Search Results (3,767)

This study examined the relationships among university students’ social-emotional learning (SEL) skills, emotional intelligence, emotion regulation, and learning motivation, with particular attention to a proposed serial indirect relationship through emotional intelligence and emotion regulation. A cross-sectional correlational design was employed with 372 students from the Faculties of Education and Arts and Sciences at a university in Türkiye, selected through stratified random cluster sampling. Data were analyzed using observed-variable path analysis in AMOS; the specific serial indirect effect in the original theoretical model was additionally estimated with PROCESS using bootstrap confidence intervals. SEL skills were positively related to learning motivation and emotional intelligence, emotional intelligence was positively related to emotion regulation, and emotion regulation was positively related to learning motivation. However, the direct relationships between SEL skills and emotion regulation and between emotional intelligence and learning motivation were not statistically significant. In the original theoretical model, the specific serial indirect relationship from SEL skills to learning motivation through emotional intelligence and emotion regulation was small and not statistically significant (β = 0.036, 95% CI [−0.0003, 0.0750]); therefore, the hypothesized serial indirect relationship was not supported. A post hoc parsimonious model excluding the two nonsignificant direct paths revealed a small but statistically significant serial indirect relationship (β = 0.043, 95% CI [0.005, 0.082]). This exploratory pattern should be interpreted cautiously and requires replication through longitudinal or experimental research. The findings may also inform curriculum developers by identifying social-emotional dimensions that warrant consideration in needs analysis and future curriculum-design and intervention studies in higher education, particularly in teacher education. Full article

Accurate building segmentation from high-resolution unmanned aerial vehicle (UAV) imagery is essential for urban mapping, three-dimensional modeling, and related remote sensing applications. However, constructing precise pixel-level annotations for such imagery is labor-intensive, whereas unlabeled imagery from new survey regions can be acquired relatively easily. This study presents a label-efficient semi-supervised framework that jointly utilizes limited labeled source-region imagery and abundant unlabeled target-region imagery without requiring target-region annotations for training. A frozen DINOv2 encoder provides transferable visual representations, and a Dense Prediction Transformer (DPT) decoder reconstructs multi-level features for dense prediction. An exponential moving average (EMA)-based teacher–student strategy incorporates unlabeled target imagery through confidence-controlled pseudo-label learning. Geometry-preserving photometric augmentation and feature-level perturbation improve consistency learning while avoiding artificial disruption of building footprints. Boundary supervision is derived only from labeled masks to enhance building geometry without propagating uncertain pseudo-boundaries. The proposed framework achieved an intersection over union (IoU) of 0.9197 and Boundary IoU of 0.4578 on the Wonju test set, and an IoU of 0.8921 and Boundary IoU of 0.4455 on the Seoul test set. These results demonstrate effective target-region building segmentation under limited annotation conditions. Full article

Artificial Intelligence (AI)-based educational systems increasingly support personalised learning, adaptive feedback, and learning analytics. However, these capabilities are often addressed separately, with comparatively less attention paid to their integration into interpretable, teacher-facing decision-support frameworks. This study presents the e-Teacher Assistant, a human-centred educational framework that integrates xAPI-style Learning Record Store (LRS) analytics, dynamic learner modelling, Sugeno-type fuzzy inference using interpretable IF–THEN rules, adaptive learning support, and a dual Student Model–Teacher Model architecture. The framework was evaluated during a three-month authentic deployment in a vocational education Computer Networks course involving 117 learners and four educators. Learner evaluation combined a structured questionnaire, open-ended responses, and LRS-based behavioural analytics, while educators evaluated the Teacher Model through a questionnaire and qualitative responses. Learners reported predominantly positive perceptions of the system, including ease of use (92.3%), usefulness of feedback (95.7%), helpful interaction with the AI Assistant (94.9%), support for independent learning (88.0%), and support for educators through learning analytics (87.2–88.0%). Fairness and objectivity were also evaluated positively by 86.3% of learners. All four educators evaluated the Teacher Model positively, and all strongly agreed on its overall usefulness and its support for progress monitoring. Prior familiarity with digital learning tools was significantly associated with evaluations across all seven questionnaire dimensions. LRS analytics further documented learner engagement, repeated assessment activity, and contextual AI use; AI-assisted examination support was recorded in 64.42% of learner–chapter records. Overall, the findings provide initial empirical support for the feasibility of integrating interpretable learner modelling, adaptive AI-assisted support, LRS-based learning analytics, and teacher-facing decision support in an authentic vocational education setting while preserving educator oversight and pedagogical responsibility. Full article

