Search Results (87)

This study investigates the potential, limitations, and pedagogical implications of applying a parallel multi-model AI evaluation workflow, using ChatGPT, DeepSeek, and Uizard, to assess student design projects in higher education. Because design assessment involves both formal criteria and subjective creative interpretation, the study first established a human expert baseline based on three independent university professors. The human inter-rater reliability was low to moderate, with a mean pairwise Spearman’s ρ of 0.36 and Cronbach’s α of 0.60 for packaging design, and ρ of 0.43 and α of 0.69 for web design. This finding is central to the study, as it shows that the human benchmark in creative design assessment is itself variable and interpretive. Against this baseline, AI–human alignment remained limited and task-dependent. For packaging design, the AI ensemble showed only a weak positive association with the human expert baseline (Spearman’s ρ = 0.30, p = 0.031), which should be interpreted cautiously given the Bonferroni-adjusted significance threshold used in the study. For web design, no significant AI–human association was observed. Qualitative analysis of AI-generated rationales identified recurring limitations, including hallucination, aesthetic shield effects, and missed context, where visually polished work was rewarded despite deeper conceptual or structural weaknesses. The findings suggest that current AI systems can provide useful formative feedback on visible formal features, but they are not reliable as autonomous grading tools for complex creative work. AI-assisted assessment is therefore best understood as a supervised formative support mechanism, while final evaluation should remain grounded in human pedagogical judgment. Full article

With an exploratory survey administered to 101 alumni who voluntarily and anonymously participated, since its inception in 1999, in the social service program at the Yunus Center, at the Mexican public Autonomous University of Baja California (UABC), five core dimensions of entrepreneurship were assessed: learning, entrepreneurial intention, skill development, inspiration and confidence, and opportunity recognition. The findings indicate that engagement with social microentrepreneurs (marginalized and impoverished) during social service served as a facility for developing entrepreneurial skills and intentions. Over 87% reported increased inspiration, motivation, and confidence, and more than 88% identified entrepreneurial opportunities through their participation. That suggests that interaction with necessity-driven microentrepreneurs as role models can create an innovative, inclusive learning environment among university students, and a possible low-cost method approach for fostering social and economic entrepreneurship, according to the UN’s sustainable development goals SDG 4 (Quality Education) and SDG 8 (Decent Work and Economic Growth). Full article

Background/Objectives: Sport participation and nutrition are increasingly recognized as key determinants of cognitive function and academic achievement in student populations. However, the biological mechanisms underpinning these associations remain only partially understood. This scoping review aimed to map and synthesize the current evidence on neuroendocrine and autonomic mechanisms linking physical activity, sport participation, and nutrition to cognitive and academic outcomes in students. Methods: A systematic search of electronic databases was performed following PRISMA-ScR guidelines. Studies involving student populations that examined physical activity, sport participation, or dietary patterns in relation to cognitive function and/or academic performance were included. Particular attention was given to studies reporting biological or physiological indicators of underlying mechanisms, including neuroendocrine, autonomic, and brain-based measures. Data were extracted and synthesized qualitatively, with studies categorized according to the type of mechanistic evidence. Results: A total of 76 studies met the inclusion criteria. The available evidence was more extensive for physical activity, sport participation, and fitness-related exposures than for nutrition-related variables or integrated lifestyle models. Cognitive outcomes, particularly executive function, attention, working memory, and memory performance, were assessed more frequently and showed more consistent associations with lifestyle behaviors than academic outcomes, which were less commonly and more heterogeneously evaluated. Mechanistic evidence was unevenly distributed: only a limited subset of studies included direct biological or psychophysiological measures, mainly neuroimaging, brain-derived neurotrophic factors, cortisol-related indices, or heart rate variability. In contrast, inflammatory, metabolic, and gut microbiota-related mechanisms were mostly discussed at a conceptual or indirect level. Overall, the findings indicate a broad associative literature but a relatively small body of studies directly testing biological pathways linking physical activity, nutrition, cognition, and academic performance. Conclusions: Current evidence indicates potential associations between sport participation, nutrition, cognitive outcomes, and multiple biological pathways. However, the scoping nature of the review, the predominance of observational designs, and the limited use of direct mechanistic assessments prevent firm causal conclusions. Future research should prioritize longitudinal and intervention studies integrating behavioral, nutritional, cognitive, academic, and biological measures within the same design. Full article

