Search Results (2,873)

Integrating social and emotional learning (SEL) into academic instruction may improve well-being and achievement. In mathematics—where anxiety and negative attitudes often hinder learning—SEL may be especially useful. This multiple case study examined how a math curriculum that explicitly embeds SEL principles shapes learning environments and teacher/student experiences. Using a multiple case study design, we conducted classroom observations, teacher interviews, and check-ins in six Grade 5–7 classrooms implementing JUMP Math, a program that centers social–emotional well-being. Three themes characterized the SEL-integrated environment: (1) Teaching Energy—steady pacing, enthusiastic delivery, and humor; (2) Learning Harmony—progressing together, peer help, and the normalization of mistakes; and (3) Emotional Stability—supportive feedback, invitations to participate, and respectful, responsive interactions. Teachers reported greater confidence and reduced math anxiety; students showed higher engagement, cooperation, and resilience in problem-solving. Findings indicate that math curricula intentionally designed with SEL can create emotionally supportive classrooms that benefit both teachers and students, while advancing academic goals. The findings contribute to understanding how academic instruction can be leveraged to develop social and emotional competence while maintaining focus on academic achievement. Full article

The urgent need to transform initial teacher education (ITE) in Australia has reached a critical juncture, as the Quality Initial Teacher Education (QITE) Review reveals concerning attrition rates with nearly 40% of ITE students sleaving within six years and approximately one in five beginning teachers exiting within their first three years. Traditional approaches to teacher preparation are failing to adequately equip educators for contemporary classrooms, particularly in developing the cultural responsiveness needed to serve Australia’s diverse student populations. This paper presents a case for reconceptualising ITE through pedagogical features that underpin dance education as a transformative vehicle for reform. In this context, dance education is defined as structured movement-based learning that integrates physical expression, cognitive development, cultural understanding, and pedagogical skills through embodied practices. Through a critical discourse analysis of recent Australian policy documents including the Teacher Education Expert Panel (TEEP) Report and Quality Initial Teacher Education (QITE) Review, alongside systematic examination of international empirical research on dance education, this study reveals how dance education’s inherent integration of physical, cognitive, social-emotional, and cultural learning uniquely addresses persistent challenges in teacher education. This article suggests that embedding dance education principles throughout ITE programs could revolutionise teacher preparation by providing embodied understanding of learning processes while developing practical teaching skills. This innovative approach holds particular promise in developing teachers who are not only technically skilled but also emotionally intelligent and culturally responsive, with implications extending beyond Australia to teacher preparation programs internationally. Full article

We aimed to explore the effects of hands-on STEM training on teachers’ attitudes toward STEM, their self-efficacy in implementing STEM methodologies, and their problem-solving skills. Additionally, we explored teachers’ professional competence in integrating STEM applications into their instructional practices and the impact of these applications on students’ 21st-century skills. The study involved 30 in-service teachers participating in a STEM training program. A nested-methods approach was adopted, combining quantitative and qualitative analyses. Pre- and post-training data were collected using Likert-type scales measuring teachers’ self-efficacy, problem-solving skills, and attitudes toward STEM. Additionally, semi-structured interviews were conducted after the training to capture teachers’ perceptions and experiences. The findings indicated significant improvements in teachers’ self-efficacy and problem-solving skills. Thematic analysis of interview data identified key factors influencing successful STEM implementation, including collaboration, technology integration, and social-emotional learning. Teachers also reported that the training positively influenced their students’ problem-solving and critical thinking skills. This study highlights the importance of professional development in enhancing teachers’ competencies for effective STEM education. The findings contribute to the literature by providing insights into teachers’ first-hand experiences and perceptions regarding the impact of STEM training on their instructional practices and student learning outcomes. Full article

