Search Results (246)

UAV small target detection in urban security, disaster monitoring, agricultural inspection, and other fields faces the challenge of increasing accuracy and real-time requirements. However, existing detection algorithms still have weak small target representation ability, extensive computational resource overhead, and poor deployment adaptability. Therefore, this paper proposes a lightweight algorithm, I-YOLOv11n, based on YOLOv11n, which is systematically improved in terms of both feature enhancement and structure compression. The RFCBAMConv module that combines deformable convolution and channel–spatial attention is designed to adjust the receptive field and strengthen the edge features dynamically. The multiscale pyramid of STCMSP context and the lightweight Transformer–DyHead hybrid detection head are designed by combining the multiscale hole feature pyramid (DFPC), which realizes the cross-scale semantic modeling and adaptive focusing of the target area. A collaborative lightweight strategy is proposed. Firstly, the semantic discrimination ability of the teacher model for small targets is transferred to guide and protect the subsequent compression process by integrating the mixed knowledge distillation of response alignment, feature imitation, and structure maintenance. Secondly, the LAMP–Taylor channel pruning mechanism is used to compress the model redundancy, mainly to protect the key channels sensitive to shallow small targets. Finally, K-means++ anchor frame optimization based on IoU distance is implemented to adapt the feature structure retained after pruning and the scale distribution of small targets of UAV. While significantly reducing the model size (parameter 3.87 M, calculation 14.7 GFLOPs), the detection accuracy of small targets is effectively maintained and improved. Experiments on VisDrone, AI-TOD, and SODA-A datasets show that the mAP@0.5 and mAP@0.5:0.95 of I-YOLOv11n are 7.1% and 4.9% higher than the benchmark model YOLOv11 n, respectively, while maintaining real-time processing capabilities, verifying its comprehensive advantages in accuracy, light weight, and deployment. Full article

The sustainable development of teaching expertise in research-intensive universities remains a critical global challenge. This study investigates the distinctive characteristics of expert teachers—exemplary faculty in research universities—addressing their developmental trajectories and motivational mechanisms within prevailing incentive systems that prioritize research productivity over pedagogical excellence. Employing grounded theory methodology, we conducted iterative coding of 20,000-word interview transcripts from 13 teaching-awarded professors at Chinese “Double First-Class” universities. Key findings reveal the following: (1) Compared to the original K-12 expert teacher model, university-level teaching experts exhibit distinctive disciplinary mastery—characterized by systematic knowledge structuring and cross-disciplinary integration capabilities. (2) Their developmental trajectory transcends linear expertise acquisition, instead manifesting as a problem-solving continuum across four nonlinear phases: career initiation, dilemma adaptation, theoretical consciousness, and leadership expansion. (3) Sustainable teaching excellence relies fundamentally on teachers’ professional passion, sustained through a virtuous cycle of high-quality instructional engagement and external validation (including positive student feedback, institutional recognition, and peer collaboration). Universities must establish comprehensive support systems—including (a) fostering a supportive and flexible learning atmosphere, (b) reforming evaluation mechanisms, and (c) facilitating interdisciplinary collaboration through teaching development communities—to institutionalize this developmental ecosystem. Full article

In this research report, we consider how to empower K-8 teachers as mathematics instructional leaders to initiate and sustain improvements within their schools, as a practical and sustainable model of enacting change in mathematics education more broadly by developing leadership from within. We share the theoretical framework and findings from a 5-year National Science Foundation project. We utilized a longitudinal mixed methods approach, collecting data on teachers’ knowledge, instructional practices, leadership practices, and self-perception of growth throughout the project, triangulated with focus group data from teachers’ school administrators and project leaders and logs of leadership activities. Findings indicate positive changes in teachers’ knowledge and practices and in their role as instructional leaders in their schools, districts, and the mathematics education community. We conclude by sharing factors that appeared to support teachers’ growth as instructional leaders and implications for practice and research. Full article