Developing pre-service mathematics teachers’ ability to understand students’ mathematical thinking and anticipate students’ difficulties remains a challenge in teacher education. This exploratory mixed-methods study involved 21 pre-service mathematics teachers in a six-week AI-supported mathematical task design module. The study combined pre–post assessment, fuzzy-set Qualitative Comparative Analysis (fsQCA), and qualitative analysis of participants’ AI interactions and reflections. The key findings were as follows: (1) Participants showed significantly higher post-test scores in overall diagnostic thinking and in perception, interpretation, and judgement than at pre-test. (2) The configurational analysis identified a comparatively stable pattern associated with high post-intervention diagnostic thinking, combining lower initial diagnostic thinking with high AI acceptance and high prompting knowledge density. (3) Qualitative evidence further showed that AI served as an interactive resource for exploring mathematical and pedagogical considerations, evaluating generated suggestions, and revising task designs. Exploratory comparisons suggested that AI use could range from broader knowledge-oriented support to more selective verification and refinement. The findings highlight the heterogeneous nature of AI-supported professional learning and underscore the importance of critical evaluation and professional agency in teacher–AI collaboration. Full article

The rapid emergence of generative artificial intelligence (AI) has intensified concerns about academic integrity, student cheating, and the future of assessment in STEM education. This paper argues that AI has not created a STEM assessment crisis; rather, it has exposed longstanding weaknesses that reward answer production, procedural fluency, and information reproduction rather than meaningful understanding, transfer, and application of knowledge. To operationalize this approach, the paper proposes a STEM-focused framework that distinguishes evidence of the disciplinary outcome, the learner’s reasoning process, the critical use and evaluation of AI, and individual understanding. Drawing on educational research and examples from STEM contexts, the paper contends that the central question is not how to prevent students from using AI, but what knowledge, skills, and disciplinary practices educators genuinely value and seek to assess. The paper proposes that AI functions as a litmus test for revealing assessment tasks that can be completed successfully without demonstrating disciplinary understanding. In response, it advocates a shift toward authentic assessment that emphasizes reasoning, creativity, communication, judgment, and the meaningful application of knowledge. Five examples are discussed: laboratory reports, problem solving, design challenges from the UBC Physics Olympics, scientific modelling and data interpretation, and STEM teacher education. Together, these examples illustrate how AI can support learning while authentic assessment focuses on students’ abilities to explain, justify, apply, evaluate, and defend their understanding in complex, meaningful contexts. Full article

