Search Results (90)

This study investigates the evolving pedagogical strategies and professional identity development of two novice college English teachers in China through a semester-long classroom-based inquiry. Drawing on Norris’s Multimodal (Inter)action Analysis (MIA), it analyzes 270 min of video-recorded lessons across three instructional stages, supported by visual transcripts and pitch-intensity spectrograms. The analysis reveals each teacher’s transformation from textbook-reliant instruction to student-centered pedagogy, facilitated by multimodal strategies such as gaze, vocal pitch, gesture, and head movement. These shifts unfold across the following three evolving identity configurations: compliance, experimentation, and dialogic enactment. Rather than following a linear path, identity development is shown as a negotiated process shaped by institutional demands and classroom interactional realities. By foregrounding the multimodal enactment of self in a non-Western educational context, this study offers insights into how novice EFL teachers navigate tensions between traditional discourse norms and reform-driven pedagogical expectations, contributing to broader understandings of identity formation in global higher education. Full article

The shortage of skilled professionals in technical fields is further compounded by a lack of qualified teachers in STEM subjects, particularly in craft education, which is vital for developing technical competencies at the elementary level. The present study investigates the professionalisation of teachers in craft education and explores the prevalence and reasons for out-of-field teaching across three German federal states. The data presented herein were collected through an online survey administered in 2023 among teaching professionals in Mecklenburg-Vorpommern, Sachsen, and Thüringen. The questionnaire was disseminated via head teachers to 1467 elementary schools, yielding a self-selection sample of 284 craft education teachers. The survey incorporated both closed- and open-ended questions, encompassing inquiries into teacher qualifications, subject-specific competence, and lesson planning. Quantitative data were analysed descriptively. The evaluation of open-ended responses employed a content-structuring content analysis approach, utilising categories that were inductively developed. The findings indicate that a considerable proportion of craft education is taught by educators who lack formal qualifications, thereby giving rise to concerns regarding the quality of instruction. The underlying factors contributing to this phenomenon include teacher shortages, personal interests, prior experience, and limited professional development opportunities. The findings emphasise the pressing necessity for enhanced teacher education and targeted training programmes to bolster instructional quality in technically oriented subjects. Full article

Few-shot object detection (FSOD) in remote sensing remains challenging due to the scarcity of annotated samples and the complex background environments in aerial images. Existing methods often struggle to capture fine-grained local features or suffer from bias during global adaptation to novel categories, leading to misclassification as background. To address these issues, we propose a framework that simultaneously enhances local feature learning and global feature adaptation. Specifically, we design an Extensible Local Feature Aggregator Module (ELFAM) that reconstructs object structures via multi-scale recursive attention aggregation. We further introduce a Self-Guided Novel Adaptation (SGNA) module that employs a teacher-student collaborative strategy to generate high-quality pseudo-labels, thereby refining the semantic feature distribution of novel categories. In addition, a Teacher-Guided Dual-Branch Head (TG-DH) is developed to supervise both classification and regression using pseudo-labels generated by the teacher model to further stabilize and enhance the semantic features of novel classes. Extensive experiments on DlOR and iSAlD datasets demonstrate that our method achieves superior performance compared to existing state-of-the-art FSOD approaches and simultaneously validate the effectiveness of all proposed components. Full article

Background/Objectives: Traditional nutrition education can increase children’s exposure to healthy foods, but preschool teachers face barriers such as limited time and competing priorities (e.g., kindergarten readiness). Integrating nutrition into other learning domains (e.g., science) has been identified as a potential solution. However, teachers need more professional development. We developed the More PEAS Please! program to support preschool teachers’ integration of food-based learning (FBL) and science, seeking to improve children’s science learning, language development, and dietary quality. Methods: In this pilot study, we used a mixed-methods process evaluation to assess the program in five Head Start centers (n = 23 classrooms) across three rural North Carolina counties. We collected teacher data via surveys and interviews. Results: A total of 24 teachers participated in the full intervention by attending a one-day workshop, completing at least one of four core learning modules, and implementing 16 food-based science learning activities in their classrooms. Teachers were Black/African American (81.1%) and 43.56 (11.89) years old. Teachers reported varying engagement levels and high satisfaction with the program, sharing increased confidence in FBL and science integration. However, barriers such as time, technology, and the coronavirus disease (COVID-19) limited full participation. Conclusions: Our findings suggest that the program is feasible and well received in Head Start settings and has promising impacts on classroom teaching practices. The findings will guide revisions to the PEAS program. Future research evaluating the revised program using a comparison group will be explored. Full article

