Search Results (308)

In China’s increasingly stratified education system, a growing number of high-scoring junior secondary students are choosing the Vocational Secondary–Undergraduate Articulation Program over key academic senior high schools, challenging conventional assumptions about merit, school choice, and vocational education. Existing studies have mainly examined this pathway from institutional and policy perspectives, with limited attention to the micro-level decision-making logic of students and families. Drawing on rational choice theory and a theoretical thematic analysis of online public discourse, this study explores how high-scoring students and their families interpret, evaluate, and justify this educational choice. The findings show that participation in the articulation program is organized around four interrelated mechanisms: action foundations based on academic strengths and family evaluation; action purposes aimed at securing a relatively stable route to a bachelor’s degree while gaining vocational advantages; action consequences involving the weighing of risks and expected returns; and institutional and cultural impacts produced by the interaction between policy incentives and persistent academic hierarchies. The study argues that choosing this pathway is not a deviant decision, but a rational response to educational competition and structural constraints, and it sheds light on the changing legitimacy of vocational pathways in contemporary China. Full article

This study explored individual differences among senior high school students in detecting and correcting logical errors in mathematical reasoning. Eight participants with high and average mathematical abilities each were recruited from a key high school in Shanghai to solve three error-detecting tasks by thinking aloud; they were then interviewed. Results showed that high ability students performed better in answering time, validation judgment, detection, explanation, and correction of logical errors. The cognitive processes for detecting and correcting logical errors were a combination of five types of cognitive behaviors—read, analyze, check, judge and correct. Although their specific combination methods were different, the two groups exhibited two different detecting styles. Error detection of high ability students was more active and effective, and their thinking processes were smooth and concise. The average ability group was more passive in error detection, more dependent on mathematical texts, and more stuck in the thinking process. Both groups agreed on the value of logical error-detecting tasks, although the high ability group had a more positive attitude toward them. Full article

Adolescent vaping has become a persistent health and behavioural challenge in schools, yet many institutions lack reliable tools to detect and respond to concealed e-cigarette use. This study addresses this problem by evaluating the real-world performance of a low-cost “Internet of Things” (IoT) vape detection system deployed across 37 high-risk restroom and change-room locations at a large Australian Independent school. The aim was to determine whether an IoT-based environmental monitoring platform could accurately identify vaping events, support timely staff intervention, and provide actionable insights into student behaviour patterns. A longitudinal case study design was used, collecting continuous particulate matter (PM_2.5 and PM₁₀) data at one-minute intervals over an 18-month period, where PM_2.5 and PM₁₀ refer to particulate matter with aerodynamic diameters ≤ 2.5 µm and ≤10 µm, respectively, reported in micrograms per cubic metre (µg/m³. Threshold-based alerting, cloud-based data processing, and school-led Closed-circuit television (CCTV) verification were combined to assess detection accuracy, temporal trends, and operational responses. The system recorded more than 300 vaping-related incidents, with clusters aligned to predictable times of day and higher prevalence among senior students. Operational detection performance was high, with alert events characterised by rapid, concurrent PM_2.5 and PM₁₀ excursions consistent with vaping-related aerosol profiles, although staff responsiveness declined over time due to alert fatigue and competing priorities. A major environmental smoke event demonstrated the need for context-aware logic to reduce false positives. The findings demonstrate that real-time aerosol monitoring is not only technically reliable but also highly effective in detecting vaping within school environments. These perspectives help explain why user engagement, alert fatigue, and institutional follow-through are as critical as sensor accuracy itself. Ultimately, the effectiveness of vape detection relies on strong organisational commitment, well-defined response workflows, and alignment with broader wellbeing and policy strategies. When these elements are in place, such systems can evolve from simple detection tools into intelligent, integrated components of school health governance. Full article

