction for applied psychology. Full article

Self-regulated learning (SRL) is a critical competency for learners in increasingly technology-enhanced educational environments, yet little is known about how SRL is fostered within video-based interventions in K-12 settings. While existing reviews and meta-analyses focus on the effectiveness of SRL interventions, this study aims to address current gaps by specifically examining the implementation processes, instructional tools, and the role of video. Addressing this, the present study conducted a systematic literature review of peer-reviewed K-12 intervention studies published since 2010, guided by PRISMA standards and other methodological frameworks in the field of SRL. 30 quantitative or mixed-methods studies focusing on K-12 SRL interventions were selected from Web of Science and ERIC, with the requirement that video served as an instructional component rather than a research tool. These studies were then systematically coded by eight researchers for SRL strategies, instructional methods, video roles, and pedagogical settings. Findings show that most video interventions targeted multiple SRL strategies across different phases of the SRL cycle, offering a comprehensive approach to fostering regulation. However, while cognitive and metacognitive strategies were frequently addressed, motivational and resource-management strategies were seldom included within explicit instruction, which focused primarily on cognitive and metacognitive training. Video played multiple pedagogical roles, including delivering disciplinary content, teaching SRL strategies, or combining both. A thematic analysis identified four pedagogical settings that characterized successful interventions: Teacher-guided, Active, Social, and Knowledge-based (TASK) learning. These settings appear to mitigate common challenges of video-based learning, such as cognitive load and learner passivity. The review contributes a novel synthesis of SRL-video integration and proposes TASK learning as a framework for designing SRL interventions. Full article

The author investigates how digital learning competence (DLC) is bridged to academic achievement (AA) through informal digital learning engagement (IDLE) and how meta-cognitive self-regulation (MSR) shapes these pathways among university students. Grounded in a moderated mediation framework, this research conceptualizes DLC not as a static skill set but as a latent capacity that is channeled into academic outcomes when students autonomously engage in digital environments and regulate their cognition. Survey data were collected from 432 undergraduate students and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that DLC significantly predicts AA both directly and indirectly via IDLE, identifying informal digital learning engagement as a central pathway through which digital learning competence is translated into academic gains. Furthermore, MSR moderates the relationship between DLC and IDLE, such that higher levels of metacognitive self-regulation strengthen the conversion of digital learning competence into productive informal digital learning engagement. These findings support a dynamic view of digital learning competence and underscore the roles of learner autonomy and metacognitive awareness in transforming digital skills into meaningful educational outcomes. By integrating perspectives on digital literacy, self-regulated learning, and informal learning, this study offers implications for the design of digital learning ecosystems that effectively bridge students’ digital capacities with their academic achievement. Full article

Bio-inspired and bio-based materials are increasingly recognized as powerful enablers of climate-responsive and low-carbon architecture. By learning from natural systems, such as adaptability, self-regulation, and resource efficiency, these materials offer promising solutions to the escalating environmental pressures faced by the built environment. However, their systematic integration into building design remains limited, particularly in tropical monsoon climates. To address this gap, this study applies the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to identify, prioritize, and map the interdependencies among ten bio-based and bio-inspired strategies for sustainable building design. The results highlight five dominant solutions: living building systems, bio-composite exterior cladding for weather resistance, mycelium-based insulation for humidity control, bio-based natural ventilation and passive cooling, and bio-inspired self-shading systems. The causal analysis reveals three key characteristics: (1) living building systems function as a central integrative nexus, (2) bio-composite cladding acts as a primary driver of durability and climate resilience, and (3) bio-based water filtration and local timber exhibit lower systemic leverage despite their environmental benefits. Theoretically, this study advances biomimetic design research by introducing a causal, system-level framework for understanding interactions among nature-inspired strategies. Practically, it provides architects, engineers, and policymakers with an evidence-based decision-support tool to prioritize climate-adapted, bio-inspired solutions, contributing to the development of resilient and regenerative architecture in rapidly changing climates. 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

Automatic sleep staging from polysomnography is challenged by marked spectro-temporal heterogeneity and non-stationary cross-channel artifacts, which often undermine naïve multimodal fusion. To address this, a Gated Cross-Modal and Temporal Fusion Network (G-CMTF Net) is proposed as an end-to-end model operating on 30 s EEG epochs and auxiliary EOG and EMG signals, in which cross-modal contributions are regulated through reliability-aware gating. A spectro-temporal disentanglement frontend learns multi-scale temporal features while incorporating FFT-derived band-power embeddings to preserve physiologically meaningful oscillatory cues. At the epoch level, gated fusion suppresses artifact-prone auxiliary inputs, thereby limiting noise transfer into a shared latent space. Long-range sleep dynamics are modeled via a convolution-augmented self-attention encoder that captures both local morphology and transition structure. On Sleep-EDF-20 and Sleep-EDF-78, G-CMTF Net achieves Macro-F1/ACC of 81.3%/85.5% and 78.2%/83.4%, respectively, while maintaining high sensitivity and geometric-mean performance on transitional epochs, consistent with the function of reliability-aware gated fusion under non-stationary auxiliary artifacts. From a symmetry perspective, the proposed framework enforces a structured balance between heterogeneous modalities by promoting representational consistency while adaptively suppressing asymmetric noise contributions. Full article

The digital transformation of education necessitates the integration of digital competences into teacher training programs, particularly in subjects such as mathematics, informatics and information technologies. This study explores how digitalisation influences the development of digital competences among pre-service teachers at the Faculty of Mathematics and Informatics of Sofia University St. Kliment Ohridski. This article uses a triangulated research approach, combining curriculum documentation, faculty self-assessments and classroom observations, to examine the alignment between the ideal, formal, perceived, operational and experiential levels of digital competence development, based on John Goodlad’s five-level curriculum typology and Jo Tondeur’s SQD 2.0 model. The findings reveal significant discrepancies between the intended, the implemented and the experienced curriculum. Although ICT-focused disciplines strongly embed digital competences, non-ICT subjects show fragmented and inconsistent integration. Faculty staff self-assessments indicate high confidence in creating digital resources and assessment strategies, gaps persist in reflective practice, computational thinking, inclusion and self-regulated learning. Classroom observations confirm limited use of emerging technologies and insufficient development of collaborative digital learning environments. The study underscores the gap between policy requirements, faculty practices and classroom realities. The discussion emphasizes the need for systemic reforms in teacher education, offering insights for policymakers, curriculum designers and training programs. Full article