Search Results (232)

Background/Objectives. Social and Emotional Education (SEE) interventions during early childhood have shown considerable promise in enhancing children’s emotion understanding, social competence, and behavioural adjustments. However, few studies have examined their long-term impact, especially across the preschool-to-primary school transition. This study evaluated the effectiveness of a manualized SEE program, Timmy’s Trip to Planet Earth, in promoting emotional, behavioural, and social functioning over time. Methods. A quasi-experimental longitudinal design was adopted with pre- and post-test assessments conducted approximately 18 months apart. Participants were 89 typically developing children (aged 59–71 months), assigned to an experimental group (n = 45) or a waiting-list group (n = 44). The program combined teacher training, classroom-based lessons, home activities, and teachers’ ongoing implementation support. The effectiveness of the program was measured via the Test of Emotion Comprehension (TEC), the Strengths and Difficulties Questionnaire (SDQ), and the Social Competence and Behavior Evaluation (SCBE-30). Results. Significant Time × Group interactions were observed for the TEC External and Mental components, indicating greater improvements in emotion recognition and mental state understanding in the intervention group. The SDQ revealed significant reductions in conduct problems and increased prosocial behaviours. In the SCBE-30, a significant interaction effect was found for social competence, with the intervention group showing greater improvement over time compared to the control group. Conclusions. The findings suggest that SEE programs can produce meaningful and lasting improvements in children’s emotional and social skills across key educational transitions. Teacher training and family involvement likely played a critical role in supporting the program’s sustained impact. Full article

Students with special needs often require more assistance and attention to meet their educational needs. However, schools frequently grapple with a critical shortage of special education teachers and support staff. This shortage of special education teachers can result in limited resources for general and subject teachers (e.g., math, science), making it challenging to provide individualized support to students with special needs. Specifically, subject teachers may struggle to design effective curricular content modifications and accommodations for such students without the guidance and suggestions of special education teachers. Artificial Intelligence (AI) technologies can provide some support for teachers and schools in meeting the needs of students with special needs. Also, AI may help reduce teachers’ workload. In this technology review, we assess the capabilities of Magic School AI (MSAI) in providing accommodations and modifications to assist teachers in streamlining their workload and fostering inclusivity in their classrooms. We examined five functions: text leveler, text scaffolders, assignment scaffolder, exemplar and non-examples, and sentence starters. Additionally, we discuss the limitations of MSAI and conclude by suggesting potential improvements for the system. Full article

This study investigated the effectiveness of integrating digital tools with origami activities to enhance geometric understanding and creative thinking among kindergarten children in Kuwait. A quasi-experimental pre-test–post-test design involved 60 children (aged from 5 years and 9 months to 6 years), who were randomly assigned to experimental (n = 30) and control (n = 30) groups. The experimental group received a four-week intervention using the Paperama app and paper folding, while the control group followed the standard curriculum. Wilcoxon signed-rank tests showed significant gains in the experimental group’s geometric understanding (Z = 3.82; p < 0.001) and creative thinking (Z = 4.15; p < 0.001), with large effect sizes (r = 0.78). Descriptive analysis further revealed that the experimental group outperformed the control group in post-test scores for geometric understanding (M = 84.06 vs. M = 74.39), reinforcing the intervention’s practical impact. The control group showed no significant improvement (p = 0.16). These findings highlight the value of blended origami instruction in developing spatial reasoning and creativity. This study contributes to early STEAM education and supports the integration of digital tools into kindergarten learning and teacher training. Full article