With the rapid development in data mining and learning analytics, integrating predictive analytics into educational data has become increasingly critical for supporting students’ learning trajectories. In many schooling systems, the academic tracks (such as Liberal Arts or Science) and the performance of junior high school students can substantially shape their subsequent university pathways and career planning. Despite the long-term impact of these decisions, academic track selections and the evaluation of students’ potential are often made without systematic and evidence-based guidance. Predictive computer applications can assist, but the training of accurate models and the selection of adequate features remain key challenges. This paper details our process of engineering such an application comprising two tasks based on 1357 real-world junior high school academic performance records. The first task applies a classification approach to predict students’ academic track orientation, while the second task employs a multi-output regression model to forecast students’ future academic performance in senior high school. Our approach shows that the stacking ensemble model achieved a classification accuracy of 85.76%, whereas the Bi-LSTM model with multi-head attention attained an overall R² exceeding 82% in performance forecasting; both models demonstrated strong and reliable predictive capability. Moreover, the proposed approach provides inherent interpretability by decomposing predictions at the subject level. Feature importance analysis reveals how different academic subjects contribute variably to both academic track decisions and future academic performance, offering actionable insights for academic counselling and future study planning. By bridging predictive modelling with students’ educational and career planning needs, this study advances the practical application of educational data mining and provides support for evidence-based academic guidance and future career choices in real-world contexts. Full article

Background and Purpose: Neurology requires integration of clinical reasoning, imaging interpretation, and current knowledge, making it an ideal field for evaluating large language models (LLMs). Methods: Using 1715 questions from the Taiwan Neurology Board Examination (2018–2024), we assessed four LLMs—GPT-4o, GPT-o1, DeepSeek-V3, and DeepSeek-R1—across four formats: single-choice, multiple-choice, true–false, and image-based items. Results: GPT-o1 achieved the highest overall accuracy (83.86%) and demonstrated strong performance on cognitively demanding tasks (82.50% on true–false; 77.26% on image-based). DeepSeek-V3 scored lowest (65.62%) and showed the greatest variability. Statistical analyses confirmed significant inter-model differences (p < 0.01). Accuracy declined across all models in 2024, coinciding with shifts in question design. DeepSeek-R1 was further penalized by alignment-based refusals, resulting in up to 3.81% score loss. Conclusions: These results position the Taiwan Neurology Board Exam as a rigorous benchmark for LLM evaluation and underscore GPT-o1’s potential utility in neurology education and decision support. Full article

The challenge of designing generative AI (GenAI) tutors that are both pedagogically sound and effective for guided inquiry remains significant. This paper introduces and evaluates a replicable design framework-termed a Pedagogy-to-Prompt Engineering Framework-that systematically translates established pedagogical models into structured AI interactions. We engineered a 5E-structured GenAI coach by integrating the 5E Learning Cycle as the instructional architecture and the 5S Prompting Principles to govern the AI’s dialogue. The coach was evaluated in a middle school chemistry context (N = 60) focusing on procedural skill acquisition for balancing chemical equations. A quasi-experimental study showed the GenAI group achieved significantly higher learning gains than a control group receiving traditional instruction (t(58) = 2.646, p = 0.011, Cohen’s d = 0.68). Crucially, a Johnson-Neyman analysis revealed that the coach was particularly beneficial for students with lower prior knowledge (pre-test scores < 39.39), effectively narrowing the achievement gap. Furthermore, Lag Sequential Analysis of the interaction logs confirmed that the student-AI dialogue successfully adhered to the intended 5E pedagogical sequence (e.g., Engage → Explore transition, z = 11.157). This study demonstrates that the proposed framework is a viable method for creating effective, scalable AI-driven learning environments. Beyond chemistry, this approach is readily adaptable to other STEM disciplines requiring guided inquiry, such as physics and mathematics. By validating a low-code, pedagogy-first methodology, this work offers a scalable blueprint for instructional designers to bridge the gap between generative AI capabilities and rigorous educational standards. Full article

