Search Results (1,000)

Serious games that integrate educational content with engaging gameplay mechanics hold promise for reducing cognitive load and increasing student motivation in STEM and health science education. This preliminary study presents the development and evaluation of the Medimon NASA Demo, a game-based learning prototype designed to teach undergraduate students about the musculoskeletal and visual systems—two critical domains in space medicine. Participants (n = 23) engaged with the game over a two-week self-regulated learning period. The game employed mnemonic-based characters, visual storytelling, and turn-based battle mechanics to reinforce medical concepts. Quantitative results demonstrated significant learning gains, with posttest scores increasing by an average of 23% and a normalized change of c = 0.4. Engagement levels were high across multiple dimensions of situational interest, and 74% of participants preferred the game over traditional formats. Qualitative analysis of open-ended responses revealed themes related to intrinsic appeal, perceived learning efficacy, interaction design, and cognitive resource management. While the game had minimal impact on short-term STEM career interest, its educational potential was clearly supported. These findings suggest that mnemonic-driven serious games like Medimon can effectively enhance engagement and learning in health science education, especially when aligned with real-world contexts such as space medicine. Full article

Objective: Considering the absence of research testing the entire sequence of passion, behavioral regulation, and subjective well-being (SWB), this study aimed to analyze the hypothetical causal relationships between the variables of a model that integrates the Dualistic Passion Model (DMP) and Self-Determination Theory (SDT) in order to understand the impact of harmonious passion (HP) and obsessive passion (OP) and the regulation of motivation on the SWB of elite athletes with disability. Method: This study includes 143 elite athletes from national adapted sports (36 female; 107 male) aged between 15 and 59 (M = 29.21; SD = 10.45). Weekly training sessions ranged from 1 to 12 (M = 4.52; SD = 2.71), and the years of competitive practice ranged from 1 to 28 (M = 5.55; SD = 6.98). Data were collected using valid and reliable questionnaires for the study population and analyzed using structural equation analysis. The following results were identified: a positive and a significant effect between OP and self-determination motivation (SDM); a positive but not a significant effect between OP and non-self-determination motivation (NSDM); a significant effect between SDM and SWL and SDM and positive affect (PA); and, finally, a positive but non-significant effect between SDM and negative affect (NA). In contrast, there is a positive and significant effect between HP and SDM; a negative and significant effect between HP and NSDM; a positive but non-significant effect between NSDM and SWL; a negative and significant effect between NSDM and PA; and, finally, a positive and significant effect between NSDM and NA. Conclusions: The perception of passion regarding sport can be a positive predictor of SDM, which, in turn, can influence levels of SWB, both from a cognitive point of view (SWL) and from an emotional point of view (PA). This reinforces the positive effect of the self-determination behavior in adapted sport on SWB and its contribution to health and quality of life in people with disabilities. Full article

The organization of consciousness is described through increasingly rich theoretical models. We review evidence that working memory capacity—essential to generating consciousness in the cerebral cortex—is supported by dual limbic memory systems. These dorsal (Papez) and ventral (Yakovlev) limbic networks provide the basis for mnemonic processing and prediction in the dorsal and ventral divisions of the human neocortex. Empirical evidence suggests that the dorsal limbic division is (i) regulated preferentially by excitatory feedforward control, (ii) consolidated by REM sleep, and (iii) controlled in waking by phasic arousal through lemnothalamic projections from the pontine brainstem reticular activating system. The ventral limbic division and striatum, (i) organizes the inhibitory neurophysiology of NREM to (ii) consolidate explicit memory in sleep, (iii) operating in waking cognition under the same inhibitory feedback control supported by collothalamic tonic activation from the midbrain. We propose that (i) these dual (excitatory and inhibitory) systems alternate in the stages of sleep, and (ii) in waking they must be balanced—at criticality—to optimize the active inference that generates conscious experiences. Optimal Bayesian belief updating rests on balanced feedforward (excitatory predictive) and feedback (inhibitory corrective) control biases that play the role of prior and likelihood (i.e., sensory) precision. Because the excitatory (E) phasic arousal and inhibitory (I) tonic activation systems that regulate these dual limbic divisions have distinct affective properties, varying levels of elation for phasic arousal (E) and anxiety for tonic activation (I), the dual control systems regulate sleep and consciousness in ways that are adaptively balanced—around the entropic nadir of E–I criticality—for optimal self-regulation of consciousness and psychological health. Because they are emotive as well as motive control systems, these dual systems have unique qualities of feeling that may be registered as subjective experience. Full article