Understanding students’ feelings about daily school life can be a tool for schools to enhance their learning experience and sense of belonging. Despite the abundant research on achievement and engagement, few studies jointly examine the effect of students’ over-age status considering the grade attended, gender, and school level within a multi-domain framework of student feelings. Even rarer are studies that examine how these variables interrelate to identify predictors of students’ feelings about the learning experience, the aim of this study. Then, adopting a quantitative research approach, data were collected through a 1012-participant survey to map the students’ feelings about school life. The data were analyzed using t-tests, ANOVA, and linear regression statistics to identify causes and associations with the schooling experience. The findings indicate that students who did not disclose their gender or are over-age, considering the grade attended, exhibited less-positive feelings. Additionally, positive feelings decrease along the school path, and six predictors—assessment, school climate, teacher support, emotional discomfort, relationship with peers, and grade—explain the learning experience feelings, with relevance to their interaction effect. The findings highlight the need for coordinated school interventions that promote students’ positive feelings through inclusive, student-centred, and context-sensitive practices. Full article

Cultural heritage museums, as integral components of the urban built environment and public cultural space, not only preserve historical memory but also subtly shape visitors’ psychological experiences and well-being. Yet the mechanisms linking museum environmental quality with visitor mental experiences remain insufficiently explored. Drawing on 10,684 visitor reviews collected from Dianping, Weibo, and Ctrip, this study applies text mining and semantic analysis to construct an evaluation framework of visitor behavioral preferences and psychological experiences in heritage museums. The findings show that attention to spatial remains, historical artifacts, and cultural symbols is closely associated with positive emotions such as mystery, awe, and beauty, while adverse environmental conditions such as queuing and crowding often trigger negative feelings including fatigue, disappointment, and boredom. Further analysis reveals a clear pathway linking objects, behaviors, and experiences: spatial remains evoke psychological resonance through immersive perceptions of authenticity; artifacts are primarily linked to visual pleasure and emotional comfort; and cultural symbols are transformed into cognitive gains and spiritual meaning through interpretation and learning. Cross-regional comparison highlights significant differences among museums with distinct cultural backgrounds in terms of architectural aesthetics, educational value, and emotional resonance. This study not only offers a practical framework for the refined management and spatial optimization of heritage museums, but also demonstrates that high-quality cultural environments can promote mental health and emotional restoration. The results extend the interdisciplinary framework of museum research and provide empirical evidence for environmental improvement and public health promotion in cultural heritage spaces in the digital era. Full article

Immersive virtual reality (IVR) has demonstrated significant potential in educational contexts. Nonetheless, prior IVR implementations have primarily focused on visual and auditory simulations, neglecting olfaction, which has limited immersive learning. To address this gap, we conducted an experimental study involving 64 students to examine the impact of integrating olfactory stimulus into IVR systems for fire safety training. Participants were randomly assigned to the control group (without olfactory stimulus, n = 32) or the experimental group (with olfactory stimulus, n = 32). The results indicated that the integration of olfactory stimulus significantly promoted high-arousal positive emotions, increased sense of presence, and reduced cognitive load—although it did not significantly improve learning performance. Thematic analysis further revealed that the incorporation of olfactory stimulus provided learners with an immersive learning experience. Moreover, this IVR system with olfactory stimulus had a high quality of experience. These findings have significant implications for the practice of learning in IVR and multisensory learning theory. Full article

A core challenge in multimodal emotion recognition lies in the precise capture of the inherent multimodal interactive nature of human emotions. Addressing the limitation of existing methods, which often process visual signals (facial expressions) and physiological signals (EEG, ECG, EOG, and GSR) in isolation and thus fail to exploit their complementary strengths effectively, this paper presents a new multimodal emotion recognition framework called the Gated Biological Visual Network (GBV-Net). This framework enhances emotion recognition accuracy through deep synergistic fusion of facial expressions and physiological signals. GBV-Net integrates three core modules: (1) a facial feature extractor based on a modified ConvNeXt V2 architecture incorporating lightweight Transformers, specifically designed to capture subtle spatio-temporal dynamics in facial expressions; (2) a hybrid physiological feature extractor combining 1D convolutions, Temporal Convolutional Networks (TCNs), and convolutional self-attention mechanisms, adept at modeling local patterns and long-range temporal dependencies in physiological signals; and (3) an enhanced gated attention fusion module capable of adaptively learning inter-modal weights to achieve dynamic, synergistic integration at the feature level. A thorough investigation of the publicly accessible DEAP and MAHNOB-HCI datasets reveals that GBV-Net surpasses contemporary methods. Specifically, on the DEAP dataset, the model attained classification accuracies of 95.10% for Valence and 95.65% for Arousal, with F1-scores of 95.52% and 96.35%, respectively. On MAHNOB-HCI, the accuracies achieved were 97.28% for Valence and 97.73% for Arousal, with F1-scores of 97.50% and 97.74%, respectively. These experimental findings substantiate that GBV-Net effectively captures deep-level interactive information between multimodal signals, thereby improving emotion recognition accuracy. Full article

