Search Results (317)

In cognitive science and education research, learning has been described to occur at surface and deep levels. Learners are thought to orient more toward one of these approaches to learning versus the other. In cognitive science, this has been assessed with a concept-building framework using objective function learning tasks to classify students as exemplar (surface) or abstraction (deep) learners. In education, the student approach to learning (SAL) framework has used self-report survey measures to classify learners as relying on surface approaches or deep approaches to learning. In two studies, we directly compared these two frameworks using self-report data from the Modified Approaches and Study Skills Inventory (M-ASSIST) and the Revised Study Process Questionnaire (R-SPQ-2F) along with objectively determined concept-building classifications from a computer-based function learning task. Potential links between exemplar learning and surface approaches and between abstraction learning and deep approaches were not found. We discuss possible explanations for the absence of empirical links, including inaccuracies in students’ metacognitions regarding their learning, the measures, and possible differences between learning-content-dependencies of the survey responses versus content neutrality of the concept-building task. We conclude by suggesting directions for future work in assessing and comparing surface and deep learning across frameworks. Full article

Background/Objectives: Artificial intelligence (AI) is rapidly emerging as a transformative force in healthcare education, including orthodontics. This systematic review and meta-analysis aimed to evaluate the integration of AI into orthodontic training programs, focusing on its effectiveness in improving diagnostic accuracy, learner engagement, and the perceived quality of AI-generated educational content. Materials and Methods: A comprehensive literature search was conducted across PubMed, Scopus, Web of Science, and Embase through May 2025. Eligible studies involved AI-assisted educational interventions in orthodontics. A mixed-methods approach was applied, combining meta-analysis and narrative synthesis based on data availability and consistency. Results: Seven studies involving 1101 participants—including orthodontic students, clinicians, faculty, and program directors—were included. AI tools ranged from cephalometric landmarking platforms to ChatGPT-based learning modules. A fixed-effects meta-analysis using two studies yielded a pooled Global Quality Scale (GQS) score of 3.69 (95% CI: 3.58–3.80), indicating moderate perceived quality of AI-generated content (I² = 64.5%). Due to methodological heterogeneity and limited statistical reporting in most studies, a narrative synthesis was used to summarize additional outcomes. AI tools enhanced diagnostic skills, learner autonomy, and perceived satisfaction, particularly among students and junior faculty. However, barriers such as limited curricular integration, lack of training, and faculty skepticism were recurrent. Conclusions: AI technologies, especially ChatGPT and digital cephalometry tools, show promise in orthodontic education. While learners demonstrate high acceptance, full integration is hindered by institutional and perceptual challenges. Strategic curricular reforms and targeted faculty development are needed to optimize AI adoption in clinical training. Full article

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

Background: Rock–blast design is a canonical inverse problem that joins elastodynamic partial differential equations (PDEs), fracture mechanics, and stochastic heterogeneity. Objective: Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, a systematic review of mathematical methods for geomechanically informed blast modelling and optimisation is provided. Methods: A Scopus–Web of Science search (2000–2025) retrieved 2415 records; semantic filtering and expert screening reduced the corpus to 97 studies. Topic modelling with Bidirectional Encoder Representations from Transformers Topic (BERTOPIC) and bibliometrics organised them into (i) finite-element and finite–discrete element simulations, including arbitrary Lagrangian–Eulerian (ALE) formulations; (ii) geomechanics-enhanced empirical laws; and (iii) machine-learning surrogates and multi-objective optimisers. Results: High-fidelity simulations delimit blast-induced damage with ≤0.2 m mean absolute error; extensions of the Kuznetsov–Ram equation cut median-size mean absolute percentage error (MAPE) from 27% to 15%; Gaussian-process and ensemble learners reach a coefficient of determination ($R^2>0.95$) while providing closed-form uncertainty; Pareto optimisers lower peak particle velocity (PPV) by up to 48% without productivity loss. Synthesis: Four themes emerge—surrogate-assisted PDE-constrained optimisation, probabilistic domain adaptation, Bayesian model fusion for digital-twin updating, and entropy-based energy metrics. Conclusions: Persisting challenges in scalable uncertainty quantification, coupled discrete–continuous fracture solvers, and rigorous fusion of physics-informed and data-driven models position blast design as a fertile test bed for advances in applied mathematics, numerical analysis, and machine-learning theory. Full article

This paper presents the development, implementation, and outcomes of the ELEVATE-US-UP (Engaging Learners through Exploration of Visionary Academic Thought and Empowerment in UnderServed and UnderPrivileged communities) teaching methodology, an equity-centered, culturally responsive pedagogical framework designed to enhance student engagement, academic performance, and science identity among underrepresented learners. This framework was piloted at Northern New Mexico College (NNMC), a Hispanic- and minority-serving rural institution. ELEVATE-US-UP reimagines science education as a dynamic, inquiry-driven, and contextually grounded process that embeds visionary scientific themes, community relevance, trauma-informed mentoring, and authentic assessment into everyday instruction. Drawing from culturally sustaining pedagogy, experiential learning, and action teaching, the methodology positions students not as passive recipients of content but as knowledge-holders and civic actors. Implemented across upper-level environmental science courses, the method produced measurable gains: class attendance rose from 67% to 93%, average final grades improved significantly, and over two-thirds of students reported a stronger science identity and a newfound confidence in their academic potential. Qualitative feedback highlighted increased perceptions of classroom inclusivity, community relevance, and instructor support. By centering on cultural context, student voice, and place-based application, the ELEVATE-US-UP framework offers a replicable and scalable model for educational transformation in underserved regions. Full article