As autonomous driving technology advances, the deployment of autonomous vehicles in urban environments is rapidly increasing. Lens flare—an often overlooked optical artifact in object detection research—can lead to increased false positives or missed detections, particularly in the challenging conditions inherent to autonomous driving. Current mitigation methods are often ill-suited for real-time implementation. This work proposes a solution to alleviate the adverse effects of lens flare by utilizing a lightweight lens flare perception network, eliminating the need for additional hardware or complex image pre-processing. Specifically, we propose a reference-free model utilizing a ResNet18 backbone integrated with a lightweight Multi-Layer Perceptron (MLP) to extract and leverage lens flare information. This model is developed via a teacher–student framework, which was distilled from an end-to-end reference-based model optimized using the Learned Perceptual Image Patch Similarity (LPIPS) metric. Our experiments demonstrate that incorporating lens flare information significantly enhances the performance of the baseline object detection network, outperforming previous mitigation methods by a substantial margin. The proposed method can be seamlessly integrated into existing object detectors and requires only an efficient training process, facilitating its deployment in practical autonomous driving tasks. Full article

Weed identification and quantification are processes that are usually manual, subjective, and error-prone. Weeds compete with crops for nutrients, minerals, physical space, sunlight, and water. Thus, weed identification is a crucial component of precision agriculture for autonomous removal and site-specific treatments, efficient weed control, and sustainability. Convolutional Neural Networks (CNNs) are very common in weed identification. This work implemented CNN models for semantic segmentation based on the U-Net architecture for automatically segmenting and quantifying weeds in potato crops using RGB images acquired by a drone at 9–10 m height, flying at 1 m/s. Remote sensing images are affected by factors that degrade image quality and the model’s accuracy. Five U-Net variants were evaluated: the original U-Net, Residual U-Net, Double U-Net, Modified U-Net, and AU-Net. The models were trained using the TensorFlow/Keras frameworks on Google Colab Pro+, following the Knowledge Discovery in Databases (KDD) methodology for image analysis. Each model was trained using a diverse custom dataset in uncontrolled environments, considering six classes: background, Broadleaf dock (Rumex obtusifolius), Dandelion (Taraxacum officinale), Kikuyu grass (Cenchrus clandestinum), other weed species, and the crop potato (Solanum tuberosum L.). The models’ segmentation was widely assessed using Mean Dice Coefficient, Mean IoU, and Dice Loss metrics. The results showed that the Residual U-Net model performed the best in multi-class segmentation, achieving a Mean IoU of 0.8021, a performance comparable to or superior to that reported by other authors. Additionally, a Student’s t-test was applied to complement the data analysis, suggesting that the model is reliable for weed quantification. Full article

In the academic year 2022/2023, an orientation course addressed to high school students was proposed at Politecnico di Milano. The course was conducted using active methodologies referring to the Problem-Based Learning pedagogical framework. Most of the time was dedicated to an open-ended style physics laboratory in which students worked in small groups exploring different areas of physics. The main aim of the course was to foster science self-efficacy and motivation, in order to enable students to prepare themselves for passing the test required to enrol in any engineering programme at Politecnico di Milano University. To investigate the effectiveness of the course, a statistical analysis of students’ attendance and students’ results on the test was performed. The results of the analysis show that students who attended the course had a slightly better improvement in their test scores compared to those who did not attend. The impact of the course seems to be more effective for female students. The results confirm the validity of active and open-ended activities to increase scholastic performance and to enable students in autonomous preparation. Using these strategies in an orientation course can help students make more informed choices about the university pathway best suited to them, thereby reducing issues related to student dropout. Full article

