Search Results (4,694)

This study examined engineering students’ intrapersonal factors, self-regulated learning (SRL) and achievement emotions, and their prior emergency, disaster, and crisis management (EDCM) knowledge, within a three-week EDCM module embedded in an engineering course. We collected data at three points during the implementation of the EDCM module N = 53). First, before the module began, students completed a demographic questionnaire, the SRL motivational orientation questionnaire, and an EDCM knowledge pretest. Second, during module implementation, students completed the SRL learning strategy questionnaire. Third, after the module was completed, students completed the achievement emotion questionnaire. We included 53 students’ data and analyzed it using non-parametric statistics. Results showed no significant gender differences in learning strategies or achievement emotions; however, male students’ motivation was significantly higher than that of female students. In addition, significant differences by race were found in motivational orientation, learning strategies, achievement emotions, and prior EDCM knowledge. Motivational orientation was positively associated with positive emotions, particularly enjoyment and pride, and negatively associated with negative emotions. Learning strategies, meanwhile, were primarily associated with positive emotions. Prior EDCM knowledge showed limited associations with intrapersonal factors. These findings highlight the importance of redesigning EDCM instruction in engineering education to be both effective and more responsive. Full article

Teacher discipline serves as a vital instructional and managerial tool for facilitating adolescent development and exerts profound influences on students’ physical and mental well-being. Grounded in expectancy violation theory, deservingness justice theory, and life history theory, the present study adopts data from the China Education Panel Survey (China Education Panel Survey, CEPS, 2013–2014) to explore the association between teacher disciplinary intensity and adolescents’ mental health problems and rigorously tests violation severity as the first-stage moderator and family migration status as the second-stage moderator within a moderated moderation framework. This study employs cross-sectional data from a nationally representative sample of 17,788 adolescents, and statistical analyses, including descriptive statistics, bivariate correlations, and moderation effect analyses, were conducted using SPSS 24.0 and the PROCESS macro. The empirical results demonstrate significant positive associations between teacher disciplinary intensity, student violation severity, and adolescent mental health difficulties. Family migration status is negatively correlated with perceptions of disciplinary appropriateness and the degree of student violation. Furthermore, violation severity negatively moderates the predictive relationship between discipline intensity and mental health problems. Although intensified teacher discipline significantly predicts higher levels of adolescent mental health problems across both high- and low-violation contexts, such adverse effects are substantially weakened when student violations are severe. Critically, the interactive pattern between discipline intensity and violation severity is more salient for migrant adolescents than for their non-migrant peers. These findings offer a clear theoretical explanation for why reasonable and equitable school discipline can protect and promote adolescents’ mental health. Full article

This study compares the effectiveness of Generative AI-supported instruction and strategic dictionary use on upper elementary students’ receptive and expressive vocabulary development. A quasi-experimental pretest–posttest design was employed with 136 students aged 10–12, including monolingual and bilingual learners, who were assigned to three conditions: Generative AI-supported instruction, strategic dictionary use, and a control group. Receptive vocabulary was assessed with the LEXiS standardized test, while expressive vocabulary was measured using the Greek adaptation of the Renfrew Word Finding Vocabulary Test. Both interventions focused on climate change vocabulary and were implemented over four weeks, incorporating parallel receptive and productive vocabulary-learning activities. General Linear Models revealed significant effects of intervention type on receptive vocabulary development, with both the AI-supported and dictionary-based groups outperforming the control group. No significant differences emerged between the two experimental conditions. For expressive vocabulary, no overall intervention effect was observed; however, a significant interaction between intervention type and bilingualism indicated that bilingual students in the dictionary-based condition achieved significantly greater productive vocabulary gains than their peers in the AI and control groups. The findings suggest that Generative AI and strategic dictionary instruction support different dimensions of lexical development and should be viewed as complementary rather than competing pedagogical approaches. Full article

Feedback loops are fundamental to understanding complex systems across the sciences, yet undergraduate students consistently struggle to correctly identify and distinguish positive and negative feedback loops. Traditional instructional approaches, which typically provide a definition and a single example, often fail to produce robust understanding. We tested an alternative pedagogical approach grounded in cognitive science research on analogical reasoning: mutual alignment of analogies. In this approach, students compare two feedback loop examples drawn from different domains (e.g., Earth science and economics) and identify their shared structural features, thereby extracting the abstract schema of a feedback loop. We compared student performance across two consecutive offerings of an introductory geoscience course at a large urban university. In the first year (Year 1, n = 71), feedback loops were taught using a traditional definition-plus-example approach. In the second year (Year 2, n = 100), a mutual alignment activity was introduced. Students in the mutual alignment condition outperformed those in the traditional instruction condition at all three time points following instruction, with a large effect size on the immediate assessment (Cohen’s d = 0.86) that persisted at three months (Cohen’s d = 0.51). We also identified two factors that may impede feedback loop understanding: the perceived desirability of a loop’s outcome and the readability of the loop description. Full article

