Search Results (389)

This article reports on a Swedish study investigating how children aged 2–3 years experience mathematical concepts through representations in play-responsive teaching. Drawing on the semiotic–cultural theory of learning, this study examines how representations, such as spoken language, bodily action, and artifacts, are mediated. Video-recorded teaching sessions are analyzed to identify semiotic means of objectification and semiotic nodes at which these representations converge. The analysis distinguishes between children encountering concepts expressed by others and expressing concepts themselves. The results indicate that play-responsive teaching creates varied opportunities for experiencing mathematical concepts, with distinct modes of sensuous cognition linked to whether a concept is encountered or expressed. This study underscores the role of teachers’ choices in shaping these experiences and highlights bodily action as a significant form of representation. These findings aim to inform the use of representations in teaching mathematics to the youngest children in preschool. Full article

Artificial intelligence (AI) holds considerable promise to transform education, from personalizing learning to enhancing teaching efficiency, yet it simultaneously introduces significant concerns regarding ethical implications and responsible implementation. This exploratory survey investigated the perceptions of 376 teachers, university students, and future educators from the University of Salerno area concerning AI integration in education. Data were collected via a comprehensive digital questionnaire, divided into sections on personal data, AI’s perceived impact, its usefulness, and specific applications in education. Descriptive and inferential statistical analyses, including mean, mode, standard deviation, and 95% confidence intervals, were applied to the Likert scale responses. Results indicated a general openness to AI as a supportive tool for personalized learning and efficiency. However, significant reservations emerged regarding AI’s capacity to replace the human role. For instance, 69% of participants disagreed that AI tutors could match human feedback efficiency, and strong opposition was found against AI replacing textbooks (81% disagreement) or face-to-face lessons (87% disagreement). Conversely, there was an overwhelming consensus on the necessity of careful and conscious AI use (98% agreement). Participants also exhibited skepticism regarding AI’s utility for younger learners (e.g., 80% disagreement for ages 0–6), while largely agreeing on its benefit for adult learning. Strong support was observed for AI’s role in providing simulations and virtual labs (89% agreement) and developing interactive educational content (94% agreement). This study underscores a positive inclination towards AI as an enhancement tool, balanced by a strong insistence on preserving human interaction in education, highlighting the need for thoughtful integration and training. Full article

The present study focuses on investigating the contribution of puppetry as a pedagogical and psychosocial tool in special education, addressing the literature gap in the systematic documentation of the experiences of special education teachers, concerning its use in daily teaching practice. The main objective is to capture the way in which puppetry enhances the learning, social and therapeutic support of students with complex educational and psychosocial needs. The study employs a qualitative phenomenological approach, conducting semi-structured interviews with eleven special education teachers who integrate puppetry into their teaching. Qualitative data were analyzed using thematic analysis. The findings highlight that puppetry significantly enhances cognitive function, concentration, memory and language development, while promoting the active participation, cooperation, social inclusion and self-expression of students. In addition, the use of the puppet acts as a means of psycho-emotional empowerment, supporting positive behavior and helping students cope with stress and behavioral difficulties. Participants identified peer support, material adequacy and training as key factors for effective implementation, while conversely, a lack of resources and time is cited as a key obstacle. The integration of puppetry in everyday school life seems to ameliorate a more personalized, supportive and experiential learning environment, responding to the diverse and complex profiles of students attending special schools. Continuous training for teachers, along with strengthening the collaboration between the arts and special education, is essential for the effective use of puppetry in the classroom. 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

Metacognition and self-regulated learning are widely understood to offer significant benefits for pupils’ mathematical problem solving; however, the existing literature highlights that the under-representation of these concepts in curriculum, policy, and teacher professional development means that their potential for impact remains unfulfilled. This article, therefore, examines the potential value of an innovative fading professional development programme—“Stepping Stones”—in enhancing teachers’ understanding and use of metacognitive strategies for mathematical problem solving. Adopting a convergent mixed methods design, this pilot evaluation involved Year 2 teachers across five primary schools. The results from both qualitative and quantitative data demonstrate that, as the scaffolding provided by programme materials faded and teachers assumed greater responsibility for session planning, they incorporated metacognitive strategies into their planning and delivery with increased independence. The results also indicate the acceptability of this professional development model, suggesting that, when combined with peer collaboration, the fading design was associated with improvements in knowledge and confidence regarding both metacognition and mathematical problem solving, alongside increased ownership and buy in. The conclusions advocate further examination and implementation of fading models of professional development to promote the understanding and use of metacognition for mathematical problem solving and recommend exploration into different professional development contexts. Full article