Voice disorders occur frequently in schoolteachers. The aim of the present cross-sectional study involving 124 German teachers was to investigate whether singing, voice training, and high self-efficacy are protective factors for vocal health. Furthermore, vocal self-concept was examined as a potential mediator explaining this relationship. Participants were assigned to the cases group if they had a clinically significant finding in voice examinations consisting of video laryngoscopy (VLS), auditory assessment (RBH), and the Voice Handicap Index (VHI) were assigned to the cases group. Psychosocial assessments comprised questions about singing activities and participation in voice training as well as validated questionnaires regarding self-efficacy (LSWS) and vocal self-concept (FESS). Group comparisons and mediation analyses were conducted. Analyses revealed a decreased risk of voice problems for teachers who sing regularly (OR: 0.442, p = 0.038). Furthermore, the absence of voice problems was associated significantly with higher self-efficacy ratings (t(113) = 1.71, p = 0.045). Both associations were mediated by vocal self-concept ratings (singing: ab = −0.422, 95%-CI [−1.102, −0.037]; self-efficacy: ab = −0.075, 95%-CI [−0.155, −0.022]). Participation in voice training in the past did not reduce the risk of voice problems significantly. The presented data suggest that regular singing and self-efficacy should be promoted in health care and prevention programs. Since no impact of sporadic participation in voice training activities on the occurrence of voice problems was found, efforts regarding the transfer of regular vocal exercises into daily life need to be intensified. Full article

Background/Objectives: Early care and education programs promote children’s social–emotional development, predicting later school success. The COVID-19 pandemic worsened an existing youth mental health crisis and increased teacher stress. Therefore, we applied an infant and early childhood mental health consultation model, Jump Start Plus COVID Support (JS+CS), aiming to decrease behavioral problems in children post-pandemic. Methods: A cluster randomized controlled trial compared JS+CS to an active control, Healthy Caregivers–Healthy Children (HC2), at 30 ECE centers in low-income areas in South Florida. Participants were not blinded to group assignment. Teachers reported on children’s social–emotional development at baseline and post-intervention using the Devereux Early Childhood Assessment and Strengths and Difficulties Questionnaire. We assessed whether teacher stress, classroom practices, and self-efficacy mediated the relationship between JS+CS and child outcomes. We also explored whether baseline behavior problems moderated JS+CS effects on child protective factors, relative to HC2. Results: Direct group-by-time differences between JS+CS and HC2 were limited. However, JS+CS demonstrated significant within-group improvements in teacher-reported child protective factors, behavior support practices, and classroom safety practices. Classroom safety practices consistently mediated positive changes in child behaviors, including the DECA total protective factor score and subdomains of initiative and self-regulation. Additionally, teacher perceptions of behavior support mediated gains in child attachment. Conclusions: JS+CS shows promise in building protective systems around children through intentional support for teachers, underscoring the value of whole-child, whole-environment approaches in early intervention. Full article

Background: Consistent evaluation methods foster fairness, reduce bias, and enhance student understanding and motivation. Notably, mathematical inconsistencies, such as improper weighting, flawed averaging, and unsound scaling, can undermine the accuracy and reliability of assigned grades. This paper addresses the critical need for consistent student evaluation methods, with a primary focus on ensuring mathematical consistency within grading systems. Methods: We propose a scheme aimed at identifying inconsistencies in student evaluation related to the mathematical framework of the used grading method. To explain the functioning of our construct, we provide mathematical representation of conventional grading methods, including summative assessments, rubrics, and the Systematic Task-Based Assessment Method (STBAM) that we have recently developed, which incorporates both traditional and fuzzy logic-based grading modules. We introduce a Consistency Index $\left({CI}_M\right)$ depending on the Mean Absolute Deviation of assigned scores ${(MAD}_M)$ and a method’s Complexity Pointer ${(CP}_M$). We also propose a method’s Efficiency Index ${(\beta }_M)$ expressed in terms of the Consistency Index and the latter indicator. We compared the mathematical consistency and efficiency of the methods addressed in this study through simulation runs. Results: We demonstrated how the proposed indices can reveal the strengths and weaknesses of each grading scheme analysed. Conclusions: The fuzzy logic-based modulus of the STBAM yielded the highest values of ${CI}_M$ and ${\beta }_M$. However, performing a pending analysis of scalability, teacher training, and cultural adaptability would be essential to strengthen the potential of the STBAM to be adopted as a reliable grading alternative to conventional grading approaches. In the meantime, our approach could provide a clear, logical, and defensible framework for testing the mathematical consistency of student assessment methods. Full article