This instrumental case study investigates how learning support assistants (LSAs) understand their role and implement pedagogical strategies when working with autistic students in Dubai’s private education sector. Despite robust inclusive education policies, schools frequently emphasise academic achievement metrics, leading to reliance on LSAs for students experiencing learning or behavioural challenges. The research analyses LSA scaffolding practices through three theoretical lenses: repair, heuristic, and support functions. Through observations and interviews with six LSAs working in individualised and small-group contexts at a premier British-curriculum institution, the study identifies several patterns in the support practices of the LSAs. Results demonstrate that LSAs consistently apply intensive support without progressively reducing assistance or building student independence. Participant interviews revealed widespread assumptions about autistic learners’ ability to manage challenging academic work, directly limiting opportunities for growth. Pedagogical choices favoured managing student behaviour through external reward mechanisms rather than cultivating genuine learning engagement or developing autonomous problem-solving abilities. This research exposes disconnects between policy intentions, the scaffolding theory and classroom realities for learners with autism and their supporting educators. Full article

The rapid adoption of generative artificial intelligence (AI) in higher education has intensified a pedagogical dilemma: while AI tools can increase immediate classroom engagement, they do not necessarily foster the self-regulated learning (SRL) capacities required for ethical and reflective professional practice, particularly in human-service fields. In this two-time-point, pre-post cohort-level (repeated cross-sectional) evaluation, we examined a six-week AI-integrated curriculum incorporating explicit SRL scaffolding among social work undergraduates at a Taiwanese university (pre-test N = 37; post-test N = 35). Because the surveys were administered anonymously and individual responses could not be linked across time, pre-post comparisons were conducted at the cohort level using independent samples. The participating students completed the AI-Enhanced Learning Attitude Scale (AILAS); this is a 30-item instrument grounded in the Technology Acceptance Model, Attitude Theory and SRL frameworks, assessing six dimensions of AI-related learning attitudes. Prior pilot evidence suggested an engagement regulation gap, characterized by relatively strong learning process engagement but weaker learning planning and learning habits. Accordingly, the curriculum incorporated weekly goal-setting activities, structured reflection tasks, peer accountability mechanisms, explicit instructor modeling of SRL strategies and simple progress tracking tools. The conducted psychometric analyses demonstrated excellent internal consistency for the total scale at the post-test stage (Cronbach’s α = 0.95). The independent-samples t-tests indicated that, at the post-test stage, the cohorts reported higher mean scores across most dimensions, with the largest cohort-level differences in Learning Habits (Cohen’s d = 0.75, p = 0.003) and Learning Process (Cohen’s d = 0.79, p = 0.002). After Bonferroni adjustment, improvements in the Learning Desire, Learning Habits and Learning Process dimensions and the Overall Attitude scores remained statistically robust. In contrast, the Learning Planning dimension demonstrated only marginal improvement (d = 0.46, p = 0.064), suggesting that higher-order planning skills may require longer or more sustained instructional support. No statistically significant gender differences were identified at the post-test stage. Taken together, the findings presented in this study offer preliminary, design-consistent evidence that SRL-oriented pedagogical scaffolding, rather than AI technology itself, may help narrow the engagement regulation gap, while the consolidation of autonomous planning capacities remains an ongoing instructional challenge. Full article

Blended learning is widely adopted in higher education, yet little is known about how students experience its motivational and instructional features. In this study, we examined undergraduate students’ experiences regarding blended learning by integrating Herzberg’s two-factor theory with the TPACK framework. Semi-structured interviews were conducted with 24 undergraduates at a large Vietnamese university. A theory-informed qualitative content analysis approach was used to identify codes, categories, and themes. These were then mapped onto the pedagogical content knowledge (PCK), technological content knowledge (TCK), and technological pedagogical knowledge (TPK) intersections of the TPACK framework. The findings showed that hygiene factors included unengaging teaching practices, inadequate digital infrastructure, and limited online interaction. These factors often produced frustration and reduced engagement. Motivator factors included active and relevant pedagogical strategies, engaging and accessible digital resources, and technology-facilitated autonomous, expressive, and creative learning work. These factors encouraged deeper learning and stronger motivation. It is concluded that blended learning design must address both hygiene and motivator factors to improve student engagement. Integrating these factors with the TPACK intersections offers a practical model for improved course structures, enhanced digital resources, and the design of more interactive technology-supported pedagogy. The findings provide actionable implications for higher education institutions seeking to improve the quality of blended learning. Full article