Advances in neuroscience have improved the understanding of cognitive, emotional, and social processes involved in learning. Simultaneously, technologies such as artificial intelligence, augmented reality, and gamification are transforming educational practices. However, their integration into formal education remains limited and often misapplied. This study aims to evaluate the impact of technology-supported neuroeducational models on student learning and well-being. A systematic review was conducted using PubMed, the Web of Science, ScienceDirect, and LILACS, including open-access studies published between 2020 and 2025. Selection and methodological assessment followed PRISMA 2020 guidelines. Out of 386 identified articles, 22 met the inclusion criteria. Most studies showed that neuroeducational interventions incorporating interactive and adaptive technologies enhanced academic performance, intrinsic motivation, emotional self-regulation, and psychological well-being in various educational contexts. Technology-supported neuroeducational models are effective in fostering both cognitive and emotional development. The findings support integrating neuroscience and educational technology into teaching practices and teacher training, promoting personalized, inclusive, and evidence-based education. Full article

Defensive pessimism, an important emotion regulation and motivation strategy, has increasingly attracted scholarly attention in psychology. Recently, sensor-based methods have begun to supplement or replace traditional questionnaire surveys in personality research. However, current approaches for collecting vital signs data face several challenges, including limited monitoring durations, significant data deviations, and susceptibility to external interference. This paper proposes a novel approach using a NiCr/NiSi alloy film temperature sensor, which has a K-type structure and flexible piezoelectric pressure sensor to identify and predict defensive pessimism personality traits. Experimental results indicate that the Seebeck coefficients for K-, T-, and E-type thermocouples are approximately 41 μV/°C, 39 μV/°C, and 57 μV/°C, respectively, which align closely with national standards and exhibit good consistency across multiple experimental groups. Moreover, radial artery frequency experiments demonstrate a strong linear relationship between pulse rate and the intensity of external stimuli, where stronger stimuli correspond to faster pulse rates. Simulation experiments further reveal a high correlation between radial artery pulse frequency and skin temperature, and a regression model based on the physiological sensor data shows a good fit (p < 0.05). These findings verify the feasibility of using temperature and flexible piezoelectric pressure sensors to identify and predict defensive pessimism personality characteristics. Full article

In crisis-stricken economies, leadership effectiveness increasingly hinges not on technical expertise alone but on emotional competence. While emotional intelligence (EI) has been widely acknowledged as a catalyst for effective leadership and employee outcomes, its role in volatile and resource-scarce contexts remains underexplored. This study addresses this critical gap by investigating the impact of five core EI dimensions, namely self-awareness, self-regulation, motivation, empathy, and social skills, on employee performance amid Lebanon’s ongoing multidimensional crisis. Drawing on Goleman’s EI framework and the Job Demands–Resources theory, the research employs a quantitative, cross-sectional design with data collected from 398 employees across sectors in Lebanon. Structural Equation Modeling revealed that all EI dimensions significantly and positively influenced employee performance, with self-regulation (β = 0.485) and empathy (β = 0.361) emerging as the most potent predictors. These findings underscore the value of emotionally intelligent leadership in fostering productivity, resilience, and team cohesion during organizational instability. This study contributes to the literature by contextualizing EI in an under-researched, crisis-affected setting, offering nuanced insights into which emotional competencies are most impactful during prolonged uncertainty. Practically, it positions EI as a strategic leadership asset for crisis management and sustainable human resource development in fragile economies. The results inform leadership training, policy design, and organizational strategies that aim to enhance employee performance through emotionally intelligent practices. Full article

Participation in organized sport is widely encouraged for youth development, yet positive outcomes are not guaranteed. Parents play a pivotal role in shaping young athletes’ experiences, requiring emotional support, interpersonal skills, and self-regulation. This study examines the meanings parents attribute to their children’s sports participation and how young athletes construct their experiences under parental and coaching influences. An interpretive phenomenological methodology involved semi-structured interviews with coaches, focus groups with parents, and open-ended questionnaires to young athletes. Seventeen players (M = 11.2 years, SD = 0.59), nineteen parents (M = 47.6 years, SD = 3.61), and two coaches from the same football club volunteered to participate in the study. Participants were selected through purposive sampling to ensure a homogeneous experience. The findings reveal that parental involvement balances support and pressure, while trust-building between parents and coaches significantly impacts the athletes’ experiences. The evolving role of technology and the importance of social dynamics within teams also emerged as critical factors. Intrinsic motivation, fostering emotional bonding through the sport, and adopting a developmental rather than purely competitive framework were emphasized factors identified as supporting positive youth sport experiences. These findings offer insights into how interconnected relationships among parents, coaches, and athletes influence children’s sports engagement and development. Full article

