Search Results (996)

Lockdown drills have become standard practice in K-12 schools across the United States, but there are concerns about the psychological health impact, quality of implementation, and equity implications of current practices. This scoping review compiles the empirical literature on lockdown and active-threat drills to provide insight into how drills are defined and conducted, what outcomes are measured, and the remaining gaps. In accordance with well-researched scoping review methodologies, 27 peer-reviewed U.S.-based studies were aggregated from six primary areas: drill definitions and typologies, implementation practices, reported outcomes, trauma-informed and developmental considerations, equity and disability inclusion, and evidence gaps. Findings reveal wide variability among drill terminology and protocol categorization and most studies emphasize advance warning and low-realism practices. Psychological outcomes are measured much more often than objective measures of implementation fidelity or physical preparedness. Educator and staff experiences, caregiver perceptions, and longitudinal outcomes are underrepresented. Although a number of studies report developmental adaptations and disability accommodations, comprehensive equity analyses remain rare. Overall, potential psychological harms are more clearly documented than protective effects in the literature. This review emphasizes the importance of standardized drill taxonomies, fidelity measurement methods, trauma-informed mental health integration, and inclusive designs to inform school safety policy and practice. Full article

A dual-platform mushroom cultivation system integrating artificial intelligence (AI)-assisted environmental monitoring and controlled-environment agriculture (CEA) was developed to support experiential STEM education across K–12 and undergraduate settings. Hands-on instruction with multicellular fungi is often limited by reliance on microbial models and by constraints associated with field-based activities. To address this gap, we implemented an indoor instructional platform that combines a commercial AI-assisted automated cultivation unit with a tent-based chamber for hands-on environmental control. Representative cultivated species included oyster mushrooms (Pleurotus spp.) and lion’s mane (Hericium erinaceus). The AI-assisted system provided sensor/camera-based monitoring, app-based feedback, and software-assisted regulation of humidity, light, and airflow, whereas the tent-based system enabled direct student manipulation of cultivation conditions. Together, the systems allowed students to observe fungal development, manage environmental parameters, and collect quantitative and qualitative data within a single academic term. Post-harvest activities, including mushroom-based food preparation and tasting, further connected fungal biology with food and sustainability. A matched pre- and post-course survey (n = 30) showed increases in students’ self-reported perceived understanding, cultivation confidence, and engagement, with mean scores increasing from approximately 2–4 to 6–8. Because the survey instrument was not formally validated and no control group was included, these results are interpreted as preliminary self-reported perceptions rather than objective evidence of learning gains. The platform provides a practical model for integrating fungal biology, AI-assisted environmental monitoring, and CEA into STEM education. Full article

The prediction of student engagement characteristics involves forecasting and analyzing student interaction with educational materials using engagement prediction models. This process encompasses the prediction of cognitive, behavioral, and emotional dimensions of engagement. The existing student engagement prediction models have some limitations, including poor convergence, less generalizability, complexity issues, overfitting, false errors, and limited resources. Hence, the research proposes the Multi-Instance Localization-based Gradient Boosted Long Short-Term Memory (MIL-GBLTM) model to tackle the challenge of predicting student engagement characteristics in online classes. The integration of effective MIL with a Triplet Attention mechanism focuses on the significant features that help with engagement prediction; LSTM layers capture intricate sequential patterns, and fractional gradient boosting is used for fine-tuning for accurate prediction, alongside ensemble-based learning. The LSTM layers with the Triplet Attention module refine temporal attention, and Fractional Gradient Boosting ensures the model’s adaptability and robustness. By combining these components, the proposed model is able to predict accurate student engagement with high convergence. This integrated approach enhances the capabilities of engagement prediction models in educational contexts, facilitating more effective interventions and personalized student support in online learning environments. Experimental results demonstrate that the proposed MIL-GBLTM model outperforms other existing models by achieving the highest accuracy of 96.55% with a k-fold of 10, utilizing the wacv2016 dataset. Full article