This study investigated pre-service teachers’ (PTs) intentions to adopt generative AI (GenAI) tools in future classrooms by applying an extended Technology Acceptance Model (TAM). Participants were enrolled in multiple teacher-preparation programs within a single U.S. higher education institution. Through a structured GenAI-integrated activity using Khanmigo, a domain-specific AI platform for K-12 education, PTs explored AI-supported instructional tasks. Post-activity data were analyzed using PLS-SEM. The results showed that perceived usefulness (PU), perceived ease-of-use (PEU), and self-efficacy (SE) significantly predicted behavioral intention (BI) to adopt GenAI, with SE also influencing both PU and PEU. Conversely, personal innovativeness in IT and perceived cyber risk showed insignificant effects on BI or PU. The findings underscored the evolving dynamics of TAM constructs in GenAI contexts and highlighted the need to reconceptualize ease-of-use and risk within AI-mediated environments. Practically, the study emphasized the importance of preparing PTs not only to operate AI tools but also to critically interpret and co-design them. These insights inform both theoretical models and teacher education strategies, supporting the ethical and pedagogically meaningful integration of GenAI in K-12 education. Theoretical and practical implications are discussed. Full article

This study investigates the impact of teacher decisions and other contextual factors on indoor air quality (IAQ) in mechanically ventilated elementary school classrooms using low-cost air quality sensors. Four classrooms at a K–8 school in San Jose, California, were monitored for airborne particulate matter (PM), carbon dioxide (CO₂), temperature, and humidity over seven weeks. Each classroom was equipped with an HVAC system and a portable air cleaner (PAC), with teachers having full autonomy over PAC usage and ventilation practices. Results revealed that teacher behaviors, such as the frequency of door/window opening and PAC operation, significantly influenced both PM and CO₂ levels. Classrooms with more active ventilation had lower CO₂ but occasionally higher PM_2.5 due to outdoor air exchange, while classrooms with minimal ventilation showed the opposite pattern. An analysis of PAC filter material and PM morphology indicated distinct differences between indoor and outdoor particle sources, with indoor air showing higher fiber content from clothing and carpets. This study highlights the critical role of teacher behavior in shaping IAQ, even in mechanically ventilated environments, and underscores the potential of low-cost sensors to support informed decision-making for healthier classroom environments. Full article

The United Nations’ 2030 Agenda for Sustainable Development centers on the 17 Sustainable Development Goals (SDGs). Among these goals, two address climate change education: Goal 13, Climate Action, and Goal 4, Quality Education. In order to build a more sustainable future, climate change education is critical. In 2022, New Jersey became the first state in the US to integrate climate change into learning standards across subjects and grade levels K-12. In an effort to better understand the way in which teachers began to include climate change in their instruction, 50 teachers were observed implementing a lesson of their choosing that included climate change throughout the 2023–2024 academic year. Though most of the observed lessons featured science, many subject areas were included in the dataset, such as art, technology, history, and physical education. Teachers engaging in climate change instruction tended to use a variety of instructional practices. In nearly all cases, a multitude of methodologies were used in each lesson. However, small group instruction was featured in nearly all observed lessons. Quantitative descriptions of the findings are followed by three vignettes of exemplar instruction to provide a clearer understanding of the context of this work. These findings provide a scope for how climate change can be integrated in instructional settings at scale and suggestions for leveraging the experiences of early adopters of this innovation to support widespread implementation. Full article

This study investigates the creators, effects, and inhibitors of technostress among STEM teachers, addressing a critical yet underexplored issue in the digitalization of education. Grounded in the technostress model and the job demands–resources (JD-R) model, the study examines the relationships among technostress creators, burnout, organizational effects (job satisfaction, organizational commitment, and work performance), and technostress inhibitors. A cross-sectional survey was conducted with 378 STEM teachers from Zhejiang Province, China. Structural equation modeling (SEM) was employed to test the hypothesized paths. The results revealed that technostress creators significantly increased teacher burnout and negatively affected organizational commitment and work performance. Burnout mediated the impact of technostress creators on job satisfaction and organizational commitment. Technostress inhibitors were found to alleviate burnout, mitigate technostress creators, and enhance STEM teachers’ commitment. These findings validate the applicability of the technostress model in the context of K–12 STEM education in China and highlight the importance of organizational mechanisms for supporting teacher well-being and performance. The study contributes to both theory and practice by proposing an integrative model of technostress and offering actionable recommendations for school leadership to effectively manage technostress. Full article