Virtual reality is increasingly used in health sciences education, including healthcare, nursing, and medical education. Hence, this study provides an overview of the use of virtual reality within healthcare education, nursing education, and medical education through the analysis of published documents from 2010 to 2025. Based on the outcomes of this study, virtual reality emerged as an effective educational tool that can support students and health professionals. The immersive, realistic, and safe environments created in virtual reality allowed learners to enhance their knowledge and practice their skills, patient interactions, and decision-making without risking patient safety. Improvements in learning outcomes, including performance, clinical skills development, critical thinking, and knowledge acquisition were observed. Virtual reality also positively contributes toward a more holistic health sciences education as it increases students’ empathy and behavioral understanding. Finally, eight main research topics were identified and research gaps and future research directions are presented. Full article

Critical data literacy (CDL) has emerged as a crucial component in data science education, transcending traditional disciplinary boundaries. Promoting CDL requires collaborative approaches to enhance learners’ skills in data science, going beyond mere quantitative reasoning to encompass a comprehensive understanding of data workflows and tools. Despite the growing literature on CDL, there is still a need to explore how students use data science practices for supporting the learning of CDL throughout a summer-long data science program. Drawing on situative perspectives of learning, we utilize a descriptive case study to address our research question: How do data science practices taught in a classroom setting differ from those enacted in real-world social justice projects? Key findings reveal that while the course focused on abstract principles and basic technical skills, the Food Justice Project provided students with a more applied understanding of data tools, ethics, and exploration. Through the project, students demonstrated a deeper engagement with CDL, addressing real-world issues through detailed data analysis and ethical considerations. This manuscript adds to the literature within data science education and has the potential to bridge the gap between theoretical knowledge and practical application, preparing students to address real-world data science challenges through their coursework. Full article

The rapid development of science and technology enables technological innovations to change the way of English oral learning. Based on the use of a large language model (LLM), we developed a novel dynamic evaluation system for oral English, LLM-DAELSL, which combines daily oral habits and a textbook outline. The model integrates commonly used vocabulary from everyday social speech and authoritative prior knowledge, such as oral language textbooks. It also combines traditional large-scale semantic models with probabilistic algorithms to serve as an oral assessment tool for undergraduate students majoring in English-related fields in universities. The model provides corrective feedback to effectively enhance the proficiency of English learners through guided training at any time and place. The technological principle of the model involves inputting prior template knowledge into the language model for reverse guidance and utilizing the textbooks provided by China’s Ministry of Education. The model facilitates the practice and evaluation of pronunciation, grammar, vocabulary, and fluency. The six-month tracking results showed that the oral proficiency of the system learners was significantly improved in the four aspects, which provides a reference for other language learning method developments. Full article

Multi-agent large language models promise flexible, modular architectures for delivering personalized educational content. Drawing on a pilot randomized controlled trial with middle school students (n = 23), we introduce a two-agent GPT-4 framework in which a Profiler agent infers learner-specific preferences and a Rewrite agent dynamically adapts science passages via an explicit message-passing protocol. We implement structured system and user prompts as inter-agent communication schemas to enable real-time content adaptation. The results of an ordinal logistic regression analysis hinted that students may be more likely to prefer texts aligned with their profile, demonstrating the feasibility of multi-agent system-driven personalization and highlighting the need for additional work to build upon this pilot study. Beyond empirical validation, we present a modular multi-agent architecture detailing agent roles, communication interfaces, and scalability considerations. We discuss design best practices, ethical safeguards, and pathways for extending this framework to collaborative agent networks—such as feedback-analysis agents—in K-12 settings. These results advance both our theoretical and applied understanding of multi-agent LLM systems for personalized learning. Full article

Contemporary educational literature suggests that academic emotions and emotion regulation should be explored in tandem, while academic motivation has been discussed both as a self-regulation metacognitive construct and as a construct inherently tied to motivation. The present study uses a person-centered approach to explore profiles of university students based on academic emotions, emotion regulation, academic self-regulation, and approaches to learning. In addition, the impact of students’ profiles on academic performance (GPA) is investigated. The sample consists of 509 university students studying at a Greek university social science department. Cluster techniques and multivariate analysis of variance are used to identify the profiles and test for differences among them. Students were grouped in clusters that revealed both consistent and dissonant patterns of scores on the relevant variables. Analysis reveals three distinct profiles: (a) the “Anxious, effectively-engaged, and organized learners”, (b) the “Deep, Happy, and intrinsically motivated learners” and (c) the “Disengaged, Bored, and Suppressing Learners”. These profiles open new insights into educational literature, revealing links among learning, emotional, and motivational factors. Practical implications and directions for future research are discussed. Full article