Contemporary education increasingly emphasises the development of Social and Emotional Learning (SEL) competencies alongside academic achievement, recognising their importance for students’ well-being, interpersonal relationships, and lifelong success. Project-Based Learning (PBL) is frequently presented as a pedagogical approach capable of fostering these competencies through collaboration, active engagement, and authentic learning experiences. However, empirical evidence regarding the extent to which these socio-emotional benefits emerge through participation in project work remains inconclusive. This study examined changes in students’ self-perceived socio-emotional competencies following participation in PBL intervention in secondary education. A one-group pre-test–post-test pre-experimental design was employed with 38 students aged 12–14 from a public secondary school in Andalusia, Spain. Students participated in four interdisciplinary PBL projects, each comprising approximately 10–12 sessions and implemented across the first two terms of the 2024–2025 academic year. Self-perceived SEL competencies were assessed using the 30-item Social and Emotional Learning Scale, and data were analysed using Wilcoxon signed-rank tests, Mann–Whitney U tests and Spearman’s rank correlations. No statistically significant pre-test–post-test differences were detected in self-awareness, social awareness, relationship skills, or responsible decision making. Self-control showed a statistically significant decrease in the individual Wilcoxon analysis, with a moderate effect size. No statistically significant gender differences were detected in the magnitude of change. The five competencies were positively associated at both measurement points, although these analyses did not test whether the correlational pattern remained unchanged over time. Rather than questioning the socio-emotional potential of PBL, these findings challenge assumptions regarding the automatic emergence of measurable SEL gains through project participation alone. They highlight the need for further controlled research into the pedagogical conditions under which PBL may support socio-emotional development, including intentional SEL integration, teacher preparation, structured collaborative scaffolding, and performance-based assessment. Full article

Terrain-aware training can support locomotion without requiring terrain-height input at deployment. We present a quadrupedal control framework that combines privileged teacher–student learning with a shared gait-structured sensor-consistency central pattern generator (GSC-CPG). A recurrent student uses proprioception, contact, commands, action history, and rhythmic state to produce 12-dimensional modulation of the controller. Teacher and student act through the same phase and foot-target pathways. We evaluated the framework in Go2 simulations with disturbances applied to actor observations and direct simulator feedback retained downstream. Across three matched policy pairs, each with a shared behavior-cloned initialization, continued teacher guidance reduced the pooled rough-terrain failure rate from 1.479 to 1.097 events per robot-minute. Tracking, body tilt, forward speed, and action saturation also improved, while action variation increased. These locomotion benefits extended to a held-out roughness amplitude. On an unseen $5°$ uphill slope, guided policies had no physical falls over 384 robot-minutes, compared with nine for guidance-off policies. Component tests showed selective, reversible phase responses and a support-related speed–stability trade-off. The results support a shared rhythmic interface for transferring terrain-aware supervision to terrain-height-blind locomotion under the tested simulation conditions. Full article

The use of technology is a central component of initial teacher education; yet how preservice teachers use technological resources during teaching internship remains poorly understood, particularly when assessed through actual deployment frequencies rather than self-perceived competence. This study aimed to analyze material technology use among student teachers (N = 705) at a Spanish university during teaching internship, to examine differences by personal, academic and contextual variables and to identify technology use profiles. A quantitative, non-experimental, cross-sectional design was employed. Exploratory factor analysis, cross-validated through confirmatory factor analysis in an independent subsample, revealed four dimensions of technology use: mobile, emerging and innovative technology; printing, digitization and storage equipment; audiovisual equipment and connectivity; and interactive presentation technologies. Multivariate analyses of variance, multiple linear regression and cluster analysis were conducted. Results showed differentiated patterns of technology use, with greater reliance on conventional tools and more limited adoption of mobile and emerging technologies. Among academic variables, year of study showed the most consistent associations with technology use dimensions, while educational stage was significantly related to the use of specific resources. Cluster analysis identified differentiated levels of technology use (low, moderate and high). The high-use profile was concentrated among fourth-year Primary Education students completing teaching internship in upper primary grades, a pattern that should be interpreted alongside the longer duration and greater autonomy of the final-year practicum. This study offers a more granular account of technology infusion than other competence-based approaches due to combined variable and person-centered analyses of technology use during real school-based internships. These findings indicate that technology infusion during teaching internship is shaped by both training and contextual factors, underscoring the need to strengthen technology-related learning opportunities in teaching internship and to foster closer university–educational centers coordination to support effective technology integration in teaching and learning. Full article