This article presents a comparison of two qualitative case studies. The first case study is a partnership group involving two urban secondary school teachers working with one engineer and one education faculty member where they implemented several science, technology, engineering, and mathematics (STEM) lessons over the course of an academic year. The second case study is a partnership group involving undergraduate college students working together to build a data collection device attached to a high-altitude balloon to answer a scientific question or solve an engineering problem and translate the project into engaging lessons for a K-12/secondary student audience. The studies employed a socio-cultural theoretical framework as the lens to examine the individuals’ perspectives, experiences, and engineering meaning-making processes, and to consider what these meant to the partnership itself. The methods included interviews, focus groups, field notes, and artifacts. The analysis involved multi-level coding. The findings indicated that the strength of the partnership (pre, little p, or big P) among participants influenced the strength of the secondary engineering lessons. The partnership growth implications in terms of K-12/secondary and collegiate engineering education included the engineering lesson strength, partnership, and engineering project sustainability The participant partnership meanings revolved around lesson creation, incorporating engineering ideas into the classroom, increasing communication, and increasing secondary students’ learning, while tensions arose from navigating (not quite negotiating) roles as a team. A call for attention to school–university partnerships and the voices heard in engineering partnership building are included since professional skills are becoming even more important due to advances in artificial intelligence (AI) and other technologies. Full article

Programming education has become increasingly vital within global K–12 curricula, and large language models (LLMs) offer promising solutions to systemic challenges such as limited teacher expertise and insufficient personalized support. Adopting a human-centric and systems-oriented perspective, this study employed a six-week quasi-experimental design involving 103 Grade 7 students in China to investigate the effects of instruction supported by the iFLYTEK Spark model. Results showed that the experimental group significantly outperformed the control group in programming performance, cognitive interest, and programming self-efficacy. Beyond these quantitative outcomes, qualitative interviews revealed that LLM-assisted instruction enhanced students’ self-directed learning, a sense of real-time human–machine interaction, and exploratory learning behaviors, forming an intelligent human–AI learning system. These findings underscore the integrative potential of LLMs to support competence, autonomy, and engagement within digital learning systems. This study concludes by discussing the implications for intelligent educational system design and directions for future socio-technical research. Full article

With the need to ensure equitable and inclusive computer science (CS) education for K-12 students, much effort has been devoted to promoting secondary CS teachers’ practices and pedagogies. However, there is a lack of focus on elementary teachers’ experiences, especially those of teachers of color. This study stands at the intersections of Black/African American teachers teaching at an elementary school with a majority of historically underrepresented and economically disadvantaged students (Black/African Americans and Hispanic/Latinx). Using a basic qualitative approach with constant comparative analysis, this study revealed important insights regarding the professional growth manifested by six teachers of color over the course of computer science professional development and classroom implementation. Data analysis revealed five main themes reflecting the teachers’ growth: (1) Teachers reported positive outcomes including improved understanding, confidence, and intentions regarding CS integration as a result of attending PD; (2) Teachers demonstrated enhanced abilities to use a variety of tools and resources in CS teaching after PD; (3) Teachers discussed various pedagogies, including culturally and personally responsive pedagogical practices, and racial awareness to promote inclusive instruction in the classroom and used strategies to promote personal relevance more than the collective cultural values or beliefs in CS teaching specifically; (4) Teachers reported having ongoing reflections on how they can implement successful CS-integrated instruction with their enhanced knowledge and beliefs; (5) Positive student outcomes were both reported by the teachers and observed by the researchers as a result of teachers’ experimentation, which gave the teachers more confidence to enact CS teaching. Areas for improvement were also identified. This paper discussed the important implementations of fostering professional growth in teachers of color for broadening minoritized students’ participation in computing. Full article

The integration of artificial intelligence (AI) in education highlights the growing need for AI literacy among K–12 teachers, particularly to enable effective human–machine cooperation. This study investigates Saudi K–12 educators’ AI literacy competencies across four key dimensions: awareness, ethics, evaluation, and use. Using a survey of 426 teachers and analyzing the data through descriptive statistics and structural equation modeling (SEM), this study found high overall literacy levels, with ethics scoring the highest and use slightly lower, indicating a modest gap between knowledge and application. The SEM results indicated that awareness significantly influenced ethics, evaluation, and use, positioning it as a foundational competency. Ethics also strongly predicted both evaluation and use, while evaluation contributed positively to use. These findings underscore AI literacy skills’ interconnected nature and point to the importance of integrating ethical reasoning and critical evaluation into teacher training. This study provides evidence-based guidance for educational policymakers and leaders in designing professional development programs that prepare teachers for effective and responsible AI integration in K–12 education. Full article