School discipline disproportionately affects Black students and is associated with diminished academic outcomes. However, the mechanisms through which exclusionary discipline constrains college/university degree attainment—and the role of mental health in this pathway—remain underexplored with longitudinal data from a large urban birth cohort. This study examines whether depressive symptoms mediate the relationship between high school discipline and college/university degree attainment, and whether this mediation pathway varies by race and sex. Using data from the Future of Families and Child Wellbeing Study (N = 1417), I employed generalized structural equation modeling (GSEM) to test a serial mediation model: school discipline (Year 15) → depressive symptoms (Year 15) → college-going behaviors (Year 15) → college/university degree attainment (Year 22). Bootstrap confidence intervals (1000 replications) tested indirect effects. Moderation analyses examined whether the mediation pathway differed by race, sex, and depressive symptoms’ severity. School discipline significantly predicted higher depressive symptoms (b = 0.46, p = 0.001), which in turn predicted fewer college-going behaviors (b = −0.02, p = 0.001) and lower odds of college/university degree attainment (OR = 0.89, p = 0.001). The total indirect effect through depressive symptoms was significant (b = −0.06, 95% BC CI [−0.134, −0.017]). Sex, but not race (F = 0.24, p = 0.868), moderated the discipline–depressive pathway: discipline increased depressive symptoms more strongly for females (b = 0.78, p = 0.001) than males (b = 0.21, p = 0.251). Depressive symptoms amplified discipline’s effect on college/university degree attainment (interaction OR = 0.39, p = 0.037). Depressive symptoms partially mediate school discipline’s negative effect on college attainment, with the strongest effects among females. Higher education institutions must prepare to support students whose K-12 experiences were marked by exclusionary discipline. Full article

Chinese cloze question generation for educational assessments requires identifying gap phrases that accurately reflect key knowledge points, posing significant challenges to automated systems. We observe that the syntactic boundaries revealed by part-of-speech (POS) tags closely align with the semantic boundaries of target gap phrases. Motivated by this observation, we propose a multi-task learning framework in which gap phrase identification serves as the primary task and POS tagging as a complementary auxiliary task. The two tasks share a common BERT-BiLSTM encoder, enabling mutual reinforcement of both syntactic and semantic representations through joint training. To further capture the interaction between label semantics and contextual word representations, we introduce a label-attention mechanism that models dependencies between the global word sequence and candidate label embeddings. Additionally, we construct a refined POS tag subset by excluding categories whose boundaries show no alignment with gap phrase boundaries, thereby strengthening the correspondence between the two tasks. Evaluated on a real-world dataset of 20.5K questions spanning five academic disciplines, our method achieves an F1 score of 65.85%, with a Recall of 67.79%, representing improvements of 2.12% and 4.35% over the prior state-of-the-art, respectively. These results demonstrate that exploiting the alignment between syntactic and semantic structures through joint learning is effective for generating educationally meaningful fill-in-the-blank questions. Full article

Engineering design education is an effective instructional approach for enhancing students’ motivation, interest, and creativity while introducing them to the engineering design process (EDP). However, there is limited knowledge on how to integrate the EDP into robotics education, particularly AI-robotics, and how students experience AI-enabled robotics project-based learning grounded in an EDP. This pre-/posttest embedded mixed-methods study adds to the scarce body of literature on interdisciplinary education in engineering design, robotics, and AI. This project developed, implemented, and evaluated a project-based engineering design AI-robotics curriculum that introduced novice Computer Science (CS) high school students to robotics, machine learning, and AI. Students’ collaborative robotics projects were grounded in an EDP to introduce the students to engineering practices and promote engagement and interest through design-based, hands-on learning. An analysis of quantitative and qualitative data revealed an improvement in students’ CS attitudes, collaboration, and social interactions after participating in the curriculum. Recommendations for designing AI-robotics projects grounded in an EDP are discussed. Full article

Student dropout remains an important challenge in higher education because it affects degree completion, institutional resource efficiency, workforce preparation, and students’ long-term socioeconomic opportunities. This requires not only accurate predictions but also decision support that is both reliable and aware of uncertainty. This study posits that the amalgamation of probabilistic modeling, uncertainty quantification, and graph-based refinement can augment both predictive reliability and decision support for the early detection of dropouts. A reliability-centered predictive framework is presented, integrating Educational Competition Optimization (ECO)-based feature selection, probabilistic Support Vector Classification (SVC), isotonic regression for probability calibration, and split conformal prediction for distribution-free uncertainty quantification. In addition, a similarity-driven Graph-based Fuzzy Cellular Automata (Graph-FCA) refinement mechanism is developed, where student relationships are modeled using a k-nearest neighbor graph with radial basis function similarity. Entropy-based confidence weighting is used to control uncertainty-aware propagation. An Explainable Artificial Intelligence layer based on SHAP provides both global and local interpretability, and fairness-aware evaluation assesses consistency across demographic groups. The suggested framework maintains predictive performance while improving probabilistic reliability. The Graph-FCA refinement achieves an accuracy of 0.7503, which is close to the calibrated ECO–SVC baseline (Accuracy = 0.7537; Macro-F1 = 0.6704) and also reduces the Brier score. The conformal prediction layer achieves empirical coverage close to the desired confidence level, ensuring reliable uncertainty estimates. The ECO–SVC–Conformal–GraphFCA framework transforms traditional classification into a reliable, understandable, and uncertainty-aware early warning system, enhancing its usefulness for ethical and informed decision-making in engineering education. Full article

