Search Results (1,015)

Learning environments are typically designed according to nominal seating capacity, yet their actual visual performance often falls well below this figure—a discrepancy defined here as an illusion of capacity. Conventional two-dimensional sightline analysis, the standard method for assessing visibility, cannot fully capture the three-dimensional effects of human occlusion or the influence of room scale on visual performance. To address this gap, the study evaluates three higher-education classroom typologies—a flat-floor classroom, a terraced classroom, and a constant-slope auditorium—using a Python-based three-dimensional ray-casting framework. Each seating position is assessed against four criteria: horizontal legibility, vertical strain, two-dimensional sectional clearance, and three-dimensional functional visibility. The results reveal marked differences between the two analytical approaches. In the smaller configuration, the three-dimensional analysis uncovered visible areas overlooked by sectional analysis: effective capacity in the flat-floor classroom increased from 12.50% to 29.16%, and total obstruction in the terraced classroom fell from 45.45% to 36.36%. The large auditorium showed the opposite trend—owing to cumulative shoulder overlap and a phenomenon termed perspective closure, its effective capacity dropped to 6.42% (versus 9.28% by sectional analysis), with 73.57% of seats fully obstructed. Full article

This study evaluated the effects of a strictly auditory–vocal instructional package, combining cued vocal rehearsal, forward chaining, and multiple stimulus control arrangements, on the acquisition of forward intraverbal story recall (RC, divergent relations) and the emergent performance on untaught reverse character identification (CI, convergent relations) probes across nine children with autism spectrum disorder (ASD). Utilizing a concurrent multiple-baseline design across character chains replication, the 1:1 intervention integrated the auditory–vocal components without initial permanent visual aids. All nine participants met the mastery criterion for the directly taught forward story-recall chains (mean = 4.6, 4.9, and 4.3 sessions across target characters). Following instruction, unreinforced probe performance on untaught CI relations increased above pre-instruction levels for all participants, with seven achieving mastery on these convergent tasks. Maintenance probes conducted at 2 and 4 weeks post-instruction demonstrated sustained responding for the majority of participants, though marked individual variability—including notable performance decays in story recall for two participants—was observed. Because a formal component analysis was not conducted, the specific contribution of vocal rehearsal cannot be isolated from the overall package, and theoretical mechanisms such as joint control serve only as a cautious, tentative conceptual account. While limited by CI’s functional problem-solving evidence being tempered by its measurement format within the staggered concurrent arrangement, the absence of component isolation, and direct classroom generalization probes, these preliminary findings suggest that package utilizing strictly auditory–vocal arrangements demonstrates initial feasibility for establishing complex intraverbal forward chains under controlled 1:1 clinical conditions. This foundation allows future research on adapting this package, considering temporal location parameter adjustments, to support multi-component stimulus control for vocal independence within inclusive classroom settings like Learning in Regular Classrooms (LRC). Full article

This study develops and empirically evaluates a multilevel analytical framework linking AI-generated mathematical reasoning, students’ diagnostic reasoning, and mathematics achievement in AI-supported mathematical problem solving. A classroom intervention with 76 undergraduate Calculus A students generated 380 repeated student–problem observations across five mathematical tasks and three instructional formulations. Students evaluated AI-generated solutions for final-answer correctness, methodological correctness, and procedural completeness, identified mathematical errors, and proposed corrections before completing an individual posttest. Hierarchical mixed-effects models and student-level achievement analyses were used to examine successive stages of the learning process. Instructional formulation was not significantly associated with the quality of AI-generated mathematical reasoning. Final-answer correctness, methodological correctness, and procedural completeness showed substantial task-specific variation. Instructional formulation was significantly associated with students’ error detection, with higher detection performance under Instruction Types B and C than under Type A, while AI-generated response quality remained comparable across instructional formulations. Baseline mathematical proficiency was the strongest predictor of posttest performance. Students’ Mean Error-Detection Event Rate was positively associated with posttest achievement. No robust overall independent association between instructional formulation and posttest achievement was identified after baseline adjustment. Students’ diagnostic engagement with AI-generated mathematical reasoning was associated with subsequent mathematics achievement, supporting the analysis of diagnostic reasoning as a distinct component of AI-supported mathematics learning. Full article

The study explores the paradigm shift in education brought about by the introduction of generative artificial intelligence (AI) tools, focusing on educational stakeholders’ self-reported perceptions rather than observed changes in teaching or learning outcomes. We consider stakeholders’ views on AI-based technologies within the teaching–learning process. The current study uses a cross-sectional empirical survey design with a sample of N = 917 respondents, including teachers, students, administrators, and management. It examines the use of advanced AI technologies such as ChatGPT, Gemini, DeepSeek, and Grok, and stakeholders’ perceived connection between digital skills and classroom performance, student motivation, and critical thinking. We also discuss the ethical dilemmas and structural challenges that accompany this digital change. Inferential statistics, such as One-Way ANOVA and the Pearson Chi-Square test, show statistically significant differences in perceptions and regulatory expectations across organizational responsibilities. The findings contribute to understanding how advanced digitalization is perceived to reshape traditional academic roles, offering practical insights for creating effective, responsible, and sustainable teaching practices. Full article

