Search Results (355)

Systems thinking (ST) is a foundational cognitive skillset to advance sustainability education but has not been well examined for learners prior to higher education. This case study research in rural middle schools in the Midwestern U.S. examines systems thinking outcomes of a place-based energy literacy unit focused on energy-efficient building design. The unit employs the science of energy-efficient, green buildings to illuminate the ways in which energy flows between natural and built environments. The unit emphasized electrical, light, and thermal energy systems and the ways these systems interact to create functional and energy-efficient buildings. This study focuses on three case study classrooms where students across schools (n = 89 students) created systems models as part of pre- and post-unit tests (n = 162 models). The unit tests consisted of student drawings, annotations, and writings, culminating into student-developed systems models. Growth from pre- to post-test was observed in both the identification of system elements and the linkages between elements. System elements included in the models were common classroom features, such as windows, lights, and temperature control, suggesting that rooting the unit in place-based teaching may support ST skills. Full article

Kelp is a natural hydrogel material, which has been widely used in food industry. However, as a natural material, its properties have not been well explored. In this work, the surface and mechanical properties of kelp were investigated. The surface of kelp exhibited superoleophobicity and a self-clean property. The friction coefficient (COF) of the kelp surface was also low (<0.1). Interestingly, kelp demonstrated anisotropic mechanical properties either with or without metal ions. The tensile strength and toughness of kelp along with the growth direction (H) were better than those at the direction vertical to the growth direction (V). The adsorption of metal ions would significantly enhance the mechanical properties and ionic conductivity. Triboelectric nanogenerator (TENG) was assembled using kelp with NaCl, which showed excellent output performance (open-circuit voltage of 30 V, short-circuit current of 0.73 μA and charge transfer on contact of 10.5 nC). A writing tablet was prepared to use as the kelp-based self-powered tactile sensor. This work provides a new insight into natural kelp, which may be used as a renewable material. Full article

This study proposes a framework that leverages natural language processing and unsupervised machine learning techniques to measure, identify, and classify examinees’ writing strategies. The framework integrates three categories of writing strategies (text complexity, evidence use, and argument structure) to identify the characteristics of examinees’ writing. Additionally, a measurement model is used to calibrate examinees’ writing proficiency. An empirical example is presented to demonstrate the performance of the framework. The data comprise 430 Grade 8 examinees’ responses to English Language Arts (ELA) assessments in the United States. Using K-means clustering, distinct patterns were identified in each category. The one-parameter logistic measurement model was applied to estimate examinees’ writing proficiency. Analyses revealed significant effects of text complexity and evidence use on writing proficiency, while argument structure was not significant. This study has implications for writing instruction and assessment design that highlight the point that effective writing is not simply a matter of isolated skill acquisition, but rather the coordinated implementation of complementary strategies, a finding that supports cognitive developmental theories of writing. Full article

Background/Objectives: Children with above-average cognitive functioning often present complex developmental profiles, combining high cognitive potential with heterogeneous socio-emotional and learning trajectories. Although the cognitive and behavioral characteristics of giftedness have been widely studied in Anglophone countries, evidence remains limited in Southern Europe. This study aimed to investigate the cognitive, academic, and emotional–behavioral profiles of Italian children and adolescents with above-average cognitive functioning, using an inclusive, dimensional approach (IQ > 114). Methods: We analyzed a cross-sectional sample of 331 children and adolescents (ages 2.11–16.5 years), referred for clinical cognitive or behavioral evaluations. Participants were assessed using the WPPSI-III or WISC-IV for cognitive functioning, the MT battery for academic achievement, and the Child Behavior Checklist (CBCL) for emotional and behavioral symptoms. Comparative and correlational analyses were performed across age, gender, and functional domains. A correction for multiple testing was applied using the Benjamini–Hochberg procedure. Results: Gifted participants showed strong verbal comprehension (mean VCI: preschoolers = 118; school-aged = 121) and relative weaknesses in working memory (WM = 106) and processing speed (PS = 109). Males outperformed females in perceptual reasoning (PR = 121 vs. 118; p = 0.032), while females scored higher in processing speed (112 vs. 106; p = 0.021). Difficulties in writing and arithmetic were observed in 47.3% and 41.8% of school-aged participants, respectively. Subclinical internalizing problems were common in preschool and school-aged groups (mean CBCL T = 56.2–56.7). Working memory negatively correlated with total behavioral problems (r = −0.13, p = 0.046). Conclusions: These findings confirm the heterogeneity of gifted profiles and underscore the need for personalized educational and psychological interventions to support both strengths and vulnerabilities in gifted children. Caution is warranted when interpreting these associations, given their modest effect sizes and the exploratory nature of the study. Full article