This article introduces China’s first Center for Music Performance Science and Musicians’ Medicine. In the center, motion capture (MoCap) technology is used to study piano performance and musicians’ health. An idea and methodology to assess the performance of piano performance are developed in the center. The center uses high-precision MoCap system to analyze movement efficiency, posture, joint angles, and coordination of pianists. By addressing physical challenges, the center promotes healthier, more efficient practice ways, especially for adolescent piano learners. The pioneering research results bridge the gap between music performance (art) and science, positioning China as a leader in music performance science and musicians’ health. Full article

The Language, Culture, and Knowledge-building through Science (LaCuKnoS) project tests and refines a model of science teaching and learning that brings together current research on the role of language in science communication, the role of cultural and community connections in science engagement, and the ways people apply science knowledge to their daily decision making. One key component of the model brings families together as co-learners and co-teachers through family learning experiences. We describe our work to promote more robust family conversations about science in our lives within an existing research practice partnership, using a two-tiered qualitative conversational analysis to compare the family conversations that result from three family engagement models: (a) family science festivals; (b) family science workshops; and (c) family science home learning. More specifically, this paper addresses the question: How do families describe and evaluate science in their lives and communities during family conversations that occur during each of these three engagement models? Discourse analysis using the appraisal dimension of systemic functional linguistics highlights the affective components of families evaluating science in their lives, as well as how each model provided unique affordances for different communicative goals. These findings are used to propose a set of design principles to guide the continued exploration of community-sustaining and family-centric models of family engagement as a key strategy for broadening science participation. Full article

Background: Conventional machine learning courses are usually designed for academic learners, instead of working professionals. This study addresses this gap by proposing a new instructional framework that builds practical computational thinking skills for developing neural network models on business data. Methods: This study proposes a five-component computational thinking framework tailed for working professionals, aligned with the standard data science pipeline and an artificial intelligence instructional taxonomy. The proposed course instructional framework consists of mixed lectures, visualization-driven and coding-driven workshops, case studies, group discussions, and gamified model tuning tasks. Results: Across 28 face-to-face course iterations conducted between 2019 and 2024, participants consistently demonstrated satisfactions in gaining computational-thinking skills. Conclusions: The tailored framework has been implemented to strengthen working professionals’ computational thinking skills for neural-network work on industrial applications. Full article

This study investigates participation patterns in publicly accessible educational events organized by a college policy and research center. These programs, which addressed topics related to energy and sustainability, were conducted both online and in person and featured expert speakers from government, business, and nonprofit sectors. By analyzing registration data from over 1400 participants across multiple events, the study identifies key trends in attendance, including sector affiliation, repeat attendance, and the impact of regional relevance on program popularity. The findings indicate that most participants were private citizens or affiliated with the business sector, with lower attendance rates from academia, government, and nonprofit sectors. Furthermore, the study underscores the popularity of regionally pertinent topics and the challenges in attracting participants to more specialized topics. The research highlights the importance of providing low-barrier, accessible adult environmental education (AEE) opportunities. It suggests that colleges and universities, with organizational capabilities and access to expert speakers, are uniquely positioned to offer these programs. This study contributes to the limited quantitative research on AEE, addressing a gap in understanding participation patterns and engagement within the field. Full article

This study systematically compares the predictive performance of single machine learning (ML) models (KNN, Bayesian ridge regression, decision tree) and ensemble learning methods (bagging, boosting, stacking) for quantifying the rheological properties of mineral powder-modified asphalt, specifically the complex shear modulus (G*) and the phase angle (δ). We used two emulsifiers and three mineral powders for fabricating modified emulsified asphalt and conducting rheological property tests, respectively. Dynamic shear rheometer (DSR) test data were preprocessed using the local outlier factor (LOF) algorithm, followed by K-fold cross-validation (K = 5) and Bayesian optimization to tune model hyperparameters. This framework uniquely employs cross-validated predictions from base models as input features for the meta-learner, reducing information leakage and enhancing generalization. Traditional single ML models struggle to characterize accurately as a result, and an innovative stacking model was developed, integrating predictions from four heterogeneous base learners—KNN, decision tree (DT), random forest (RF), and XGBoost—with a Bayesian ridge regression meta-learner. Results demonstrate that ensemble models outperform single models significantly, with the stacking model achieving the highest accuracy (R² = 0.9727 for G* and R² = 0.9990 for δ). Shapley additive explanations (SHAP) analysis reveals temperature and mineral powder type as key factors, addressing the “black box” limitation of ML in materials science. This study validates the stacking model as a robust framework for optimizing asphalt mixture design, offering insights into material selection and pavement performance improvement. Full article