Reducing carbon emissions from daily travel has become an important goal of 15-minute living-circle planning, yet it remains unclear which facility configurations are most supportive of green travel. Using 634 living circles and 20 million mobile-phone travel records and point-of-interest (POI) data, this study examines how facility layout within a 15-minute cycling circle influences residents’ walking and cycling travel behavior. Extreme Gradient Boosting (XGBoost) models and Shapley Additive Explanations (SHAP) suggest that low accessibility is generally associated with lower green travel shares, while moderate facility density promotes green travel, yet for some facility types, high density may show diminishing marginal benefits. Vegetable markets and primary schools emerge as key facilities, with education facilities driven mainly by accessibility, entertainment facilities by density, and commercial and healthcare facilities by both. K-means clustering identifies three types of low-green-travel-performing living circles—characterized by low density and poor accessibility—concentrated in peripheral and newly developed areas. The methodology is transferable, and the derived numerical ranges and living-circle typologies offer context-specific implications for Tangshan, and identified differences in facility importance and diminishing marginal benefits enrich 15-minute city theory. Full article

Objectives: We aimed to descriptively evaluate the feasibility and clinical utility of TowardPi BO (4K ultra-HD microscope integrated with a 400 kHz swept-source intraoperative optical coherence tomography (SS-iOCT) system) in managing various ophthalmic surgical conditions in a real-world setting. Methods: We analyzed surgical videos and data from 123 consecutive cases that underwent elective surgery with the assistance of this SS-iOCT system at Beijing Tongren Hospital between 2 September 2025 and 10 February 2026. Cases were included when the iOCT provided critical, real-time information that directly influenced surgical decision-making or technique modification. Cases were excluded if iOCT served only routine confirmatory or educational purposes without altering the surgical plan. Results: A total of 72 surgical cases were included, comprising 7 intraocular lens implantations with ciliary sulcus fixation, 19 macular holes, 3 cases of macular hole retinal detachment (MHRD), 4 cases of macular schisis with or without foveal detachment (MSRD), 12 cases of submacular hemorrhage, 20 cases of rhegmatogenous retinal detachment (RRD), and 7 intraocular mass lesions. The 400 kHz SS-iOCT significantly aided in surgical visualization, guided real-time decision-making, and prompted modifications in surgical techniques. Conclusions: To our knowledge, this is the first real-world study to evaluate the application of a 400 kHz SS-iOCT system across a wide spectrum of ophthalmic conditions, including its novel use in intraocular tumors. From routine to complex surgical cases, SS-iOCT enhances surgical precision and facilitates real-time decision-making, ultimately contributing to improved surgical outcomes. Full article

This paper presents and validates a unified spreadsheet-based framework for engineering mechanics education and preliminary design. Three modules are integrated within a single openly available workbook: multi-point resultant force and moment computation; axial normal stress with stress concentration effects for three geometric configurations (plate with hole, shoulder plate, stepped shaft); and beam deflection for simply supported and cantilever configurations under point loads. All governing equations are implemented as explicit closed-form expressions validated against analytical reference solutions for six independent cases; relative errors fall below ${10}^{-10}$ in all cases. Three worked exercises demonstrate the practical scope of the framework. A biomechanical multi-point force system yields joint moments of $-6880$, −33,421, and −58,241 N·mm at the wrist, elbow, and shoulder, respectively. A tensile shoulder plate with $K_t\approx 1.85$ produces ${\sigma }_{max}=232$ MPa against ${\sigma }_y=200$ MPa, identifying a design failure; a parametric redesign with fillet radius $r=10$ mm reduces $K_t$ to approximately 1.59 and ${\sigma }_{max}$ to approximately 198.7 MPa, restoring structural safety. A cantilever beam subjected to a 20,000 N tip load yields a maximum deflection of 13,133 μm. The framework constitutes a validated intermediate layer between manual analytical derivations and high-fidelity numerical simulations, applicable to preliminary design, parametric sensitivity studies, and engineering education at the linear elastic level. Full article