While research articles on students’ perceptions of large language models such as ChatGPT in language learning have proliferated since ChatGPT’s release, few studies have focused on these perceptions among English as a foreign language (EFL) university students in South America or their application to academic writing in a second language (L2) for STEM classes. ChatGPT can generate human-like text that worries teachers and researchers. Academic cheating, especially in the language classroom, is not new; however, the concept of AI-giarism is novel. This study evaluated how 56 undergraduate university students in Ecuador viewed GenAI use in academic writing in English as a foreign language. The research findings indicate that students worried more about hindering the development of their own writing skills than the risk of being caught and facing academic penalties. Students believed that ChatGPT-written works are easily detectable, and institutions should incorporate plagiarism detectors. Submitting chatbot-generated text in the classroom was perceived as academic dishonesty, and fewer participants believed that submitting an assignment machine-translated from Spanish to English was dishonest. The results of this study will inform academic staff and educational institutions about how Ecuadorian university students perceive the overall influence of GenAI on academic integrity within the scope of academic writing, including reasons why students might rely on AI tools for dishonest purposes and how they view the detection of AI-based works. Ideally, policies, procedures, and instruction should prioritize using AI as an emerging educational tool and not as a shortcut to bypass intellectual effort. Pedagogical practices should minimize factors that have been shown to lead to the unethical use of AI, which, for our survey, was academic pressure and lack of confidence. By and large, these factors can be mitigated with approaches that prioritize the process of learning rather than the production of a product. Full article

The unique demands of teaching contribute to elevated stress levels among educators worldwide. Equipping teachers with adaptive coping skills is increasingly important. However, there is a gap in understanding which coping strategies are essential for pre-service teachers and how universities can best promote them. This study examines pre-service teachers’ coping strategies during a long-term internship and evaluates a low-threshold intervention to enhance stress management and self-care. Three seminar groups were randomly assigned to the experimental group (n = 54), while the remainder formed the control group (n = 119). The experimental group received a self-directed workbook at the start of their internship and three brief face-to-face sessions during accompanying seminars. The workbook modules and seminars guided the pre-service teachers in identifying stressors, developing coping skills, and utilizing personal resources. Data were collected before and after the 18-week internship, measuring well-being, internship-related stressors, and coping strategies. Structural equation modeling showed that positive self-instruction and rumination significantly predicted well-being at the internship’s end, reducing or increasing stress from the internship. Despite the positive response of the pre-service teachers, the workbook did not have an impact. However, the results provide clear implications for the design of future interventions in this area. This study highlights the need for universities to integrate stress management into their curricula. Full article

Matching problems arise in various settings where two or more entities need to be matched—such as job applicants to positions, students to colleges, organ donors to recipients, and advertisers to ads slots in web advertising platforms. This study introduces the preference adjustment matching algorithm (PAMA), a novel matching framework that pairs elements, which conceptually represent a bipartite graph structure, based on rankings and preferences. In particular, this algorithm is applied to tutor–module assignment in academic settings, and the methodology is built on four key assumptions where each module must receive its required number of candidates, candidates can only be assigned to a module once, eligible candidates based on ranking and module capacity must be assigned, and priority is given to mutual first-preference matches with institutional policies guiding alternative strategies when needed. PAMA operates in iterative rounds, dynamically adjusting modules and tutors’ preferences while addressing capacity and eligibility constraints. The distinctive innovative element of PAMA is that it combines concepts of maximal and stable matching, pending status and deadlock resolution into a single process for matching tutors to modules to meet the specific requirements of academic institutions and their constraints. This approach achieves balanced assignments by adhering to ranking order and considering preferences on both sides (tutors and institution). PAMA was applied to a real dataset provided by the Hellenic Open University (HOU), in which 3982 tutors competed for 1906 positions within 620 modules. Its performance was tested through various scenarios and proved capable of effectively handling both single-round and multi-round assignments. PAMA effectively handles complex cases, allowing policy-based resolution of deadlocks. While it may lose maximality in such instances, it converges to stability, offering a flexible solution for matching-related problems. Full article