This study examines the effectiveness of a virtual reality (VR) application as a learning tool in higher hospitality education, focusing on declarative knowledge acquisition and test-based learning outcomes. Conducted as a classroom-based pilot study, it involved a census of undergraduate students enrolled in a restaurant management course (N = 28) and compared VR-supported learning with traditional instruction using printed materials. Declarative knowledge acquisition was assessed using identical multiple-choice pre- and post-tests. Results showed that scores improved across all students. A statistically significant time × group interaction indicated that the two groups improved at different rates over time. Students who learned using printed materials demonstrated greater improvement than those who used the VR application. The findings suggest that, in its implementation, the VR application did not demonstrate superior test-based knowledge outcomes compared to traditional learning. One possible explanation lies in the type of knowledge assessed: the test focused on declarative and recognition-based knowledge, whereas VR may be more effective for procedural and experience-based learning. The limited interactivity of the early-stage VR application (VR HOTEL v0.9.4) may have also constrained its instructional potential. Overall, the results highlight the importance of aligning VR-based learning activities with learning objectives, instructional design, and assessment methods. Full article

This study analyzes the relationship between verbal interaction and eye behavior among 112 primary school students in urban and rural classrooms in Chile, using wireless eye-tracking technology. The results reveal statistically significant differences based on socioeducational context and sex. Linear regression analyses show that gaze is a significantly more robust predictor of class participation in rural contexts (R² adjusted = 0.671) than in urban contexts (R² adjusted = 0.342). Furthermore, eye behavior explained 71% of the variance in male students, compared to 37.7% in female students. While female students focused their attention primarily on teachers, male students relied on a shared visual distribution between the teacher and peers to regulate their participation in class. In conclusion, the gaze acts as a differentiated scaffolding whose importance intensifies in boys and rural environments. These findings suggest distinct maturational trajectories that require teachers to implement visually intentional instructional strategies to ensure communicative efficiency in the classroom. Full article

Developing higher-order thinking skills in general, and question-asking skills in particular, is considered a cornerstone for fostering critical and cognitive thinking skills. Question-asking is a key component in student engagement toward understanding complex scientific concepts. This study investigates two innovative dimensions in chemistry education: the integration of nanoscience and nanotechnology aspects into chemistry instruction, and its impact on development of question-asking skills among 11th-grade high school students. Using a quasi-experimental design, 120 students were divided equally into experimental and control groups. Both groups read a simplified scientific text on redox reactions; the control group’s text addressed redox reactions only, while the experimental group’s text had nanoscience aspects embedded within the same redox context. Students’ questions were evaluated by five judges using a validated four-level questioning hierarchy, ranging from factual to complex integrative knowledge. Results revealed significant differences in both the number and cognitive level of questions generated by the two groups. Students exposed to the nano-integrated text demonstrated markedly higher question-asking skills. In conclusion, integrating nanoscience into chemistry instruction was shown to be a powerful gateway to developing higher-order thinking, enabling students to ask more cognitively advanced questions, deepen their scientific understanding, and engage more meaningfully with scientific content. Full article

Background: Reliable structured-output generation is a prerequisite for using large language models (LLMs) in automated clinical documentation workflows, but many evaluations focus on clinical quality before testing whether outputs are parseable, schema-compliant, and stable. Methods: We evaluated three locally deployed open-weight LLMs in the 7- to 8-billion-parameter range (Llama3-Med42-8B, Meta-Llama-3-8B-Instruct, and Mistral-7B-Instruct-v0.3) for structured admission-note editing. Seventy de-identified English-language admission notes (35 internal medicine and 35 surgical) were processed by each model in three independent runs under two output-control conditions: a free-text JSON prompt and a schema-enforced structured-output condition. A total of 1260 local inferences were performed in LM Studio on consumer-grade hardware. Automated proxy metrics assessed JSON/schema validity, run-to-run stability, instruction compliance, verbosity, numeric-token preservation, and uncertainty-marker change without clinician adjudication of clinical correctness. Results: Under the free-text JSON prompt, the tested Mistral-7B-Instruct-v0.3/embedded-prompt configuration had the weakest structural reliability (74.3–78.6% first-pass validity per run; 18.6–21.4% persistent parse/schema failures after retry), with at least one final failure for 17 of 70 notes. In a message-format sensitivity analysis using Meta-Llama-3-8B-Instruct, embedding system instructions in the user message increased first-attempt invalid outputs compared with separate system/user roles (55/700, 7.9% vs. 12/700, 1.7%). Under schema enforcement, all models produced 70 of 70 first-pass valid, schema-compliant outputs in every run. Documentation behavior nevertheless differed by model, including differences in verbosity and numeric-token preservation. Conclusions: Schema enforcement removed parsing failures in this sample but did not eliminate model-specific editing behavior. Proxy-based screening can identify structurally unstable model-prompt or model-format configurations before clinician review. Full article