Aiming at the problems of insufficient vehicle detection accuracy, high misdetection and omission rate, and heavy model computational burden caused by complex lighting conditions, target occlusion, and other factors in urban traffic scenarios, this paper proposes an improved lightweight detection network, ZZ-YOLO. Firstly, the current mainstream target detection algorithms lack components to improve the network’s focus on the edges of the objects, which can indirectly lead to unclear classification and localization. For this reason, in this paper, we self-develop a module of GlobalEdgeInformationTransfer (GEIT), which can help us to transfer the edge information extracted from the shallow features to the whole network and fuse it with the features of different scales. Secondly, to reduce the number of parameters in the detection head and to fuse the extracted features better, a self-developed Lightweight Detail Convolutional Detection Head (LDCD) detection head is introduced. After that, the most effective layer-adaptive magnitude-based pruning (LAMP) method is used to build away the redundant parameters to make the detection network more lightweight. Finally, in order to ensure that the detection accuracy of the pruned model will not be too low, a model distillation method was used, in which YOLOv11x + LDCD was used as the teacher model and the pruned model was distilled as the student model. Experimental data on the optimized KITTI and BDD100K datasets show that the detection accuracy of the ZZ-YOLO algorithm is 70.9%, the mAP (mean Average Precision) @0.5 is 58%, the model-parameter quantity is 14.1GFLOPs compared to the original algorithm, the detection accuracy is increased by 5.7%, and the average precision is increased by 2.3%. The amount of model parameters is reduced by 34%, and the real-vehicle verification session effectively reduces the misdetection and omission of vehicles. Full article

Unsupervised domain adaptation (UDA) effectively transfers knowledge learned from a labeled source domain to an unlabeled target domain. The teacher–student framework, which generates pseudo-labels for target domain samples and uses them for pseudo-supervised training, enables self-training and improves generalization in UDA object detection. However, for one-stage detection models, pseudo-labels are unreliable when positive and negative samples are imbalanced. This may lead the model to overfit the source domain and overlook important target-domain information. In this work, we propose a novel domain-specific student–teacher framework to address this issue. The innovations of the proposed framework can be summarized in two aspects. First, we employ two domain-specific heads (DSHs) in the student model to handle inputs from the source domain and the target domain separately. These two heads are optimized independently with samples from their respective domains. This design allows for reducing the impact of unreliable pseudo-labels and fully leveraging unique information specific to the target domain. Second, we introduce an auxiliary reconstruction branch, named the multi-scale mask adversarial alignment (MMAA) module, into the teacher–student framework. The MMAA is tasked with reconstructing randomly masked multi-scale features of the source domain, which enhances the student model’s semantic representation capability and facilitates the generation of high-quality pseudo-labels. Experimental results on six diverse cross-domain scenarios demonstrate the effectiveness of our framework. Full article

Reliable quality and size inspection of shrimp meat is essential in food processing to ensure food safety, enhance production efficiency, and promote sustainable practices. However, significant scale differences in shrimp meat categories and the presence of subtle local defects pose challenges to traditional manual inspection methods, resulting in low efficiency and high rates of false positives and negatives. To address these challenges, we propose a lightweight multi-scale object detection framework specifically designed for automated shrimp meat inspection in food processing environments. Our framework incorporated a novel downsampling module (ADown) that was engineered to reduce parameters while preserving essential features. Additionally, we propose dual-scale information selection convolution (DSISConv), multi-scale information selection convolution (MSISConv), and a lightweight multi-scale information selection detection head (LMSISD) to improve detection accuracy across diverse object scales. Furthermore, a bidirectional complementary knowledge distillation strategy was employed, which enabled the lightweight model to learn crucial features from a larger teacher model without increasing inference complexity. Experimental results validated the effectiveness of our approach. Compared to the YOLOv11n (baseline) model, the proposed framework improved precision by 1.0%, recall by 0.8%, mAP50 by 0.9%, and mAP50-95 by 1.3%, while simultaneously reducing parameters by 7.1%, model size by 8.0%, and GFLOPs by 22.2%. The application of knowledge distillation yielded further improvements of 0.1% in precision, 1.2% in recall, 0.5% in mAP50, and 0.5% in mAP50-95. These results indicated that the proposed approach provided an effective and efficient solution for real-time shrimp meat inspection, balancing high accuracy with low computational requirements. Full article