School attendance problems (SAPs) are a widespread phenomenon with significant academic, psychological, social, and relational consequences. These issues are particularly prevalent among adolescents worldwide and require careful examination of the individual and contextual factors associated with their onset and persistence to develop effective prevention and intervention strategies. Grounded in an ecological and systemic framework of school absenteeism, this longitudinal study examined the reciprocal associations between SAPs, learned helplessness, mastery orientation, academic procrastination, perceived competence (general, academic, relational, leisure), and school climate (teacher support, peer connection, school connection, affirming diversity, clarity of rules, and reporting and seeking help) in a sample of 523 high school students (Mage at T1 = 15.64, SD = 0.97), using a cross-lagged panel model. Results indicate temporal stability of SAPs and the examined variables over one school year. Notably, alongside the longitudinal associations between SAPs and different individual and contextual variables, a significant bidirectional relationship emerges between learned helplessness and SAPs, suggesting that feelings of helplessness are linked to greater school disengagement and increased absences. This finding confirms the importance of emotional, motivational, and cognitive vulnerabilities in shaping school participation trajectories. The results emphasize the need for school-based prevention and intervention strategies targeting these factors. Accordingly, guidelines are proposed for educational interventions addressing SAPs. Full article

Play and toys are central resources for learning; however, their integration into schooling is typically traversed by curricular, evaluative, disciplinary, and institutional tensions, particularly in resource-limited contexts. This study examined the meanings, tensions, and re-significations that shape the lived experience of in-service teachers, pre-service teacher education students, and university teacher educators when mediating instruction with play and toys. A phenomenological–hermeneutic study was conducted in Imbabura, Ecuador, with 61 participants: 27 undergraduate education students, 26 school teachers, and 8 university faculty members. In-depth phenomenological interviews were administered, and the corpus was organized and analyzed using Atlas.ti to support the interpretive traceability of the process. The phenomenological–hermeneutic interpretation yielded five nuclei of meaning: embodied ludic heritage, material scarcity narrated as a source of invention, playing under pressure as a structural condition of the classroom as lived by participants, transformation of the didactic gaze, and the teacher’s symbolic horizon. Within this corpus, pressure emerges not as external to play but as a condition participants describe as constitutive of its school-based practice. The study concludes that, in the situated experience analyzed here, ludic mediation appears as a practice that demands professional judgment, contextual sensitivity, and institutional recognition. Full article

Professional placement is a central component of early childhood pre-service teachers’ professional learning and identity formation; however, it is also a context in which a sense of belonging is unevenly experienced. This study explores how early childhood pre-service teachers experience, construct, and perceive belonging during professional placements, with particular attention to identity, prior experiences, institutional contexts, mentorship, perceptions of support and structural inequalities. Drawing on a qualitative multi-case study design, the study utilises student reflections, field notes, and semi-structured interviews to examine how belonging is constructed, disrupted, or withheld within professional placement settings. The data were thematically analysed using a reflexive thematic analysis approach. The findings demonstrate that belonging is relational, dynamic, and contextually constructed through everyday interactions in placement settings. Pre-service teachers experienced a sense of belonging when they felt valued, welcomed, included, trusted, and meaningfully engaged in practice. In contrast, exclusionary practices and inconsistent relational support undermined their confidence and professional identity formation. Mentorship emerged as a critical factor in professional belonging, being and becoming. Affirming and scaffolded mentoring fosters confidence, agency, teacher efficacy, and a sustained commitment to the profession. Conversely, deficit-oriented mentoring constrained participation and professional growth by limiting opportunities for engagement, feedback, and identity development. Professional identity formation was thus relational, shaped through participation and engagement within practice contexts. Institutional inequalities further mediated access to belonging, although strengths-based mentoring approaches helped to mitigate these challenges. This study adapts the Early Years Learning Framework concepts of “belonging, being, and becoming” as a conceptual framework for understanding pre-service teacher’s professional placement experiences. It positions professional placement as a relational arena for pre-service teachers’ identity formation and professional growth. It offers a lens for advancing more equitable and inclusive initial teacher education (ITE) practices in Australia and beyond. Full article