Emotion detection significantly impacts healthcare by enabling personalized patient care and improving treatment outcomes. Single-modality emotion recognition often lacks reliability due to the complexity and subjectivity of human emotions. This study proposes a multi-modal emotion detection platform integrating visual, audio, and heart rate data using AI techniques, including convolutional neural networks and support vector machines. The system outperformed single-modality approaches, demonstrating enhanced accuracy and robustness. This improvement underscores the value of multi-modal AI in emotion detection, offering potential benefits across healthcare, education, and human–computer interaction. Full article

Integrating emotional intelligence into AI systems is essential for developing empathetic chatbots, yet deploying fully empathetic models is often constrained by business, ethical, and computational factors. We propose an innovative solution: a dedicated empathy rephrasing layer that operates downstream of a chatbot’s initial response. This layer leverages large language models (LLMs) to infuse empathy into the chatbot’s output without altering its core meaning, thereby enhancing emotional intelligence and user engagement. To implement this layer, we extend and validate the IDRE (Italian Dialogue for Empathetic Responses) dataset. We evaluated small- and medium-scale LLMs across three configurations: baseline models, models augmented via few-shot learning with IDRE exemplars, and models fine-tuned on IDRE. Performance was quantitatively assessed using the LLM-as-a-judge paradigm, leveraging custom metrics. These results were further validated through an independent human evaluation and supported by established NLP similarity metrics, ensuring a robust triangulation of findings. Results confirm that both few-shot prompting and fine-tuning with IDRE significantly enhance the models’ capacity for empathetic language generation. Applications include empathetic AI in healthcare, such as virtual assistants for patient support, and demonstrate promising generalization to other domains. All datasets, prompts, fine-tuned models, and scripts are publicly available to ensure transparency and reproducibility. Full article

Stance detection on social media is increasingly vital for understanding public opinion, mitigating misinformation, and enhancing digital trust. This study proposes a modular Multi-Task Learning (MTL) framework that jointly models stance detection and sentiment analysis to address the emotional complexity of user-generated content. The architecture integrates a RoBERTa-based shared encoder with BiCARU layers to capture both contextual semantics and sequential dependencies. Stance classification is reformulated into three parallel binary subtasks, while sentiment analysis serves as an auxiliary signal to enrich stance representations. Attention mechanisms and contrastive learning are incorporated to improve interpretability and robustness. Evaluated on the NLPCC2016 Weibo dataset, the proposed model achieves an average F1-score of 0.7886, confirming its competitive performance in emotionally nuanced classification tasks. This approach highlights the value of emotional cues in stance inference and offers a scalable, interpretable solution for secure opinion mining in dynamic online environments. Full article

We pose the question of whether life has become unlivable for the young in America amidst the current political climate, which has systematically deregulated our social structures that safeguard against oppressive and unjust practices. What leads the young to become demoralized to the point of wanting to end their lives? Drawing on several established psychosocial models for suicide, including those of Durkheim, Joiner, and Butler, we highlight how groups of youth as disparate as youth of color, LGBTQ+ youth, and young men experience unique sociopolitical stressors that contribute to increased suicidality. We argue that despite differences in their contexts, they experience shared pathways to suicide. At a time when U.S. funding cuts threaten to dismantle the progress made in recent years to address structural racism and sexism, we also make a case for the importance of mental health clinicians’ engagement in advocacy work that recognizes the sociopolitical influences on mental health and highlight universal school-based social emotional learning (USB SEL) as one beneficial intervention to target mental health outcomes across disparate youth groups. Full article