Background/Objectives: Early childhood represents a sensitive period for developing positive dietary preferences and important school readiness skills. However, few evidence-based programs leverage opportunities to support children’s development in both areas. Our study aimed to assess the preliminary effects of multi-level, teacher-led More PEAS Please! on Head Start children’s (3–5 years old) science knowledge, development of academic language, fruit-and-vegetable (FV) liking, and dietary quality. Methods: In this pilot study, we used a repeated-measure research design to assess child-level outcomes. Trained teachers implemented 16 food-based science-learning activities. We assessed child outcomes using validated measures of science knowledge, academic language, FV liking, and dietary quality (Veggie Meter^®). We used linear mixed models to examine changes from the baseline to post intervention. Fixed effects included age, sex, and race/ethnicity, while the center was treated as a random effect. Results: A total of 273 children were enrolled in the study. The children were mostly male (51.6%), Black/African American (82.1%) and, on average, 3.94 (SD = 0.70) years old. The children demonstrated significant improvements in science knowledge (T1 M = −0.01, SD = 0.82; T4 M = 0.33, SD = 0.90; 95% CI [0.17, 0.50]; p < 0.001) and vocabulary (T1 M = 14.4, SD = 4.5; T4 M = 16.7, SD = 5.3; 95% CI [1.4, 3.3]; p < 0.001). The children’s dietary quality improved from the baseline, but the changes were not significant. Conclusions: The findings suggest that the intervention may support improvements in science knowledge and academic vocabulary among preschool-aged children. We theorize a longer intervention with additional FV exposures may be needed to observe significant dietary changes. Future research should evaluate program effects with a comparison group. Full article

Introduction: This study aimed to develop a service model for daycare centers for the elderly through community participation using participatory action research methods. The objectives were threefold: (1) to investigate the current situation of the elderly in the community and their needs for daycare center services, (2) to develop a daycare center for the elderly with active community involvement, and (3) to evaluate the effectiveness of the service delivery at the daycare center for the elderly. Methods: The study was conducted in Ban Kho Subdistrict, Phon Sawan District, Nakhon Phanom Province. Research participants included 210 elderly individuals surveyed to assess their situation, and 15 key informants, including elderly club leaders, subdistrict health promotion hospital staff, volunteers, subdistrict administrative organization officers, and village health volunteers, were specifically selected for in-depth insights. The research process was structured into three phases: Phase 1 focused on studying the situation of the elderly in the community and their service needs; Phase 2 was dedicated to developing the daycare center with community participation; and Phase 3 involved evaluating the service delivery of the daycare center. Results: The results indicated that the development process of the daycare center service model for the elderly, through community participation, involved four key mechanisms: elderly clubs, subdistrict health promotion hospitals, volunteer teachers or technicians, and village volunteers. Additionally, the supporting mechanisms included academic institutions, hospitals, temples, village heads, the Non-Formal Education Center, foundations, and the subdistrict administrative organization. The comprehensive service model encompassed five components: health, social, psychological, economic, and environmental aspects. Conclusions: The study successfully developed a daycare center service model for the elderly through community participation, which can be expanded and adapted to other semi-urban and semi-rural contexts. This model demonstrates the importance of community involvement in providing holistic care for the elderly, addressing various aspects of their well-being. Full article

The integration of sustainability in education has gained global attention as a critical component of achieving the United Nations Sustainable Development Goals (SDGs). Within the United Arab Emirates (UAE), significant efforts have been made to incorporate sustainability into national policies, reflecting the country’s vision for sustainable economic, social and environmental development. Within the context of Education for Sustainable Development (ESD), this research aims to investigate the alignment between national sustainability policies and their practical implementation in secondary schools, with a focus on identifying barriers and proposing actionable recommendations to enhance the integration of sustainability into education. This study employs a qualitative case study design with content analysis of data gathered from interviews and focus groups collected from a total of 21 teachers, school leaders, heads of departments and government officials, alongside the review of 14 relevant key policy documents. Key findings include a gap between policy and implementation, lack of a unified framework, resource disparities, and several barriers and strengths. This research concludes with recommendations to address these challenges, so that the UAE can strengthen its position as a leader in sustainability education, further aligning its national vision with global SDGs. Full article

Medical image segmentation is a challenging task due to limited annotated data, complex lesion boundaries, and the inherent variability in medical images. These challenges make accurate and robust segmentation crucial for clinical applications. In this study, we propose the Uncertainty-Driven Auxiliary Mean Teacher (UDAMT) model, a novel semi-supervised framework specifically designed for skin lesion segmentation. Our approach employs a dual-headed teacher–student architecture with an uncertainty-guided mechanism, enhancing feature learning and boundary precision. Extensive experiments on the ISIC 2016, ISIC 2017, and ISIC 2018 datasets demonstrate that UDAMT achieves significant improvements over state-of-the-art methods, with increases of 1.17 percentage points in the Dice coefficient and 1.31 percentage points in mean Intersection over Union (mIoU) under low-label settings (5% labeled data). Furthermore, UDAMT requires 12.9 M parameters, which is slightly higher than the baseline model (12.5 M) but significantly lower than MT (14.8 M) and UAMT (15.2 M). It also achieves an inference time of 25.7 ms per image, ensuring computational efficiency. Ablation studies validate the contributions of each component, and cross-dataset evaluations on the PH2 benchmark confirm robustness to small lesions. This work provides a scalable and efficient solution for semi-supervised medical image segmentation, balancing accuracy, efficiency, and clinical applicability. Full article