In recent years, a lot of digital platforms offer a variety of word games and the number of educators who include such games in classes has grown. But such games support an increasing trend of digital technology usage which implicates negatively on human health. Regarding this, and keeping the benefits of word games for the learning process, the current research proposes a word puzzle generator which produces word games that are intended for printing, so the students should solve them on paper. The discussed generator is implemented as an application with a graphical user interface on C# to make teachers work easy for word game preparation. It was applied in practical classes of engineering students, and a survey about their experience regarding printed puzzle games was conducted. The results of the survey indicate that the usage of word games helps students to remember terms related to the specific course but also, as a gamification element, they motivate active work in classes. Full article

Digital rights and responsibilities education is a core component of digital citizenship in primary schools, yet existing approaches often prioritize rule transmission over behavioral internalization. This study examined the effects of integrating Digital Game-Based Learning (DGBL) into the final lesson of a digital rights and responsibilities curriculum. A quasi-experimental study was conducted with 88 fifth-grade students during a four-lesson curriculum delivered over four weeks. Both groups received the same teacher-led instruction during the first three lessons. In the final lesson, Cyber Judge was incorporated into instruction for the experimental group (EG) as a game-supported learning activity, while the control group (CG) received conventional instruction on the same content. Data were analyzed using ANCOVA and t-tests, with Holm–Bonferroni adjustments applied for multiple comparisons. The EG showed higher adjusted post-test digital rights and responsibilities literacy, with significant differences in Rights Awareness and Responsibilities Behavior, and higher overall cognitive absorption, particularly in Temporal Separation. No significant between-group differences were found in learning anxiety. These findings highlight the potential of DGBL to connect ethical knowledge with situated decision-making while underscoring the importance of considering learners’ affective responses in the design of game-supported instruction. Full article

Compact deep models are attractive for medical image classification, but conventional knowledge distillation mainly transfers class-level predictions and may not adequately preserve lesion-relevant spatial cues. To address this limitation, we propose LaHKD, a lesion-aware hierarchical knowledge distillation framework for parameter-efficient medical image classification. LaHKD enables a lightweight student to learn hierarchical semantic representations together with lesion-focused guidance from a stronger teacher. We evaluate LaHKD on HAM10000 dermoscopic lesion classification and a brain tumor MRI classification benchmark. Across both datasets, LaHKD improves compact-student classification performance, with the clearest lesion-focused spatial benefits observed on HAM10000, where lesion morphology is central to diagnosis and direct lesion supervision is available. On the magnetic resonance imaging (MRI) benchmark, localization analysis is limited to an auxiliary recovered-mask subset and is therefore interpreted as exploratory; under this setting, consistent localization gains are not observed. Overall, LaHKD provides an effective framework for compact medical image classification, with spatial benefits most clearly supported in tasks with reliable lesion supervision. Full article

Hyperspectral image (HSI) classification is critical for remote sensing but faces challenges in balancing accuracy and inference efficiency. Existing graph-based knowledge distillation (KD) methods are confined to single geometric spaces, ignoring the hierarchical semantics of land-cover categories. Cross-geometry distillation compresses multiple geometries into a single Euclidean student, forcing one geometry to collapse into the other. In this paper, we propose Dual-Space Knowledge Distillation (DSKD), a novel dual-student dual-space KD framework integrating Euclidean (GCN) and Hyperbolic (HGCN) teachers to jointly train a native MLP student and a native HNN student. With cross-geometric feature bridging (CGFB) and output distribution cohesion (ODC), the two students mutually learn each other’s complementary geometry, so DSKD captures complementary spatial-spectral and hierarchical features while enabling graph-free inference without message passing. Extensive experiments on four HSI datasets and four general graph benchmarks demonstrate that DSKD outperforms single-space distillation and single-student cross-geometry baselines in most settings, confirming its effectiveness and generalization capability across diverse graph-structured data. Full article