The quality of technology integration in K-8 science classrooms has significant implications for educational equity, particularly in minority–majority districts where teacher practice is among the strongest predictors of STEM persistence among underserved populations. This study examined the extent to which K-8 science teachers’ technology integration knowledge translated into transformative instructional practice within a large, minority–majority district in the U.S, using the frameworks of Information and communication technology (ICT)-Technological Pedagogical Content Knowledge (TPACK) and Passive, Interactive, Creative, Replacement, Amplification, Transformative (PICRAT) model. Technology integration planning knowledge was assessed using the ICT-TPACK instrument across elementary and middle school teachers. Instructional practice was rated using the PICRAT framework applied to teachers’ open-ended descriptions of their technology use. These responses also provided contextual illustration of quantitative patterns. Results indicate that while middle school teachers demonstrated significantly higher ICT-TPACK planning knowledge, this advantage primarily reinforced foundational science concepts through passive consumption rather than facilitating student agency. PICRAT analysis revealed that technology use across all grade levels was dominated by Replacement and Amplification practices, while creative and transformative uses remained nearly absent. These findings reveal a persistent knowing–doing gap in which planning knowledge did not translate into transformative enacted practice. Implications for equity-focused professional development and structural supports moving K-8 science teachers toward more transformative technology integration are discussed. Full article

The selection of educational technology (EdTech) tools by teachers remains a poorly systematized process, frequently disconnected from their pedagogical and technological competencies. This paper proposes a computational system that integrates teacher profiling based on the Technological Pedagogical Content Knowledge (TPACK) framework with unsupervised learning techniques and a cosine similarity-based recommendation mechanism. Using data collected through an adapted TPACK instrument administered to 303 secondary and higher education teachers, four structurally distinct profiles were identified using hierarchical clustering and K-means analysis. These profiles were used to generate personalized EdTech tool recommendations by matching them to the TPACK feature space. The system was evaluated using Recall@K, Mean Average Precision (MAP), Structural Alignment Index (SAI), and comparison with baseline models, using an expert-validated ground truth. The results show values of Recall@10 = 0.805, MAP = 0.777, and SAI = 0.998, with a Precision@1 improvement of 0.340 over the cosine baseline, suggesting that structural profiling of teacher knowledge improves the quality of the generated recommendations under expert-defined relevance conditions. The results obtained suggest that operationalizing the TPACK framework as a computational representation constitutes a promising basis for developing pedagogically contextualized recommendation systems in educational analytics environments. Full article

Aligning information technology (IT) workforce demands with educational assessments is essential for bridging skills gaps; yet, no prior corpus maps IT task reasoning to Programme for International Student Assessment (PISA) proficiency levels. This paper introduces a large language model (LLM)-powered framework aligning IT competencies with PISA 2022 and the OECD (Organisation for Economic Co-operation and Development) Learning Compass 2030, drawing on O*NET v30.2 (Occupational Information Network), ESCO (European Skills, Competences, Qualifications, and Occupations) v1.2.1, PISA descriptors and OECD definitions. The framework operates in two stages: Stage 1 aligns 562 IT task statements with minimum PISA 2022 proficiency levels via LLM annotation and cross-model validation; and Stage 2 extends this mapping to the OECD Learning Compass 2030 through the semantic clustering of task embeddings and a bidirectional gap analysis of 95 ESCO transversal skills. Using Gemini 2.5 Flash, 562 tasks are annotated with minimum PISA levels across Mathematical, Reading, and Science literacy (first stage). Annotation reliability is assessed through a five-model cross-validation against a blind human domain expert (treated as a reference benchmark, not a gold standard) on a stratified 100-task sample (17.8% of the corpus), with agreement ranging from fair (Gemini 2.5 Flash, κ = 0.29) to moderate (Claude Haiku 4.5, κ = 0.50; LLaMA 3.3 70B, κ = 0.44). A bias-correction sensitivity analysis confirms that distributional findings remain stable after accounting for the primary annotator’s systematic overestimation, and OLS-calibrated alignment against O*NET ability ratings provides directional plausibility support. Validated tasks are embedded and clustered into 25 technical profiles via K-Means, each classified against OECD dimensions. The framework is extended to 95 ESCO transversal skills in 24 clusters. Bidirectional analysis reveals that, while every PISA proficiency level is engaged by at least one transversal cluster, 33% of these clusters, covering creative, ethical, social–emotional, and dispositional competencies, fall entirely outside PISA’s cognitive scope. This boundary mapping identifies where the PISA-based alignment is valid and where complementary tools are required for a full readiness assessment. Full article