Deep learning-based object detection technology is rapidly developing, and underwater object detection, an important subcategory, plays a crucial role in various fields such as underwater structure repair and maintenance, as well as marine scientific research. Some of the major challenges in underwater object detection are the relatively limited availability of underwater image and video datasets and the high cost of acquiring high-quality, diverse training data. To address this, we propose a novel underwater object detection method, SUD-YOLO, based on the Mean Teacher semi-supervised learning strategy. More specifically, it combines a small number of labeled samples with a large number of unlabeled samples, using the teacher model to guide the generation of pseudo-labels. In addition, a multi-scale pseudo-label enhancement module is developed specifically to address the issue of low-quality pseudo-labels. To overcome the model’s difficulty in learning underwater image feature extraction, we integrate a receptive-field attention mechanism with local spatial features and then design a lightweight detection head based on the task alignment concept to further improve the model’s feature extraction capability. Experimental results on the DUO dataset show that, by using only 10% of the labeled data, the proposed method achieves an average precision of 50.8, which is an improvement of 11.0% over the fully supervised YOLOv8 algorithm, 11.3% over the fully supervised YOLOv11 algorithm, 9.3% over the semi-supervised Efficient Teacher algorithm, and 3.4% on the semi-supervised Unbiased Teacher algorithm, while only 20% of the computational cost is required. Full article

This qualitative study explores forces influencing the practices of Departmental Heads (DHs) in enacting their roles in implementing and managing Technical Mathematics (TMAT) curriculum. TMAT was piloted in a few South African schools in 2016 and later scaled to others. Since its inception, learner performance has been uneven, raising questions about the processes of managing and implementing the curriculum. We use Samuel’s Force Field Model to understand forces influencing DH practices in their quest to implement and manage the curriculum. Data were generated using one-on-one interviews and document analysis and thematically analysed using NVivo. The findings reveal that contextual and external forces are the main factors that influence DH practices when it comes to the implementation and management of the curriculum. These forces influence practices such that the roles and responsibilities are carried out mainly for compliance purposes. While in theory, DHs seem to believe in collaboration, they prefer working in silos and perceive that the success of the TMAT curriculum implementation should be at the hands of seasoned mathematics teachers. In addition, they seem to consider curriculum implementation and management to be solely about ensuring curriculum coverage. We argue that to ensure the sustainability and effectiveness of the TMAT curriculum, there is a need for the continuous professional development of DHs, such that they are able to balance external forces and internal forces. Full article

In recent years, Estonian employers, upper secondary schools, and other stakeholders have devised various new collaborative measures to effectively integrate workplace learning (WPL) and school education. These efforts are aimed at enhancing the key competences of students. However, the educational purpose and effectiveness of the various initiatives are unknown. The main aim of this research is to explore what kinds of practises are applied in Estonian upper secondary schools to integrate formal education and WPL, and the experiences and requirements of schools and employers in this area. The empirical study is based on individual and focus group interviews conducted with upper secondary school teachers, head teachers, and employers. A phenomenological approach and inductive thematic analysis were used to examine current practises. The results of the study show that workplace learning is integrated into school learning mainly for the development of key competences and career competencies. An important part of the learning process is reflecting on experiences implemented via institutional cooperation. In core or foundation subjects, especially STEM subjects, the topics of work life or recognition of work experience are rarely encountered in school. Full article

Coding and computational thinking (CT) are important skills to include in early childhood education. Measuring these vital skills in preschool-age children can be challenging. We modified the TechCheck-PreK assessment, an unplugged measure of CT for 3-to-5-year-old children, to increase reliability and to add foundational coding concepts. We created the Coding Readiness Assessment (CRA) from a subset of nine TechCheck-PreK items and twelve new items that assess additional CT and coding readiness constructs. In an initial feasibility study of the CRA, teachers observed impulsive responses by children. We mitigated this by implementing a brief delay between the appearance of the question and the timeframe in which children could respond. In a subsequent randomized control trial, the CRA was administered 1637 times by Head Start educators. The assessment took an average of 9.8 min to administer. CRA scores were normally distributed and increased on average as a function of age. Girls scored slightly higher than boys, although the difference was not significant when age and race were taken into account. The CRA showed acceptable levels of reliability in terms of internal consistency (α = 0.78) and test–retest reliability (r = 0.65). Results from a 3PL indicate that the CRA has suitable levels of difficulty and skill level discrimination for 3-to-5-year-olds. The 3PL guessing parameter was 0.28, indicating that steps to reduce impulsive responses were successful. We conclude that the CRA has suitable properties for assessing preschool-age children’s CT skills and coding readiness. Full article