Understanding media discussions on artificial intelligence (AI) is crucial for shaping policy and addressing public concerns. The purpose of this study was to understand sentiment regarding AI in the media and to discover how the discussion of topics changed over time in technology-related media outlets. The study involved three overall steps: data curation and cleaning to obtain a high-quality, timely dataset from a list of relevant technology-news-oriented websites; sentiment analysis to understand the emotion of the articles; and Latent Dirichlet Allocation (LDA) to uncover the topics of discussion. The study curated and analyzed 22,230 articles from technology-focused media outlets between the period 2006 and July 2024, split into three time periods. We found that discussion on AI-related topics has increased significantly over time, with sentiment generally positive. However, since 2022, both negative and positive sentiment proportions within articles have risen, suggesting growing emotional polarization. The introduction of ChatGPT 3.5 in November 2022 notably influenced media narratives. Machine learning remained a dominant topic, while discussion on business and investment, as well as governance and regulation, has gained prominence in recent years. This study demonstrates the impact of technological advancements on media discourse and highlights increasing emotional polarization regarding AI coverage in recent years. Full article

This paper presents a model for reimagining science learning during the early childhood years through storybook reading. Much of the research on storybooks in early childhood has emphasized how storybooks promote knowledge acquisition in literacy, social–emotional learning, and science. This model proposes that shared science storybook reading, through interactions with adults and society, integrates these domains and encourages the development of skills critical to success in science fields such as persistence in the face of failure and growth mindset. The model is situated within two theoretical frameworks: a social interactionist framework that adult–child interactions during a shared storybook reading can advance children’s learning and an ecological systems framework, which highlights how early development occurs in informal and formal learning environments in preschool through second grade, and within the context of larger societal values surrounding science. Full article

Hybrid learning spaces may be described as physical environments enhanced by digital technologies, which enable learning scenarios involving both in-person and online participation. This article presents a hybrid learning space designed for higher education. The design of the space has been informed by Lefebvre’s design principles: (a) spatial practice enabling flexible usage scenarios, (b) representations of space conveying openness and adaptability, and (c) representational spaces supporting experiences of presence in both physical and digital form. The article describes design characteristics guiding the implementation of the hybrid learning space and explains corresponding design decisions, such as the use of a wall-sized projection. Further, the article introduces affordances and usage scenarios of the hybrid learning space developed. Moreover, an evaluation study of the hybrid learning space is conducted by means of a 360°-based virtual field trip (VFT). The VFT, led by an educator, serves as preparation for a field trip (FT) to a composting plant two weeks later. Participants of both VFT and FT (N = 11) completed a questionnaire addressing psychological constructs related to learning, including motivation, emotion, immersion, presence, and cognitive load. We report the results of the VFT alongside those of the FT as a baseline. Some notable differences, for example in social presence, suggest areas for further development of the hybrid learning space. Overall, the study characterises key features of hybrid learning spaces, identifies their contribution to high-quality teaching and provides inspirations for their further development. Full article

Learning and understanding of art are increasingly understood as dynamic processes in which emotion and cognition unfold over time. However, classroom-based evidence on how structured temporal intervals and guided prompts reshape students’ emotional experience remains limited. This study addresses these gaps by quantitatively examining changes in emotion over time in a higher education institution. Employing a comparative experimental design, third-year undergraduate art students participated in two structured courses, where emotional responses were captured using an emotion recognition approach (facial expression and self-reported text) during two sessions: initial impression and delayed impression (three days later). The findings reveal a high consistency in dominant facial expressions and substantial agreement in self-reported emotions across both settings. However, the delayed impression elicited greater emotional diversity and intensity, reflecting deeper cognitive engagement and emotional processing over time. These results reveal a longitudinal trajectory of emotion influenced by guided reflective re-view over time. Emotional dynamics extend medium theory by embedding temporal and affective dimensions into TBMA course settings. This study proposes an ethically grounded and technically feasible framework for emotion recognition that supports reflective learning rather than mere measurement. Together, these contributions redefine TBMA education as a temporal and emotional ecosystem and provide an empirical foundation for future research on how emotion fosters understanding, interest, and appreciation in higher media art education. Full article