The integration of AI tools like ChatGPT in education has sparked debates on their benefits and limitations, particularly in subjects requiring region-specific knowledge. This study examines ChatGPT’s ability to generate accurate and contextually rich responses to assignments in Romanian history and geography, focusing on topics with limited digital representation. Using a document-based analysis, this study compared ChatGPT’s responses to local archival sources, monographs, and topographical maps, assessing coverage, accuracy, and local nuances. Findings indicate significant factual inaccuracies, including misidentified Dacian tribes, incorrect historical sources, and geographic errors such as misplaced landmarks, elevation discrepancies, and incorrect infrastructure details. ChatGPT’s reliance on widely digitized sources led to omissions of localized details, highlighting a fundamental limitation when applied to non-digitized historical and geographic topics. These results suggest that while ChatGPT can be a useful supplementary tool, its outputs require careful verification by educators to prevent misinformation. Future research should explore strategies to improve AI-generated educational content, including better integration of regional archives and AI literacy training for students and teachers. The study underscores the need for hybrid AI-human approaches in education, ensuring that AI-generated text complements rather than replaces verified academic sources. Full article

This paper explores how generative artificial intelligence (GenAI) technologies, such as ChatGPT 4o and other AI-based conversational models, can be applied to flipped learning pedagogy to achieve enhanced learning outcomes for students. By applying Bloom’s taxonomy to intentionally align educational objectives to the key phases of flipped learning, our study proposes a model for assigning learning activities to pre-class, in-class, and post-class contexts that can be enhanced by the integration of GenAI. In the pre-class phase, GenAI tools can facilitate personalised content delivery, enabling students to grasp fundamental concepts at their own pace. During class, the interactions between students, teacher, and GenAI encourage collaborative learning and real-time feedback. Post-class activities utilise GenAI to reinforce knowledge, provide instant feedback, and support continuous learning through summarisation and content generation. Furthermore, our model articulates the synergies between the three key actors: interactions between students and teachers, learning support provided by GenAI to students, and use of GenAI by teachers to enhance their teaching strategies. These human–AI interactions fundamentally reshape the flipped learning experience, making it more adaptive, engaging, and supportive of the development of 21st-century skills such as critical thinking, collaboration, communication, and creativity. Full article

Natural Language Processing (NLP) stands as a forefront of artificial intelligence research, empowering computational systems to comprehend and process human language as used in everyday contexts. Language models (LMs) underpin this field, striving to capture the intricacies of linguistic structure and semantics by assigning probabilities to sequences of words. The trend towards large language models (LLMs) has shown significant performance improvements with increasing model size. However, the deployment of LLMs on resource-limited devices such as mobile and edge devices remains a challenge. This issue is particularly pronounced in languages other than English, including Korean, where pre-trained models are relatively scarce. Addressing this gap, we introduce a novel lightweight pre-trained Korean language model that leverages knowledge distillation and low-rank factorization techniques. Our approach distills knowledge from a 432 MB (approximately 110 M parameters) teacher model into student models of substantially reduced sizes (e.g., 53 MB ≈ 14 M parameters, 35 MB ≈ 13 M parameters, 30 MB ≈ 11 M parameters, and 18 MB ≈ 4 M parameters). The smaller student models further employ low-rank factorization to minimize the parameter count within the Transformer’s feed-forward network (FFN) and embedding layer. We evaluate the efficacy of our lightweight model across six established Korean NLP tasks. Notably, our most compact model, KR-ELECTRA-Small-KD, attains over 97.387% of the teacher model’s performance despite an 8.15× reduction in size. Remarkably, on the NSMC sentiment classification benchmark, KR-ELECTRA-Small-KD surpasses the teacher model with an accuracy of 89.720%. These findings underscore the potential of our model as an efficient solution for NLP applications in resource-constrained settings. Full article