Introduction: The main purpose of this study was to test a hierarchical model of motivation that integrates Achievement Goal Theory and Self-Determination Theory to explain and predict exercise adherence. Method: In total, 2180 exercisers (1020 female, 1160 male) aged between 18 and 60 years, from different gyms and health clubs, completed several scales validated in exercise settings, regarding perceived motivational climate, basic psychological need satisfaction, behavioral regulation, and exercise adherence. For the last measure, weekly computer access to a control system over a 6-month period before and after data collection was consulted. Results: Through structural equation models (SEM), it was verified that (1) task-involving climate positively predicted basic psychological needs. In turn, the satisfaction of these needs predicted autonomous motivation, which led to a positive prediction of adherence; (2) a small variation in exercise adherence was explained by the motivational model under analysis. Nevertheless, models significantly improved their analytical power when past adherence was inserted in the model increasing the explained variance in future behavior from 9.2% to 64%. Conclusions: In conclusion, autonomous motivation can predict people’s exercise adherence, and past behavior increases that predictive effect. The present study brings scientific evidence to the popular saying “tell me what you’ve done and, and I’ll predict what you’ll do”. Full article

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

This review surveys recent advances and emerging prospects in phase-transfer catalysis (PTC) for fuel desulfurization. In response to increasingly stringent environmental regulations, the removal of sulfur from transportation fuels has become imperative for curbing SO_x emissions. Conventional hydrodesulfurization (HDS) operates under severe temperature–pressure conditions and displays limited efficacy toward sterically hindered thiophenic compounds, motivating the exploration of non-hydrogen routes such as oxidative desulfurization (ODS). Within ODS, PTC offers distinctive benefits by shuttling reactants across immiscible phases, thereby enhancing reaction rates and selectivity. In particular, PTC enables efficient migration of organosulfur substrates from the hydrocarbon matrix into an aqueous phase where they are oxidized and subsequently extracted. The review first summarizes the deployment of classic PTC systems—quaternary ammonium salts, crown ethers, and related agents—in ODS operations and then delineates the underlying phase-transfer mechanisms, encompassing reaction-controlled, thermally triggered, photo-responsive, and pH-sensitive cycles. Attention is next directed to a new generation of catalysts, including quaternary-ammonium polyoxometalates, imidazolium-substituted polyoxometalates, and ionic-liquid-based hybrids. Their tailored architectures, catalytic performance, and mechanistic attributes are analyzed comprehensively. By incorporating multifunctional supports or rational structural modifications, these systems deliver superior desulfurization efficiency, product selectivity, and recyclability. Despite such progress, commercial deployment is hindered by the following outstanding issues: long-term catalyst durability, continuous-flow reactor design, and full life-cycle cost optimization. Future research should, therefore, focus on elucidating structure–performance relationships, translating batch protocols into robust continuous processes, and performing rigorous environmental and techno-economic assessments to accelerate the industrial adoption of PTC-enabled desulfurization. Full article

Despite the increasing number of studies indicating that generative artificial intelligence is conducive to cultivating college students’ critical thinking skills, research on the impact of college students’ use of generative artificial intelligence on their critical thinking skills in an open learning environment is still scarce. This study aims to investigate whether the use of generative artificial intelligence by college students in an open learning environment can effectively enhance their critical thinking skills. The study is centered around the following questions: Does the use of generative artificial intelligence in an open learning environment enhance college students’ critical thinking skills (what)? What is the mechanism by which the use of generative artificial intelligence affects college students’ critical thinking (how)? From the perspective of self-regulated learning theory and learning motivation theory, what are the reasons for the impact of generative artificial intelligence on college students’ critical thinking skills (why)? To this end, the study employs questionnaires and interviews to collect data. The questionnaire data are subjected to descriptive statistical analysis, correlation analysis, multiple stepwise regression analysis, and mediation effect analysis. Based on the analysis of interview materials and survey questionnaire data, the study reveals the impacts and mechanisms of college students’ use of generative artificial intelligence tools on their critical thinking skills. The findings of the study are as follows. First, the frequency of artificial intelligence use is unrelated to critical thinking skills, but using it for reflective thinking helps to develop critical thinking skills. Second, students with strong self-regulated learning skills are more likely to use generative artificial intelligence for reflective thinking and achieve better development in critical thinking skills. Third, students with strong intrinsic learning motivation are more likely to use generative artificial intelligence for reflective thinking and achieve better development in critical thinking skills. Consequently, the article analyzes the reasons from the perspectives of self-regulated learning theory and learning motivation theory and offers insights into how to properly use generative artificial intelligence to promote the development of critical thinking skills from the perspectives of higher education institutions, college teachers, and college students. Full article