Large language models (LLMs) can assist lesson planning, but simple prompts often yield incomplete and misaligned outputs. This study proposes a three-step prompt framework grounded in Bloom’s taxonomy, Adaptive Control of Thought-Rational (ACT-R) theory, Gagné’s nine events, and problem-chain theory, decomposing planning into objective, unit, and activity design. Using three DeepSeek models (R1, V3, 32B) and five prompting strategies, 150 lesson plans were generated on ten computer networking topics. Coverage of Gagné’s nine events and functional quality were evaluated via an LLM judge and human validation. All theory-based strategies significantly outperformed naive prompting, raising Gagné’s event coverage above 90% in the full corpus and from 74.1% to 89.8–93.5% in human ratings. Functional quality scores improved by up to 17.3% (LLM judge) and 53.6% (human raters). Gagné’s five-stage design outperformed ACT-R’s three-stage design under base conditions, while problem-chain guidance benefited ACT-R substantially. Model capability moderated gains: smaller models benefited most in structural completeness, stronger reasoners achieved higher absolute quality. These findings demonstrate that pedagogically grounded, multi-stage prompts are designed to reconfigure teacher-artificial intelligence (AI) interaction from passive output consumption toward structured collaborative design, offering a scalable intervention for integrating LLMs into instructional workflows. Full article

Transparent objects such as glass containers, test tubes, and plastic bottles are common in robotic manipulation scenarios, but their refractive and reflective surfaces produce incomplete RGB-D geometry and make task-specific grasp selection unreliable. This paper presents an integrated vision-language system for transparent object perception and task-oriented grasping. First, we construct VLM-DRE, a transparent object image instruction dataset with 12,700 images and 38,100 image-instruction-bounding-box triplets. LoRA fine-tuning of Molmo-7B improves target click accuracy from 86.4% to 91.5% and IoU@0.75 from 57.5% to 69.1%. Second, MSR-Net performs monocular depth completion and mask prediction using multi-scale adaptive feature fusion and progressive feature refinement, achieving RMSE 0.066, mAP 98.61%, and IoU 94.12% on Syn-TODD, and RMSE 0.118, mAP 99.02%, and IoU 87.95% on ClearPose. Third, LMF-Net combines RGB-D cross-modal fusion with learnable multi-factor matching to rank AnyGrasp 6-DoF candidates, reaching 77.8% Top-1 and 90.5% Top-3 accuracy on TaskGrasp-Image and improving PRISM-Real success from 61.1% to 68.5%. On a RealSense D435i–Unitree Z1 Pro platform, the complete system obtains 85.4% success with manual clicks and 71.3% with VLM-predicted clicks, supporting perception-to-grasping integration while highlighting target localisation and runtime as deployment bottlenecks. Full article

The present study examined whether seventh-grade female students in Oman who were taught through the generative learning model showed higher posttest science achievement and motivation to learn science than students who received regular instruction. The study used a pretest-posttest quasi-experimental design with non-equivalent groups. There were 56 student participants, with 28 in the experimental group and 28 in the control group. The experimental group was taught using a teacher’s guide organized around the stages of the generative learning model, whereas the control group received regular instruction for the same Grade 7 science unit. Student outcomes were assessed using an academic achievement test covering knowledge, application, and reasoning, and a motivation toward learning science scale. Pretest evidence indicated baseline equivalence between the two groups on achievement, Wilks’ Lambda = 0.939, F(3, 52) = 1.13, p = 0.346, and motivation, Wilks’ Lambda = 0.943, F(6, 49) = 0.489, p = 0.807. Posttest comparisons showed higher achievement and motivation scores for the experimental group under the conditions of the study. The findings provide preliminary context-specific evidence that generative learning may be associated with stronger cognitive and motivational outcomes in middle-school science, although the intact-class design warrants cautious interpretation. Full article