The Amazigh language, spoken by millions across North Africa, presents unique computational challenges due to its complex morphological system, dialectal variation, and multiple writing systems. This survey examines technological advances over the past decade across four key domains: natural language processing, speech recognition, optical character recognition, and machine translation. We analyze the evolution from rule-based systems to advanced neural models, demonstrating how researchers have addressed resource constraints through innovative approaches that blend linguistic knowledge with machine learning. Our analysis reveals uneven progress across domains, with optical character recognition reaching high maturity levels while machine translation remains constrained by limited parallel data. Beyond technical metrics, we explore applications in education, cultural preservation, and digital accessibility, showing how these technologies enable Amazigh speakers to participate in the digital age. This work illustrates that advancing language technology for marginalized languages requires fundamentally different approaches that respect linguistic diversity while ensuring digital equity. Full article

Many academic introductions suffer from inconsistencies and a lack of comprehensive structure, often failing to effectively outline the core elements of the research. This not only impacts the clarity and readability of the article but also hinders the communication of its significance and objectives to the intended audience. This study aims to automate the CaRS (Creating a Research Space) model using machine learning and natural language processing techniques. We conducted a series of experiments using a custom-developed corpus of 50 biology research article introductions, annotated with rhetorical moves and steps. The dataset was used to evaluate the performance of four classification algorithms: Prototypical Network (PN), Support Vector Machines (SVM), Naïve Bayes (NB), and Random Forest (RF); in combination with six embedding models: Word2Vec, GloVe, BERT, GPT-2, Llama-3.2-3B, and TEv3-small. Multiple experiments were carried out to assess performance at both the move and step levels using 5-fold cross-validation. Evaluation metrics included accuracy and weighted F1-score, with comprehensive results provided. Results show that the SVM classifier, when paired with Llama-3.2-3B embeddings, consistently achieved the highest performance across multiple tasks when trained on preprocessed dataset, with 79% accuracy and weighted F1-score on rhetorical moves and strong results on M2 steps (75% accuracy and weighted F1-score). While other combinations showed promise, particularly NB and RF with newer embeddings, none matched the consistency of the SVM–Llama pairing. Compared to existing benchmarks, our model achieves similar or better performance; however, direct comparison is limited due to differences in datasets and experimental setups. Despite the unavailability of the benchmark dataset, our findings indicate that SVM is an effective choice for rhetorical classification, even in few-shot learning scenarios. Full article

Large Language Models (LLMs) are driving a revolution in the way we access information, yet there remains a lack of exploration to capture people’s information interaction preferences in LLM environments. In this study, we designed a comprehensive analysis framework to evaluate students’ prompt texts during a professional academic writing task. The framework includes a dimensionality reduction and classification method, three topic modeling approaches, namely BERTopic, BoW-LDA, and TF-IDF-NMF, and a set of evaluation criteria. These criteria assess both the semantic quality of topic content and the structural quality of clustering. Using this framework, we analyzed 288 prompt texts to identify key topics that reflect students’ information interaction behaviors. The results showed that students with low academic performance tend to focus on structural clarity and task execution, including task inquiry, format specifications, and methodological search, indicating that their interaction mode is instruction-oriented. In contrast, students with high academic performance interact with LLM not only in basic task completion but also in knowledge integration and the pursuit of novel ideas. This is reflected in more complex topic levels and diverse, innovative keywords. It shows that they have stronger self-planning and self-regulation abilities. This study provides a new approach to studying the interaction between students and LLM in engineering education by using natural language processing to process prompts, contributing to the exploration of the performance of students with different performance levels in professional academic writing using LLM. Full article

In the era of rapid technological advancement and ever-increasing data availability, the field of risk modeling faces both unprecedented challenges and opportunities. Traditional risk modeling approaches, while robust, often struggle to capture the complexity and dynamic nature of modern risk factors. This paper aims to provide a method for dealing with the insurance pricing problem of pricing predictability and MLOT (Money Left On Table) when writing a book of risks. It also gives an example of how to improve risk selection through suitable choices of machine learning algorithm and acquainted loss function. We apply this methodology to the provided data and discuss the impacts on risk selection and predictive power of the models using the data provided. Full article