This article presents early insights from a small-scale action research project designed to promote positive reading dispositions and expand reading repertoires among pre-service teachers at a regional Australian university. Building on Professor Teresa Cremin and colleagues’ seminal Teachers as Readers research in the U.K., the study highlights the critical role of teacher educators in fostering pre-service teachers’ knowledge and enthusiasm. It explores how the use of high-quality children’s literature alongside a reading-for-enjoyment (RfE) pedagogical approach can shape pre-service teachers’ identities as future reading role models. Strategies such as shared read-alouds, book talk, and enjoyment-centred reading practices were employed to strengthen connections with children’s literature. The study also modelled how children’s literature could be used as mentor texts to support curriculum-aligned instruction and develop pedagogical confidence. Wenger’s Communities of Practice theory provides a framework to demonstrate how a children’s literature-based approach and RfE pedagogical practices contribute to the formation of positive reader identities. Amid concerns about improving literacy rates and teacher preparedness for reading instruction, this study illustrates the transformative potential of integrating children’s literature and RfE pedagogy into initial teacher education to cultivate future Reading Teachers capable of inspiring a love of reading and building communities of readers in their classrooms. Full article

Microplastics are found in Earth’s atmosphere, lithosphere, hydrosphere, pedosphere, and ecosphere. While there is a growing interest and need to solve this grand challenge in both the academic and policy realms, few have engaged with academics, policymakers, and community partners to co-identify the problem, co-design research, and co-produce knowledge in tackling this issue. Using a convergence research framework, we investigated the perception of microplastic pollution among different end users, delivered educational materials to K-12 teachers and practitioners, and identified key sampling points for assessing environmental microplastic concentrations in the Columbia River Basin, United States. Three community partner workshops identified regional issues and concerns associated with microplastic pollution and explored potential policy intervention strategies. The stakeholder survey, co-designed with community partners, identified varying perceptions around microplastic pollution across educators, government employees, non-profit employees, and industry practitioners. Pre- and post-test results of teacher workshops show increases in participants’ knowledge after taking a four-week summer class with the knowledge being translated to their students. Community partners also helped develop a unique passive sampling plan for atmospheric deposition of microplastics using synoptic moss samples and provided freshwater samples for microplastic quantification across the basin. Our study drew three major lessons for successfully conducting convergence environmental research—(1) communication and trust building, supported by the use of key-informants to expand networks; (2) co-creation through collaboration, where partners and students shaped research and education to enhance impact; and (3) change-making, as project insights were translated into policy discussions, community outreach, and classrooms. Full article

The COVID-19 pandemic disrupted U.S. K-12 education systems, forcing teachers to adopt emergency remote teaching with minimal preparation. This study investigates the challenges and adaptive strategies of 16 U.S. K-12 educators during the pandemic. Qualitative analysis of semi-structured interviews with 16 educators was conducted to identify key themes. Findings reveal that innovative engagement strategies—including interactive activities, long-term projects, and inclusive virtual environments—were pivotal for sustaining participation. Challenges such as disparities in students’ home environments, technical limitations, and motivational declines underscored the need for parental collaboration, emotional support frameworks, and teacher-specific professional development. These results highlight actionable pathways to strengthen resilience and equity in online education systems during crises. Full article

Digital teaching competence (DTC) is key for the teaching profession at any educational level. In early childhood education, DTC poses an important challenge due to the particularities of integrating digital technologies into work with infants. This article proposes an adaptation of DigCompEdu for early childhood education. The construction of this proposal was based on international collaboration, an in-depth literature review, and the expert mediation of the authors, resulting in the adaptation of DigCompEdu’s 22 competency descriptors to the field of initial teacher training in early childhood education. Expert competence coefficient K was applied to select 22 experts for the validation process to establish its pertinence, importance, and clarity, who positively evaluated the 22 descriptors of the proposal. The results consist of a DTC proposal in accordance with the DigCompEdu framework for early childhood education students validated by experts, as a starting point for future research for assessing or self-assessment of DTC, and as a guide to define strategies in initial teacher training. Full article