Underwater acoustic target recognition (UATR) is challenged by complex marine noise, scarce labeled data, and inadequate multi-scale feature extraction in conventional methods. This study proposes DART-MT, a semi-supervised framework that integrates a Dual Attention Parallel Residual Network Transformer with a mean teacher paradigm, enhanced by domain-specific prior knowledge. The architecture employs a Convolutional Block Attention Module (CBAM) for localized feature refinement, a lightweight New Transformer Encoder for global context modeling, and a novel TriFusion Block to synergize spectral–temporal–spatial features through parallel multi-branch fusion, addressing the limitations of single-modality extraction. Leveraging the mean teacher framework, DART-MT optimizes consistency regularization to exploit unlabeled data, effectively mitigating class imbalance and annotation scarcity. Evaluations on the DeepShip and ShipsEar datasets demonstrate state-of-the-art accuracy: with 10% labeled data, DART-MT achieves 96.20% (DeepShip) and 94.86% (ShipsEar), surpassing baseline models by 7.2–9.8% in low-data regimes, while reaching 98.80% (DeepShip) and 98.85% (ShipsEar) with 90% labeled data. Under varying noise conditions (−20 dB to 20 dB), the model maintained a robust performance (F1-score: 92.4–97.1%) with 40% lower variance than its competitors, and ablation studies validated each module’s contribution (TriFusion Block alone improved accuracy by 6.9%). This research advances UATR by (1) resolving multi-scale feature fusion bottlenecks, (2) demonstrating the efficacy of semi-supervised learning in marine acoustics, and (3) providing an open-source implementation for reproducibility. In future work, we will extend cross-domain adaptation to diverse oceanic environments. Full article

Socioemotional relationships significantly influence students’ learning, development, and school well-being. This study explored gender differences in sympathy and antipathy based on data originally gathered via a digital platform primarily designed for mathematical learning. The platform administered a sociometric test to a large sample (3090 of fourth-grade students (mean age = 10.19 years, 47.7% girls) in the Chilean Metropolitan Region. Teachers facilitated the test, allowing students to nominate peers they liked and disliked. The study confirmed a trend of homophily in sympathy, with students preferring same-gender peers. It also revealed a gender disparity in antipathy: girls nominated an average of 5.27 disliked classmates (SD = 7.20), 1.45 more nominations than boys, with a Cohen’s d of 0.22. These findings provide insights to enhance school climate and address the mental health implications of social exclusion, considering gender differences. These results underscore the potential of scalable digital tools to support educators in monitoring peer dynamics and fostering inclusive, emotionally supportive school environments. Full article

The latest Relationships Education, Relationships and Sex Education, and Health Education (RSHE) Draft Guidance seeks to reduce the inclusion of LGBT themes in English schools. Additionally, the Gender Questioning Draft Guidance for Schools and Colleges and the Cass Review overlook the rights of trans and non-binary young people, further intensifying the heated debates surrounding their lives. In response, the author draws upon research conducted in a primary school in Greater London in 2021, when statutory RSHE, including LGBT content, was first introduced. The research aimed to understand how teachers felt about teaching RSHE and to collaborate with them to enhance pupil learning within and beyond the RSHE curriculum. This paper critiques lesson observations and teachers’ reflections on their lessons using a Framework for Sexuality Education and Queer Theory. The researcher’s call to rethink how RSHE is taught should not be taken to mean it should not be taught. To the contrary, the findings suggest a need for the school to broaden its curriculum, teaching methods, and strategies to become a truly ‘LGBT-inclusive’ environment. However, the paper also illuminates the apprehensions these primary school teachers experienced, which in turn influenced pedagogical decisions. The article concludes by recommending specific whole-school approaches and effective pedagogical practices for RSHE in the school, which could be beneficial to other primary school settings. Effective teaching of LGBT themes requires clear support for educators, especially within the complexity of a primary school setting and given the changing political and social climate. Full article