Background: Childhood obesity is a growing public-health concern in China and globally, a trend influenced by multiple factors, including poor eating behaviors and insufficient physical activity. While interactive health games have shown promise in improving children’s nutrition education and healthy eating behaviors, few have been tailored for the Chinese context. This study aimed to develop and evaluate Happy Farm, Happy Meal (HFHM), an alternate reality game (ARG) integrated into Chinese elementary students’ daily routines to enhance their nutrition knowledge and improve their eating behaviors. Methods: This pilot study employed a quasi-experimental design with two third-grade classes, which were randomly assigned to the HFHM intervention group (n = 40) or a no-game control group (n = 39). The game design was informed by a pre-intervention survey and interviews with caregivers and teachers, which identified key dietary challenges such as picky eating, slow eating, and food waste. Over a two-week period, the HFHM group engaged in food- and nutrition-focused tasks that were incorporated into their lunchtime routines. Pre- and post-intervention data were collected on nutrition knowledge, food waste, picky eating, and meal duration, with daily progress tracking in the HFHM group. Results: Compared to the control group, the HFHM group showed a significant increase in nutrition knowledge (p < 0.05), reduced food waste (p < 0.01), decreased picky eating (p < 0.01), and improved meal duration (p < 0.05). However, the small sample size and short intervention period limit generalizability. Conclusions: These findings suggest HFHM is a promising tool for improving nutrition education and dietary behaviors in Chinese children. Future research should validate these findings in a larger sample and assess long-term impacts. Full article

Flipped classrooms are a pedagogically appropriate approach that supports inclusive education by increasing in-class practise time, including active learning activities. They can support permanence by reinforcing many courses taught theoretically in universities. In this respect, flipped classrooms can become an important advantage in the training of special education teachers. In this study, we aimed to examine the association between the flipped classroom on pre-service special education teachers’ academic achievement, Individualised Education Plan (IEP) competencies and IEP quality and to determine pre-service teachers’ views on the flipped IEP course. In our research, we investigated a sample of 66 s-year pre-service teachers, 33 of whom were randomised into an experimental group, and 33 into a control group. Participants were randomly assigned to the experimental and control groups. As a result of this study, it was revealed that the flipped IEP course had a statistically significant association with the quality of the IEP prepared by the pre-service teachers, that the information they gained in the IEP course was permanent, that the subjects were more understandable, that it provided enjoyable learning opportunities based on practise, and that it increased classroom interaction. Full article

Deformation is a key physical quantity that reflects the safety status of dams. Dam deformation is influenced by multiple factors and has seasonal and periodic patterns. Due to the challenges in accurately predicting dam deformation with traditional linear models, deep learning methods have been increasingly applied in recent years. In response to the problems such as an excessively long training time, too-high model complexity, and the limited generalization ability of a large number of complex hybrid models in the current research field, we propose an improved multi-teacher distillation network for regression tasks to improve the performance of the model. The multi-teacher network is constructed using a Transformer that considers global dependencies, while the student network is constructed using Temporal Convolutional Network (TCN). To improve distillation efficiency, we draw on the concept of clustering integration to reduce the number of teacher networks and propose a loss function for regression tasks. We incorporate an adaptive weight module into the loss function and assign more weight to the teachers with more accurate prediction results. Finally, knowledge information is formed based on the differences between the teacher networks and the student network. The model is applied to a concrete-faced rockfill dam located in Guizhou province, China, and the results demonstrate that, compared to other knowledge distillation methods, this approach exhibits higher accuracy and practicality. Full article