Arabic natural language processing (NLP) faces major difficulties due to the language’s rich morphological structure and the scarcity of high-quality datasets, especially for educational material distributed across diverse online platforms. Many existing large-scale corpus construction methods depend on extensive web crawling followed by substantial post-processing. This process may introduce irrelevant or low-quality data and often fails to represent the target domain adequately. As a result, a robust approach to developing corpora tailored for domain-sensitive educational NLP systems and linguistic depth is critical, as most current resources are inadequate. This paper presents ArabicEduCrawler, an AI-assisted focused crawling framework designed to improve the acquisition, discovery, and organization of Arabic educational web content. The framework integrates domain-aware source selection, in-crawl Arabic language detection using FastText, large language model (LLM)-assisted XPath extraction, and metadata retrieval to support corpus quality and traceability. Its two-layer architecture combines dynamic web crawling using Scrapy-Playwright with advanced NLP processing, including automatic linguistic annotation with GateNLP and Stanza and a sentence-aware chunking strategy designed for transformer-compatible token limits. Experiments across four major Arabic educational domains resulted in the creation of the Arabic Educational Web Corpus (AraEdu-WC), which consists of 101,770 documents segmented into approximately 286 k text chunks, with more than 50 million tokens, 289,778 sentences, and nearly 3.5 million named entities. The system achieved a harvest ratio of 95.25%, indicating its effectiveness in filtering and retaining relevant content. The sentence-aware chunking evaluation showed consistent improvements in top-ranked retrieval, achieving the highest Hit Rate@10 and MRR@10 across all four embedding models. In particular, the multilingual-E5-large model achieved a Hit Rate@10 of 70%, Precision@10 of 18%, and MRR@10 of 57%. These findings demonstrate that the proposed approach provides an effective balance between crawl efficiency, language purity, and content richness, offering a high-quality Arabic educational corpus for downstream NLP and retrieval research. Full article

Robust Arithmetic Word Problem (AWP) solving is important for applying mathematical reasoning systems in educational scenarios, where problem statements may contain changed numerical values, paraphrased descriptions, or irrelevant distracting information. Although Large Language Models (LLMs) have shown strong potential in solving AWPs, their reasoning processes may still be sensitive to surface-form variations and perturbation-induced noise. To address this issue, this paper proposes a Scene-Aware Neuro-Symbolic solver designed to improve the robustness of AWP solving under perturbations. The proposed method extends the existing scene-aware framework by introducing perturbation-oriented mechanisms at the scene, relation, and symbolic-solving levels. A Chain-of-Scene (CoS) prompting strategy first generates candidate scenes, after which goal-guided filtering retains target-related and bridge scenes while removing distractor-induced scenes. The retained scenes are then processed by the Scene-Aware Syntax-Semantics ($S^2$) method to extract explicit and implicit relations, and relation consistency checking is applied to remove locally plausible but globally irrelevant relations. Finally, the symbolic solver performs iterative equation-based reasoning over the filtered relation sets, with fallback recovery activated when standard solving does not produce a target-compatible answer. Experiments on AGG, MAWPS, and GSM8K show an average accuracy of 92.8% on clean datasets. On GSM-Perturb and AWP-Perturb, the solver achieves perturbed accuracies of 80.8% and 87.5%, with robustness drops of 8.3% and 6.8%, respectively. Ablation results show that scene filtering and relation consistency checking are the main contributors to reducing perturbation-induced errors. These findings suggest that combining LLM-based scene understanding with symbolic relation reasoning is a promising direction for improving the robustness and interpretability of AWP solvers in the evaluated perturbation settings. Full article