In the face of escalating sustainability challenges globally, such as climate change, poverty, inequality, and injustices, the need for a systematic approach to tackle them through the infusion of Sustainable Development Goals (SDGs) in higher education has become increasingly critical. This article explores the crucial issue of professionalizing academic teaching, emphasizing the readiness of academic teachers to cope with sustainability and SDGs in higher education. Using the Stages of Concern Theory and the Concerns-Based Adoption Model (CBAM) to professionalize academic teaching to address SDGs in teaching, learning, and the curriculum, a sample of 1566 academic teachers in nine Latin American universities responded to the survey. This study aimed to answer two key questions, as follows: (1) How do the years of teaching experience affect academic staff’s stages of concern? (2) How do different academic teaching areas influence the academic staff’s stages of concern? The trend reveals that faculty members with fewer than four years of service scored higher than those with twenty or more years. Similarly, academic teaching staff from the Education Sciences have a significantly higher mean score and effect size than faculty members from the Humanities, Engineering, Social Sciences, Sciences, and Health Sciences across all stages of concern. However, despite these differences, professional development initiatives should be designed to match all teaching staff regardless of years of service and subject area by encouraging teamwork and increasing understanding of the critical importance of transformative teaching and learning. Full article

Nonlinear characteristics of solar photovoltaic (PV) and nonuniform surrounding conditions, including partial shading conditions (PSCs), are the major factors responsible for lower conversion efficiency in solar panels. One major condition is the cause of the multiple peaks and oscillation around the peak point leading to power losses. Therefore, this study proposes a novel hybrid framework based on an artificial neural network (ANN) and fractional order PID (FOPID) controller, where new algorithms are employed to train the ANN model and to tune the FOPID controller. The primary aim is to maintain the computed power close to its true peak power while mitigating persistent oscillations in the face of continuously varying surrounding conditions. Firstly, a modified shuffled frog leap algorithm (MSFLA) was employed to train the feed-forward ANN model using real-world solar PV data with the aim of generating a reference solar PV peak voltage. Subsequently, the parameters of the FOPID controller were tuned through the application of the Sanitized Teacher–Learning-Based Optimization (s-TLBO) algorithm, with a specific focus on achieving maximum power point tracking (MPPT). The robustness of the proposed hybrid framework was assessed using two different types (monocrystalline and polycrystalline) of solar panels exposed to varying levels of irradiance. Additionally, the framework’s performance was rigorously tested under cloudy conditions and in the presence of various partial shading scenarios. Furthermore, the adaptability of the proposed framework to different solar panel array configurations was evaluated. This work’s findings reveal that the proposed hybrid framework consistently achieves maximum power point with minimal oscillation, surpassing the performance of recently published works across various critical performance metrics, including the $MP{P}_{efficiency}$, relative error (RE), mean squared error (MSE), and tracking speed. Full article

This study aims to investigate the views of mentor teachers on sustainable teacher education in Northern Cyprus. The Teaching Practice course (internship) was taken as the basis, and the views of mentor teachers on the meaning, difficulties, responsibilities, and feedback process of teaching practices were examined. Data were collected through open-ended interviews with 50 classroom teachers using the qualitative research approach phenomenological model. When the findings are examined, it is revealed that mentor teachers see internship as very important in terms of applying theoretical knowledge, gaining practical experience, and developing professional identity. Difficulties include classroom management difficulties, problems with students, inadequate preparation, and communication problems. Mentor teachers emphasize that their roles of providing guidance, feedback, and support are important. It was stated that preparation, time management, and effective communication are among the responsibilities of candidate teachers. The study emphasized the importance of mentoring in teaching practices in the context of effective communication and providing constructive feedback. Recommendations include improved internship program design focusing on classroom management, communication skills, and material preparation. Additionally, fostering a supportive learning environment and providing professional development opportunities for both mentors and student teachers are essential. The study contributes to understanding the complexities of teacher education and the critical role of effective mentoring in shaping future educators. Evaluating the findings of this study in the context of sustainable teacher education highlights how mentors’ role model practices, ongoing support for pre-service teachers, and reinforcement of effective teaching strategies can contribute to the sustainability of pre-service teacher education. Full article