Perceived social support is considered essential for enhancing the inner academic motivational resources of students, in particular motivational self-regulation. We aimed to examine the possible associative mediation of motivational regulation strategies in the relationship between perceived support from family and teachers and academic achievement. A convenience sample of secondary education students was recruited. The students were asked to complete self-report questionnaires on perceived social support and motivational self-regulation strategies, and their academic grades were also recorded. Mediation regression analysis was used to test the mediation model proposed in the study. Three motivational regulation strategies mediated the relationship between perceived support and academic achievement: work-avoidance self-talk, self-efficacy enhancement, and enhancement of situational interest. Different support provisions were found to have cumulative positive and negative associations with the strategies. The findings suggest that perceived social support is associated with more autonomous forms of motivational regulation and lower levels of work-avoidance among students. Full article

Engineering programmes have been giving more weight to experiential learning, largely because many students still find it difficult to see how classroom theory connects to the work that engineers handle on the ground. With this in mind, a robotics-centred Project-based Learning (PBL) module was introduced to first-year general engineering students as part of the faculty’s engineering spine. The module asks students to design, build, and program small autonomous robots capable of navigating and competing in a set arena. Even a simple task of this kind draws together multiple strands of engineering. Students shift between sketching mechanical layouts, wiring basic circuits, writing code, testing prototypes, and negotiating the usual challenges that arise when several people share responsibility for the same piece of hardware. To explore how students learned through the module, a mixed-methods evaluation was carried out using survey responses alongside reflective pieces written by the students themselves. Certain patterns appeared repeatedly. Many students felt that their technical skills had grown, particularly in breaking down a messy problem into smaller, more workable components. Teamwork also surfaced as a prominent theme. Groups often had to sort out issues such as a robot veering off course due to a misaligned sensor or a block of code producing unpredictable behaviour. These issues were undoubtedly challenging for the students, but they also had a certain pedagogical flavour, with many students describing them as a source of frustration as well as a learning opportunity. Later iterations of the module may benefit from more targeted support at key stages. Despite the many challenges, robotics has been shown to be an attractive way for students to step into engineering practice. The project helped them build technical capability, but it also encouraged habits that matter just as much in real work, such as planning, communicating clearly, and returning to a problem until it behaves as expected. Taken together, the experience offers useful guidance for curriculum designers seeking to create early learning environments that feel authentic and manageable and for motivating students who are just beginning their engineering journey. Full article

This study examines the motivational and sustainability effects of an Anki-based, individualized project-based learning (PBL) model in an elementary Hindi language course. Conventional PBL approaches in language education typically rely on collaborative, production-focused tasks that can be demanding for novice learners and usually conclude when the final project is submitted, leaving little structured support for continued practice. In this study, script, vocabulary, expression, sentence patterns, and pronunciation are not treated as background work but defined as the core pedagogical problem. Over the semester, each learner builds and refines a personalized Anki deck—a multimedia flashcard system based on spaced repetition—designed to support Devanagari word and sentence recognition, pronunciation practice, listening comprehension, and vocabulary retention. Each student constructed an individual deck aligned with course content, selecting vocabulary items, creating example sentences, and developing personalized memory cues that matched their learning pace and needs. Motivation was measured with a modified Instructional Materials Motivation Survey (IMMS) using only positively worded items to enhance reliability. Results showed consistently high scores across all ARCS domains, particularly for Confidence (M = 3.86) and Satisfaction (M = 3.93). Female students reported higher average scores, but gender showed no association with motivational grouping. Strong correlations among ARCS dimensions indicated consistent engagement across motivational components. Cluster analysis identified two groups of learners: highly motivated learners who treated deck creation as an ongoing learning resource, and less motivated learners who still maintained scores above the neutral midpoint—engaged enough to manage typical beginner challenges. The findings suggest that Anki-based PBL can make project-based learning workable at the novice level. By positioning deck creation as both the problem students solve and the tool they build, the model integrates continuous, self-paced practice into the project structure rather than treating it as a one-time deliverable. This design responds to a familiar gap in beginner language instruction: what happens when formal scaffolding ends. Unlike conventional PBL, which concludes with project submission, this approach creates a resource learners can use independently over time, embedding ongoing vocabulary retention and autonomous practice into the learning experience itself. Full article