Background/Objectives: This study aims to evaluate the effectiveness of binaural beats (BB) on the autonomic nervous system (ANS), kinesiophobia of reinjury and fitness biomarkers in professional athletes with chronic musculoskeletal injuries. Methods: A total of 54 athletes aged 18–25 years, engaged in open-skill sports and experiencing chronic lower-limb injuries for over three months, will be recruited. They will be randomly assigned to one of three groups: an intervention group, a placebo group, and a control group. All athletes will undergo a 15 min auditory intervention five times per week for four weeks. The intervention group will listen to BB embedded in music at high beta and gamma frequencies (30–100 Hz) with guided instructions. The placebo group will receive placebo BB within the same music framework, while the control group will listen only to the music background. Assessments will be conducted at baseline, immediately post-intervention, and at a 4-week follow-up. The primary outcomes will be kinesiophobia of reinjury, ANS function, and heart rate variability (HRV), assessed using the Tampa Scale of Kinesiophobia (TSK-17), Sympathetic Skin Response (SSR), and HRV analysis, respectively. Secondary outcomes will include sport-related anxiety and pain beliefs, assessed using the Sport Competition Anxiety Test (SCAT-15) and the Pain Beliefs and Perceptions Inventory (PBPI-16), respectively; handgrip strength, assessed using the Hand Grip Test; and fitness-related biomarkers, including maximal oxygen uptake (VO₂max) and blood lactate concentration. Conclusions: These findings may provide evidence supporting BB as a complementary, non-invasive intervention to enhance psychological and physiological rehabilitation in injured athletes. Ethics and Dissemination: The study has received ethical approval and is registered on ClinicalTrials.gov. Findings will be disseminated through peer-reviewed publications, conferences, and academic presentations. Full article

The contemporary educational system places complex demands on educators, emphasizing the importance of pedagogical-didactical competencies and teacher self-efficacy alongside subject-matter expertise. The purpose of this study was to examine how future STEM educators assess their own perceived pedagogical-didactical capabilities for direct teaching and classroom management through the construct of teacher self-efficacy, examining variations by gender, student cohort, and academic major. The research was conducted in autumn 2025 using a near-census sample of graduate teaching track students at the Faculty of Science in Split, utilizing the Norwegian Teacher Self-Efficacy Scale. The descriptive and non-parametric results indicate that future teachers express higher baseline scores in the domains of instruction and coping with changes, whereas the lowest self-assessment was recorded regarding the adaptation of teaching to individual student needs. While gender and student cohort demonstrated no statistically significant impact on overall self-efficacy, a significant difference was determined in the domain of maintaining discipline with respect to the study major, with Informatics and Technology students exhibiting the greatest sense of confidence. In conclusion, the findings highlight that while students are well-prepared for content delivery, there remains a critical need to enhance initial teacher education programs by introducing practical tools for managing challenging student behaviors and working within heterogeneous classrooms. Full article

In China’s expressway work zones, it is a common phenomenon for drivers to have a low compliance rate with speed-limit instructions. Existing microscopic traffic simulation calibrations mainly focus on car-following and lane-changing behaviors, lacking research on speed-limit compliance behavior. Therefore, this paper proposes a method for collaborative calibration of the key parameters of a “car-following, lane-changing, speed-limit compliance” three-layer model based on the SUMO simulation platform. The research selects the key parameters in the IDM car-following model, LC2013 lane-changing model, and speed-limit compliance model to form a calibration parameter set, taking the time-mean speed and space-mean speed as optimization indicators, using the simultaneous perturbation stochastic approximation (SPSA) algorithm combined with a restart strategy, and aiming to minimize the root-mean-square error (RMSE) of the speed between the simulated and observed data for global optimization. The model is verified by the measured traffic flow and speed data in the expressway work zone. The verification results show that the three-layer calibration framework incorporating the speed-limit compliance model not only improves speed fitting but also better reproduces the distributional characteristics of real traffic flow in the work zone, especially the dispersion and heterogeneity of operating speeds. This research fills a gap in research involving SUMO calibration of speed-limit compliance in China and provides a theoretical basis and method-based support for microscopic simulation considering driver differences in speed-limit compliance. Full article

Generative artificial intelligence (AI) is increasingly entering teacher education, yet evidence remains limited on its responsible integration into discipline-specific pedagogical preparation. This study examined whether an AI-supported Collaborative TPACK Competency Development module based on problem-based learning (CTD-PBL) was associated with greater pre–post gains in pre-service physics teachers’ self-reported technological pedagogical content knowledge (TPACK) and perceived collaborative problem-solving (CPS) processes. Informed by ADDIE, the 8-week module used DeepSeek as a bounded scaffold for collaborative lesson design, feedback, verification, and reflective revision while preserving teacher judgment. An intact-class quasi-experimental pre-test/post-test design involved 130 third-year pre-service physics teachers at a public university in western China. Two existing classes were randomly allocated at the class level to CTD-PBL or conventional instruction. Compared with the conventional group, the CTD-PBL group reported higher post-test TPACK scores (M = 4.04 vs. M = 3.40, p < 0.001, d = 1.02) and higher perceived CPS process scores (M = 3.62 vs. M = 3.05, p < 0.001, d = 0.88), with stronger pre–post gains in both outcomes. The findings provide a bounded curriculum design case showing how generative AI can be embedded in physics teacher education through problem-based tasks, collaborative scaffolding, and human verification procedures. Full article