As the Fourth Industrial Revolution (4IR) reshapes society, digital leadership in education becomes crucial. This study explores teachers’ perspectives on high school principals’ digital leadership competencies in Oyo State, Nigeria, a region grappling with the digital divide. This offers a counterpoint to research in high-income countries. Using a quantitative, descriptive research design, 381 teachers across three senatorial districts were selected via multi-stage sampling. The study addressed one research question and tested two hypotheses. Data were collected using the High Schools’ Digital Leadership Standards Questionnaire (HSDLSQ). The reliability coefficients ranged from α = 0.85 to 0.91 across five domains: visionary leadership, digital learning culture, professional development, systemic improvement, and digital citizenship. Descriptive statistics (percentages, mean, and standard deviation) and inferential statistics (independent t-test) were used for analysis at a 0.05 significance level. The findings revealed that high school leaders possess the skills to meet the ISTE-A digital leadership standards set by the International Society for Technology in Education (ISTE). Female leaders exhibited slightly higher digital skills than males, though gender was insignificant in meeting the ISTE-A standards overall. The study underscores the importance of digital leadership in school management, offering practical implications for leadership practices, policy decisions, and future research. Although the ISTE-A standards are met, the emphasis on systemic improvement highlights how Nigerian principals adapt strategies to local resource constraints, differing from high-tech education systems. Recommendations include promoting the ISTE-A standards among school leaders, investing in digital leadership training, fostering digital literacy, and supporting adaptability to technological changes to advance digital transformation in education. Full article

(1) Background: Metacognition plays a crucial role in education, with teachers’ metacognitive awareness being a critical determinant of effective teaching and learning. Despite its importance, validated instruments for assessing teachers’ metacognition are scarce, especially in non-English-speaking contexts. This study aims to adapt and evaluate the psychometric properties of the Metacognitive Awareness Inventory for Teachers (MAIT) to the Romanian cultural and educational contexts. (2) Methods: After rigorous cross-cultural adaptation procedures, including translation, back-translation, and expert review, the MAIT was administered to 444 Romanian teachers (86.5% female, mean age = 47.18 years, SD = 8.92). Exploratory and confirmatory factor analyses were conducted to assess the structural validity of the instrument. (3) Results: A 21 item second-order two-factor model including knowledge of cognition (KC) and regulation of cognition (RC) showed good fit indices (χ²/df = 3.5, CFI = 0.965, TLI = 0.96, SRMR = 0.023, RMSEA = 0.075). Limitations of the study are discussed. Full article

The identification and classification of network traffic are crucial for maintaining network security, optimizing network management, and ensuring reliable service quality. These functions help prevent malicious activities, such as network attacks and illegal intrusions, while supporting the efficient allocation of network resources and enhancing user experience. However, the widespread use of traffic encryption technology, while improving data transmission security, also obscures the content of traffic, making it challenging to accurately classify and identify encrypted traffic. This limitation hampers both network security maintenance and further improvements in service quality. Therefore, there is an urgent need to develop an efficient and accurate encryption traffic identification method. This study addresses three key challenges: First, existing methods fail to explore the potential relationship between flow load features and sequence features during feature extraction. Second, there is a need for approaches that can adapt to the diverse characteristics of different protocols, ensuring the accuracy and robustness of encrypted traffic identification. Third, traditional deep learning models need large amounts of labeled data, which are expensive to acquire. To overcome these challenges, we propose an encrypted traffic recognition method based on a CLSTM model (a combination of 2-conv CNN and BiLSTM) and Mean Teacher collaborative learning. This approach detects and integrates traffic load features with sequence features to improve the accuracy and robustness of encrypted traffic identification while reducing the model’s reliance on labeled data through the consistency constraint of unlabeled data using Mean Teacher. Experimental results demonstrate that the CLSTM-MT collaborative learning method outperforms traditional methods in encrypted traffic identification and classification, achieving superior performance even with limited labeled data, thus addressing the high cost of data labeling. Full article

Assessing the visual quality of rural landscapes is pivotal for quantifying ecological services and preserving cultural heritage; however, conventional ecological indicators neglect emotional and cognitive dimensions. To address this gap, the present study proposes a novel visual quality assessment method for rural landscapes that integrates multimodal sentiment classification models to strengthen sustainability metrics. Four landscape types were selected from three representative villages in Dalian City, China, and the physiological signals (EEG, EOG) and subjective evaluations (Beauty Assessment and SAM Scales) of students and teachers were recorded. Binary, ternary, and five-category emotion classification models were then developed. Results indicate that the binary and ternary models achieve superior accuracy in emotional valence and arousal, whereas the five-category model performs least effectively. Furthermore, an ensemble learning approach outperforms individual classifiers in both binary and ternary tasks, yielding a 16.54% increase in mean accuracy. Integrating subjective and objective data further enhances ternary classification accuracy by 7.7% compared to existing studies, confirming the value of multi-source features. These findings demonstrate that a multi-source sentiment computing framework can serve as a robust quantitative tool for evaluating emotional quality in rural landscapes and promoting their sustainable development. Full article