The increasing adoption of video-based instruction and digital assessment in higher education has reshaped how students interact with learning materials. However, it also introduces cognitive and behavioral biases that challenge the accuracy of self-perceived learning. This study aims to bridge the gap between perceived and actual learning by investigating how illusion learning—an overestimation of understanding driven by the fluency of instructional media and autonomous study behaviors—affects cognitive performance in university mathematics. Specifically, it examines how students’ performance evolves across Bloom’s cognitive domains (Understanding, Application, and Analysis) from midterm to final assessments. This paper presents a data-driven investigation that combines the theoretical framework of illusion learning, the tendency to overestimate understanding based on the fluency of instructional media, with empirical evidence drawn from a structured and anonymized dataset of 294 undergraduate students enrolled in a Linear Algebra course. The dataset records midterm and final exam scores across three cognitive domains (Understanding, Application, and Analysis) aligned with Bloom’s taxonomy. Through paired-sample testing, descriptive analytics, and visual inspection, the study identifies significant improvement in analytical reasoning, moderate progress in application, and persistent overconfidence in self-assessment. These results suggest that while students develop higher-order problem-solving skills, a cognitive gap remains between perceived and actual mastery. Beyond contributing to the theoretical understanding of metacognitive illusion, this paper provides a reproducible dataset and analysis framework that can inform future work in learning analytics, educational psychology, and behavioral modeling in higher education. Full article

Image restoration tasks such as deraining, deblurring, and dehazing are crucial for enhancing the environmental perception of autonomous vehicles and traffic systems, particularly for tasks like vehicle detection, pedestrian detection and lane line identification. While transformer-based models excel in these tasks, their prohibitive computational complexity hinders real-world deployment on resource-constrained platforms. To bridge this gap, this paper introduces a novel Soft Knowledge Distillation (SKD) framework, designed specifically for creating highly efficient yet powerful image restoration models. Our core innovation is twofold: first, we propose a Multi-dimensional Cross-Net Attention(MCA) mechanism that allows a compact student model to learn comprehensive attention relationships from a large teacher model across both spatial and channel dimensions, capturing fine-grained details essential for high-quality restoration. Second, we pioneer the use of a contrastive learning loss at the reconstruction level, treating the teacher’s outputs as positives and the degraded inputs as negatives, which significantly elevates the student’s reconstruction quality. Extensive experiments demonstrate that our method achieves a superior trade-off between performance and efficiency, notably enhancing downstream tasks like object detection. The primary contributions of this work lie in delivering a practical and compelling solution for real-time perceptual enhancement in autonomous systems, pushing the boundaries of efficient model design. Full article

Background: Psychophysical well-being, understood as the integrated balance between physical and psychological health, is essential for both personal quality of life and academic performance. Among emerging strategies to support emotional balance and cognitive functioning, active breaks, brief physical activity sessions during study or work, are gaining recognition for their effectiveness. This pilot study explored the impact of active breaks on psychological, cognitive, and physiological variables in a sample of business students, aiming to evaluate their role in enhancing resilience, decision-making, well-being, and autonomic regulation. Methods: An experimental design was used, with students divided into two groups: the experimental group engaged in daily active breaks for 12 weeks, while the control group maintained their regular routines. Psychometric assessments (CD-RISC, DMC Test, PSS, and Stroop Test) and physiological measures (HRV and HRR) were administered before and after the intervention. Results: The findings showed significant improvements in psychological resilience, decision-making ability, and psychophysical well-being in the experimental group. Cognitive performance also improved, as indicated by better Stroop Test scores. Physiologically, increases in heart rate variability (HRV) and heart rate recovery (HRR) suggested enhanced autonomic balance and stress regulation. Conclusions: Active breaks offer a simple and effective strategy to promote students’ holistic well-being—encompassing both psychological and cognitive dimensions—thereby preparing future professionals to manage stress and maintain performance in high-demand environments. Full article