Medical imaging is crucial for diagnosing, monitoring, and treating medical conditions. The medical reports of radiology images are the primary medium through which medical professionals can attest to their findings, but their writing is time-consuming and requires specialized clinical expertise. Therefore, the automated generation of radiography reports has the potential to improve and standardize patient care and significantly reduce the workload of clinicians. Through our work, we have designed and evaluated an end-to-end transformer-based method to generate accurate and factually complete radiology reports for X-ray images. Additionally, we are the first to introduce curriculum learning for end-to-end transformers in medical imaging and demonstrate its impact in obtaining improved performance. The experiments were conducted using the MIMIC-CXR-JPG database, the largest available chest X-ray dataset. The results obtained are comparable with the current state of the art on the natural language generation (NLG) metrics BLEU and ROUGE-L, while setting new state-of-the-art results on F1 examples-averaged F1-macro and F1-micro metrics for clinical accuracy and on the METEOR metric widely used for NLG. Full article

This study investigates the effects of LLM-based coding assistance on web application development by students using a frontend framework. Rather than comparing different models, it focuses on how students interact with LLM tools to isolate the impact of query type on coding success. To this end, participants were instructed to rely exclusively on LLMs for writing code, based on a given set of specifications, and their queries were categorized into seven types: Error Fixing (EF), Feature Implementation (FI), Code Optimization (CO), Code Understanding (CU), Best Practices (BP), Documentation (DOC), and Concept Clarification (CC). The results reveal that students who queried LLMs for error fixing (EF) were statistically more likely to have runnable code, regardless of prior knowledge. Additionally, students seeking code understanding (CU) and error fixing performed better, even when normalizing for previous coding ability. These findings suggest that the nature of the queries made to LLMs influences the success of programming tasks and provides insights into how AI tools can assist learning in software development. Full article

This paper explores Great Basin arid-zone hunter–forager rock art as signalling behaviour. The rock art in Lincoln County, Nevada, is the focus, and this symbolic repertoire is analysed within its broader archaeological and ethnographic contexts. This paper mobilises an explicitly theoretical approach which integrates human behavioural ecology (HBE) and the precepts of information exchange theory (IET), generating assumptions about style and signalling behaviour based on hunter–forager mobility patterns. An archaeological approach is deployed to contextualise two characteristic regional motifs—the Pahranagat solid-bodied and patterned-bodied anthropomorphs. Contemporary Great Basin Native American communities see Great Basin rock writing through a shamanistic ritual explanatory framework, and these figures are understood to be a powerful spirit figure, the Water Baby, and their attendant shamans’ helpers. This analysis proposes an integrated model to understand Great Basin symbolic behaviours through the Holocene: taking a dialogical approach to travel backward from the present to meet the archaeological past. The recursive nature of rock art imagery and its iterative activation by following generations allows for multiple interpretive frameworks to explain Great Basin hunter–forager and subsequent horticulturalist signalling behaviours over the past ca. 15,000 years. Full article

The development of printable, conductive, and biocompatible hydrogels has emerged as a promising strategy for the next generation of flexible and soft sensor platforms. In this study, we present a systematic investigation of alginate-based hydrogels incorporating different carbonaceous materials, natural graphite, carbon black (Vulcan V3), and activated carbon (PCO1000C), to evaluate their suitability for sensor applications. Hydrogels were formulated with varying concentrations of sodium alginate and a fixed loading of carbon additives. Each composite was characterized in terms of electrical conductivity under compression, rheological behavior, and mechanical strength. Printability was assessed using a custom-designed extrusion platform that allowed for the precise determination of the minimum force and optimal conditions required to extrude each formulation through a standard 20G nozzle. Among all tested systems, the alginate–graphite hydrogel demonstrated superior extrudability, shear-thinning behavior, and shape fidelity, making it well-suited for 3D printing or direct ink writing. A simple conductivity-testing device was developed to verify the electrical response of each hydrogel in the hydrated state. The effects of different drying methods on the final conductivity were also analyzed, showing that oven drying at 50 °C yielded the highest restoration of conductive pathways. Mechanical tests on printed structures confirmed their ability to maintain shape and resist compressive forces. Finally, the biocompatibility of the printed alginate–graphite hydrogel was validated using a standard cytotoxicity assay. The results demonstrated high cell viability, confirming the material’s potential for use in biomedical sensing environments. This work offers a robust framework for the development of sustainable, printable, and biocompatible conductive hydrogels. The combined performance in printability, mechanical integrity, electrical conductivity, and cytocompatibility highlights their promise for flexible biosensors and wearable sensor technologies. Full article