The negative impacts caused by synthetic herbicides have necessitated research on environment-friendly and sustainable alternatives. In this study, a novel botanical nanoherbicide was developed through green synthesis of silver nanoparticles (Ag NPs) assisted by aqueous extract of Brucea javanica (BJ) residue. The BJ-Ag NPs were characterized using ultraviolet–visible (UV–Vis) absorption spectroscopy, dynamic light scattering (DLS), zeta potential analysis, X-ray diffraction (XRD), and transmission electron microscopy (TEM) attached with energy dispersive X-ray spectroscopy (EDX). TEM images indicated that the BJ-Ag NPs were spherical with an average particle size of 12.75 nm. Meanwhile, the herbicidal activity against two paddy weeds (Echinochloa crusgalli and Bidens pilosa L.) and phytotoxicity to rice (Oryza sativa L.) were evaluated using the Petri dish method. Compared to the BJ residue extract, the BJ-Ag NPs exhibited enhanced inhibitory activity on the seed germination and seedling growth of two target weeds, while showing alleviated phytotoxicity and partially restored seedling vigor in rice. Obviously, positive impacts on both the weed and crop were obtained after synthesizing Ag NPs using the BJ residue extract. The results in this study demonstrated the potential of the BJ-Ag NPs as a sustainable, crop-friendly nanoherbicide for weed management in paddy fields. Full article

South China’s freshwater biodiversity has been shaped by Quaternary climatic oscillations and persistent geological barriers. We investigated the phylogeography and conservation implications of the primary freshwater fish Microphysogobio kachekensis across mainland China and Hainan Island using mitochondrial (cyt b and control region) and nuclear (RAG2 and rpS7-1) markers from 200 individuals. Mitochondrial analyses recovered two major lineages and multiple sublineages largely structured by drainage basins, whereas nuclear data resolved four geographically concordant lineages. Population differentiation was strong (high F_ST), and SAMOVA/AMOVA supported major barriers restricting gene flow, including the Qiongzhou Strait, Gulf of Tonkin, Yunkai Mountains, and Nanling Mountains. Ancestral-area reconstruction inferred the Pearl River region as the most likely source area, followed by dispersal to northern Hainan and subsequent expansion to southern Hainan and the Red River, with additional northward expansion to the Zhejiang–Fujian region. Despite high haplotype diversity, within-population nucleotide diversity was low, consistent with long-term river isolation and complex demographic history. We propose six ESUs and four MUs for evolutionarily informed conservation and to guide stock enhancement in southern China. Full article

As part of our technology-enhanced learning (TEL) strategy, we developed a field-based augmented reality (AR) learning system for biodiversity education among senior elementary school students. Using a 2D illustration style to present the appearance of the species and a situational interactive design, the AR app focused on common wild animals in Taiwan. They also gained insight into wild animal species in outdoor settings, gained knowledge about the phenomenon of roadkill and the rescue of wild animals, and promoted their awareness of ecological conservation. Using the design-based research (DBR) method, we integrated user-oriented design processes and iteratively modified the system functions and interface through expert review and field usability testing. During this activity, 26 senior elementary school students were recruited to participate in an interactive AR game designed for a single player. As part of the learning content, students must collect images of species, recognize roadkill, and learn about wildlife rescue. To evaluate the effect of the activity on knowledge learning and the app’s usability, data were collected through pre- and post-test paper tests, questionnaires, and so on. Based on the research results, this system can significantly enhance students’ learning interests and contextual understanding of biodiversity topics as an effective technology-assisted learning tool. Students reported high levels of immersion and learning motivation, and the teachers agreed that it promoted inquiry-based and independent learning. The results of this study contribute to the field of educational and environmental education. Consequently, context-aware AR tools may enhance students’ situational learning experience and environmental literacy. In addition, it provides a practical design reference for future AR educational applications, demonstrating that gamification and outdoor learning can enhance the learning outcomes of traditional science education. Full article