(1) Background: Wetlands are ecologically significant ecosystems that support biodiversity and contribute to essential environmental functions such as water purification, carbon storage and flood regulation. However, these ecosystems face increasing pressures from land-use change and degradation, prompting the need for scalable and accurate classification methods to support conservation and policy efforts. In this research, our motivation was to test whether high-spatial-resolution PlanetScope imagery can be used with pixel-based machine learning to support the mapping and monitoring of wetlands at a national scale. (2) Methods: This study compared four machine learning classification models—Random Forest (RF), XGBoost (XGB), Histogram-Based Gradient Boosting (HGB) and a Multi-Layer Perceptron Classifier (MLPC)—to detect and map wetland areas across New Zealand. All models were trained using eight-band SuperDove satellite imagery from PlanetScope, with a spatial resolution of ~3 m, and ancillary geospatial datasets representing topography and soil drainage characteristics, each of which is available globally. (3) Results: All four machine learning models performed well in detecting wetlands from SuperDove imagery and environmental covariates, with varying strengths. The highest accuracy was achieved using all eight image bands alongside features created from supporting geospatial data. For binary wetland classification, the highest F1 scores were recorded by XGB (0.73) and RF/HGB (both 0.72) when including all covariates. MLPC also showed competitive performance (wetland F1 score of 0.71), despite its relatively lower spatial consistency. However, each model over-predicts total wetland area at a national level, an issue which was able to be reduced by increasing the classification probability threshold and spatial filtering. (4) Conclusions: The comparative analysis highlights the strengths and trade-offs of RF, XGB, HGB and MLPC models for wetland classification. While all four methods are viable, RF offers some key advantages, including ease of deployment and transferability, positioning it as a promising candidate for scalable, high-resolution wetland monitoring across diverse ecological settings. Further work is required for verification of small-scale wetlands (<~0.5 ha) and the addition of fine-spatial-scale covariates. Full article

This study explores the potential of immersive virtual reality (IVR) to enhance knowledge transfer in vocational education, particularly in bridging the gap between academic learning and practical workplace application. The focus lies on relevant predictors for actual learning transfer, namely knowledge acquisition and the transfer-related self-efficacy. Additionally, the Cognitive Affective Model of Immersive Learning (CAMIL) is used to investigate potential predictors in IVR learning. This approach allows for empirical testing of the CAMIL and validation of its assumptions using empirical data. To address the research questions, a quasi-experimental field study was conducted with 141 retail trainees at a German vocational school. Participants were assigned to either an IVR group or a control group receiving traditional instruction. The intervention spanned four teaching sessions of 90 min each, focusing on the design of a retail sales area based on sales-promoting principles. To assess subject-related learning outcomes, a domain-specific knowledge test was developed. In addition, transfer-related self-efficacy and other relevant constructs were measured using Likert-scale questionnaires. The results show that IVR-based instruction significantly improves knowledge acquisition and transfer-related self-efficacy compared to traditional teaching methods. In terms of the CAMIL-based mechanisms, significant correlations were found between transfer-related self-efficacy and factors such as interest, motivation, academic self-efficacy, embodiment, and self-regulation. Additionally, correlations were found between knowledge acquisition and relevant predictors such as interest, motivation, and self-regulation. These findings underscore IVR’s potential to facilitate knowledge transfer in vocational school, highlighting the need for further research on its long-term effects and the actual application of learned skills in real-world settings. Full article

Background/Objectives: Decision-analytic Bayesian approaches are ideally suited for designing clinical trials. They have been used increasingly over the last 30 years in developing medical devices and drugs. A prototype trial is a bandit problem in which treating participants is as important as treating patients in clinical practice after the trial. Methods: This article chronicles the use of the Bayesian approach in clinical trials motivated by bandit problems. It provides a comprehensive historical and practical review of Bayesian adaptive trials, with a focus on bandit-inspired designs. Results: The 20th century saw advances in Bayesian methodology involving computer simulation. In the 21st century, methods motivated by bandit problems have been applied in designing scores of actual clinical trials. Fifteen such trials are described. By far the most important Bayesian contributions in clinical trials are the abilities to observe the accumulating results and to modify the future course of the trial on the basis of these observations. In the spirit of artificial intelligence, algorithms are programmed to learn the optimal treatment assignments over the remainder of the trial. Conclusions: Bayesian trials are still nascent and represent a small minority of clinical trials, but their existence is changing the way investigators, regulators, and government and industry sponsors view innovation in clinical trials. Full article

Urban wetlands are increasingly recognized for their ecological and cultural benefits, yet remain underutilized due to limited public awareness and environmental literacy. This study investigates how visitors’ perceptions of wetland ecosystem services influence their motivations to engage with a constructed wetland in Putrajaya, Malaysia. By integrating the ecosystem services framework with push-pull motivation theory, the research aims to bridge knowledge gaps and inform sustainable wetland tourism planning. A structured questionnaire was administered to 420 visitors, with 385 valid responses (response rate: 91.7%). Data were analyzed using non-parametric tests (Kruskal–Wallis, Spearman correlation) and multiple regression analysis. Results show that cultural and regulating services are perceived most positively, while emotional restoration and aesthetic appreciation emerged as key motivational drivers. Regression findings reveal that push factors are stronger predictors of ecosystem service engagement than pull factors. These insights highlight the importance of emotional and psychological connections to nature, offering practical implications for urban wetland management, visitor education and environmental communication strategies. Full article