Metamorphosis is central to Indigenous Amazonian cosmologies, which often posit a period in the past when transformations from one being into another proliferated. This time gave way to the relative stability of the present that always runs the risk of going back to an ongoing process of transmutation. In this article, I highlight the significance of plants in Amerindian ontologies of becoming as catalysts of metamorphic movements through their entheogenic effects, through their curative properties and as the ancestors and teachers of humans. Beyond being the facilitators of other entities’ transformations and the virtual grandparents of all beings, plants are also masters of metamorphosis, displaying much more plasticity in adapting to their surroundings than animals. I argue that contemporary Amazonian women’s poetry translates the multiple transformations of vegetal life into literary form. In many Amazonian Indigenous communities, women have traditionally been the ones responsible for plant cultivation, while, in Western societies, women are often associated to certain parts of plants, such as flowers, and to nature as a whole. In the article, I analyze the poetry of Colombian author Anastasia Candre Yamacuri (1962–2014) and Peruvian writer Ana Varela Tafur (1963-), who emphasize the metamorphic potential of plants and the ontology of becoming at play in Amazonia. I contend that women’s writing on plants reflects evolving views on both plants’ and women’s roles in Amazonian societies, marked by rapid social transformation and environmental destruction. Full article

The word “curiosity” has an opaque history with contradictory attitudes and connotations acquired ever since Antiquity. This poses an interesting problem in the case of Leo Africanus, who never uses the word in his Cosmographia de l’Affrica yet exhibits curiosity at every turn as a traveler and a writer. This essay relies on a distinction that Michel Foucault makes regarding types of curiosity: that which produces conventional knowledge (which he rejects) and that which seeks extraordinary knowledge that “enables one to get free of oneself”, resulting in “the knower’s straying afield of himself”. Both as a traveler and a writer, Michel de Montaigne demonstrates that such an attitude was a living reality in sixteenth-century Europe. Montaigne’s many reflections on his “straying afield of himself” provide a bridge to interpreting Leo Africanus’s practices of traveling and writing. Leo’s profession as a diplomat, his economic expertise and his training as an Islamic legal expert all led to his far-reaching journeys, particularly in Islamic Africa but also Asia as of a young age, bringing about his many encounters with historical figures and events while also granting him access to uninhabited nature, as well as every sort of human settlement, from remote villages to great cities. His will to knowledge—curiosity that leads him to ‘stray afield of himself’ by seeking out the unusual and the unknown—proves to be the key to his travel and his writing. Full article

Traditional Mongolian document layout analysis faces unique challenges due to its vertical writing system and complex structural arrangements. Existing methods often struggle with the directional nature of traditional Mongolian text and require substantial computational resources. In this paper, we propose a direction-aware lightweight framework that effectively addresses these challenges. Our framework introduces three key innovations: a modified MobileNetV3 backbone with asymmetric convolutions for efficient vertical feature extraction, a dynamic feature enhancement module with channel attention for adaptive multi-scale information fusion, and a direction-aware detection head with $(sin\theta ,cos\theta )$ vector representation for accurate orientation modeling. We evaluate our method on TMDLAD, a newly constructed traditional Mongolian document layout analysis dataset, comparing it with both heavy ResNet-50-based models and lightweight alternatives. The experimental results demonstrate that our approach achieves state-of-the-art performance, with 0.715 mAP and 92.3% direction accuracy with a mean absolute error of only 2.5°, while maintaining high efficiency at 28.6 FPS using only 8.3 M parameters. Our model outperforms the best ResNet-50-based model by 3.6% in mAP and the best lightweight model by 4.3% in mAP, while uniquely providing direction prediction capability that other lightweight models lack. The proposed framework significantly outperforms existing methods in both accuracy and efficiency, providing a practical solution for traditional Mongolian document layout analysis that can be extended to other vertical writing systems. Full article