Background/Objectives: Teacher burnout represents a complex cognitive-emotional syndrome characterized by the interplay between mental exhaustion and emotional dysregulation, threatening educational sustainability during crisis periods. This study employed comprehensive behavioral data analysis to investigate burnout syndrome patterns among Greek teachers during the COVID-19 educational crisis, aiming to identify risk factors and resilience patterns through multiple analytical approaches that capture the syndrome’s multidimensional nature. Methods: A cross-sectional study examined primary and secondary school teachers in Western Greece during the autumn of 2021. Stratified random sampling ensured representativeness across school levels, geographic locations, and employment types. Participants completed the Greek-adapted Maslach Burnout Inventory for Educators, which measured emotional exhaustion, depersonalization, and personal accomplishment. Behavioral data analysis integrated traditional statistical methods with advanced pattern recognition techniques, including classification trees for non-linear relationships, association analysis for behavioral patterns, and cluster analysis for profile identification. Results: The majority of teachers experienced high stress with inadequate coping capabilities. Classification analysis achieved high accuracy in predicting burnout severity, identifying emotional exhaustion as the primary predictor. Deputy teachers demonstrated severe cognitive-emotional strain compared to permanent colleagues across all dimensions, with dramatically reduced personal accomplishment and minimal resources. Association analysis revealed that combined low support and high workload more than doubled burnout risk. Three distinct profiles emerged: Resilient teachers, characterized by older age and permanent employment; At-Risk teachers, showing early warning signs; and Burned Out teachers, predominantly young and in precarious employment. Remote teaching, exceeding half of the workload, significantly increased strain. Multiple regression confirmed emotional exhaustion as the dominant syndrome predictor. Conclusions: Behavioral data analysis revealed complex cognitive-emotional patterns constituting burnout syndrome during educational crisis. Employment precarity emerged as the fundamental vulnerability factor, with young deputy teachers facing dramatically higher syndrome probability compared to supported senior permanent teachers. The syndrome manifests through cascading processes where cognitive overload triggers emotional exhaustion, subsequently reducing personal accomplishment. These findings provide an evidence-based framework for early syndrome identification and targeted interventions addressing both cognitive and emotional dimensions of teacher burnout. Full article

Although previous studies have established a link between perceived discrimination and negative adolescent outcomes, potential mediating and moderating factors—specifically, the mediating role of self-esteem and the distinct moderating roles of positive and negative coping styles—remain underexplored. This longitudinal study aimed to examine whether adolescents’ perceptions of discrimination directed toward themselves or their classmates predict their prosocial behaviors through the mediating role of self-esteem and whether positive and negative coping styles moderate this pathway. A total of 531 junior high school students (M_age = 15.73, SD_age = 0.67, 47.83% males) from Changchun, Jilin Province, China, completed measures of perceived discrimination, self-esteem, prosocial behaviors, and coping styles across three time points. Higher levels of perceived discrimination at T1 were associated with fewer prosocial behaviors at T3, and this relationship was mediated by reduced self-esteem at T2. Moreover, both positive and negative coping styles at T1 served as moderators. Positive coping moderated the negative effects of perceived discrimination on both self-esteem and prosocial behaviors, while negative coping moderated the positive association between self-esteem and prosocial behaviors. These findings underscore the distinct role of perceived discrimination, self-esteem, and coping styles in shaping adolescent prosocial development and offer valuable implications for educational interventions aimed at fostering prosociality. Full article

(1) Background: The purpose of this study was to investigate the associations between perceived stress and anxiety in high school senior students, taking into account the possible influence from lifestyle (physical activity, nicotine dependence, and alcohol use) and social support. (2) Methods: A representative sample of high school senior students (N = 405; mean age: 18.2 ± 0.4), reflecting the overall geographic distribution of Lithuania’s student population, was investigated using anonymous questionnaires on perceived stress (Perceived Stress Scale, PSS-10), anxiety (Generalized Anxiety Disorder Scale, GAD-7), perceived social support (Multidimensional Scale of Perceived Social Support, MSPSS), lifestyle (Fagerström Test for Nicotine Dependence), alcohol use (Alcohol Use Disorders Identification Test, AUDIT), and physical activity (International Physical Activity Questionnaire, IPAQ). Hierarchical multiple regression analysis was employed, investigating mediating and moderating effects. (3) Results: The direct effect of perceived stress on anxiety was significant (B = 0.364; SE = 0.0486; 95% CI [0.268, 0.459]; p < 0.001). Furthermore, the analysis revealed a significant indirect effect via social support (B = 0.387; SE = 0.0525; 95% CI [0.284, 0.490]), indicating that a portion of the relationship between perceived stress and anxiety was mediated by social support. Physical inactivity, alcohol use, and nicotine dependence served as moderators. Our findings confirmed that all three moderators showed significant interaction effects, with standardized β = −0.124, p = 0.002, for physical inactivity, β = 0.073, p = 0.016, for alcohol dependence, and β = 0.119, p = 0.001, for nicotine dependence, in the relationship between perceived stress and anxiety among high school senior students. (4) Conclusions: These findings have practical insights for educators who implement physical activity and alcohol and nicotine usage programs for high school senior students to help reduce their stress and anxiety. Full article

This research sought to examine differences in the cross-modal facilitation effect of voice on facial identity recognition under mask occlusion for both oneself and others. Employing a facial recognition paradigm, we examined the influence of voice on facial identity recognition under static and dynamic mask occlusion through two eye-tracking experiments. The behavioral results from Experiments 1 and 2 indicate that mask occlusion interfered with recognition for both static and dynamic faces, with greater interference observed for others’ faces than for self-faces. In addition, voice exerts cross-modal enhancement effects on faces, with greater enhancement observed for masked faces than for no mask. Furthermore, voice provides stronger enhancement for others’ dynamic faces than for their self-dynamic faces. Eye-tracking data from both experiments revealed that the difference in dynamic facial recognition between self-faces and others’ faces due to voice emerged in the early stages of dynamic facial recognition and persisted into later stages. However, regardless of whether they were in the early or late stages of static facial recognition, the facilitation effect of voice did not differ between themselves and others. This study revealed that the cross-modal facilitation of visual stimuli by voice is influenced by the self-advantage effect. Full article

Parental educational anxiety poses a significant risk to children’s academic development. This two-stage study first establishes Parental Mathematics Education Anxiety (PMEA) as a unique construct and then examines the complex effects of a positive suggestion intervention. Study 1, a questionnaire-based investigation, revealed that PMEA is a significant and independent negative predictor of children’s mathematics achievement, distinct from parents’ general state anxiety or their own mathematics anxiety. It also identified socioeconomic factors, such as family income and parental education, as key drivers of PMEA. Study 2 employed an experimental design to test an intervention, revealing that the effectiveness of positive suggestions is not universal but is significantly moderated by the three-way interaction of PMEA level, child type (with/without math learning difficulties), and suggestion frequency. Notably, for non-math-difficult children, frequent positive suggestions from high-anxiety parents were found to be potentially detrimental (a “backfire effect”), whereas for math-difficult children in high-anxiety homes, a higher frequency of suggestion was necessary to yield benefits. These findings deepen the understanding of PMEA’s mechanisms and underscore the necessity of moving beyond one-size-fits-all approaches toward differentiated, context-aware intervention strategies in family education. Full article

As quantum technologies advance, there is growing international interest in integrating quantum concepts into secondary education. However, most K-12 quantum education studies rely on self-reported data or informal assessments lacking documented validity. This study aimed to address this gap by developing and validating the Quantum Literacy Test (QLt), a standardized instrument designed to objectively assess upper-secondary students’ understanding of foundational quantum concepts, practices, and perspectives. Grounded in the computational thinking (CT) framework, the QLt was piloted with 819 senior secondary school students in Nigeria and underwent a multi-phase validation process, including expert review, factor analysis, item-response modeling, differential item functioning analysis, and concurrent validity. The QLt demonstrated high internal consistency (α = 0.87) and structural validity. Strong concurrent validity was observed with the Computational Thinking Test (r = 0.65), and moderate validity was observed with a Spatial Ability Test (r = 0.32). However, machine learning models explained less than 40% of QLt score variance, suggesting the domain-specific nature of quantum literacy. We recommend future research to expand the QLt across diverse cultural contexts and to increase item coverage of quantum practices and perspectives. The QLt offers a valuable tool for evaluating curriculum effectiveness and monitoring equity in quantum education, thereby contributing to a more inclusive quantum-ready workforce. Full article