With the development of smart classrooms and digital learning platforms, multimodal learning behavior data provide a new sensing basis for understanding students’ cognitive states and psychological risk warnings. However, existing educational data mining methods mainly focus on performance prediction, dropout warning, or surface-level emotion recognition, while continuous and interpretable modeling of deeper cognitive biases and related psychological risks remains insufficient. To address this issue, we propose MLBS-Net, a multimodal learning behavior sensing network for teaching feedback that jointly models students’ textual expressions, behavioral sequences, classroom interactions, and psychological auxiliary signals. MLBS-Net integrates theory-guided textual cognitive bias encoding, temporal behavioral state modeling, and reliability-aware cross-modal fusion to capture psychologically interpretable cognitive patterns, characterize dynamic learning-state changes, and adaptively integrate multimodal information according to data quality and task contribution while providing interpretable feedback for teachers. Experimental results show that MLBS-Net achieves a Macro-F1 of 0.855 for cognitive bias recognition and an AUC of 0.891 for psychological risk warning, outperforming traditional machine learning, unimodal deep learning, and standard multimodal methods. Ablation results further support the effectiveness of theory-guided semantic encoding, temporal behavioral modeling, reliability estimation, and multitask learning. These findings demonstrate that MLBS-Net can jointly characterize cognitive biases and potential psychological risks from multisource learning behaviors, providing a feasible approach for learning-state sensing, risk warning, and interpretable teaching support in smart education. Full article

Providing a healthy environment in schools, particularly during a global pandemic, is crucial to saving occupants’ lives and reducing infection rates. This paper proposes a novel desk local exhaust ventilation (DLEV) system that uses a local exhaust diffuser integrated into a classroom desk. The performance of the system in providing a healthy and comfortable indoor thermal environment and reducing the risk of COVID-19 infection was assessed numerically. The assessment combined indoor thermal comfort indices and the bioaerosol dispersion behavior of airborne particles. The study was completed in a typical classroom layout, in which the results show that the DLEV system meets thermal comfort requirements by maintaining the gradients of vertical temperature within an acceptable range. The DLEV system increases the air motion in the breathing zone while keeping it within the recommended range of <0.25 m/s. The PMV and PPD indices are within recommended comfort levels for all but three occupants. Most notably, the DLEV system substantially reduces the concentration of bioaerosols, especially around the occupants’ head. This system works by capturing and removing the virus-rich aerosols exhaled by infected subjects before they disperse in the classroom. This lowers the risk of infection among healthy subjects. These findings confirm the effectiveness of the DLEV system in enhancing both thermal comfort and indoor air quality, making it suitable for environments where specific goals regarding occupants’ health and thermal management are required, such as in a classroom of healthy and infected subjects. Full article

Project-based learning (PBL) holds significant promise as a student-centered instructional approach in secondary mathematics, yet teachers’ capacity to leverage it for diverse learners is deeply shaped by their affective experiences during implementation. This narrative inquiry examines the affective journeys of three high school mathematics teachers as they adopted PBL in classrooms characterized by varied student readiness levels and engagement profiles. We document teachers’ affective journeys as they experience initial PBL awareness, participate in active experimentation, and work toward forming emerging commitments and facilitator identities. Analysis of focus groups and interviews reveals three affective tensions: comfort versus transformative reflection, epistemic ideals versus ontological school realities, and career risk versus identity change. The findings demonstrate how teachers can navigate iterative methods of trial, how teacher educators can better support future teachers’ use of student-centered pedagogies, and how administrators can be aware of the impact that teacher affect can have on performance and pedagogy. Implications address how this broader education community can support differentiated, inquiry-based mathematics instruction. This support can be advanced through scope-controlled projects, micro-evidence portfolios, and incremental adoption pathways. These approaches honor teachers’ emotional labor while supporting their sustained use of student-centered learning strategies intended to broaden opportunities for participation and learning in mathematics classrooms. Full article

Background/Objectives: Problematic internet use (PIU) is a growing behavioral health concern among school-aged children. Whether health literacy and health-promoting lifestyle beliefs are associated with PIU among gifted children—a developmentally distinct population—has, to the best of our knowledge, not been investigated. Methods: A cross-sectional correlational design was used with 118 gifted students (M = 13.80, SD = 2.06) attending a Science and Art Center (BİLSEM) in İzmir, Türkiye. Data were collected using the Problematic Internet Use Scale–Adolescent Form (PIUS-A; α = 0.92), the Health Literacy for School-Aged Children Scale (HLSAC; α = 0.87), and the Adolescent Health-Promoting Lifestyle Beliefs Scale (AHPLBS; α = 0.91) in a classroom setting under researcher supervision. A three-step hierarchical multiple linear regression was performed, with age, sex, daily sleep duration, daily screen time, and weekly physical activity entered as covariates in the first step. Results: Covariates explained 24.8% of the variance in PIU (p < 0.001), with daily screen time as the strongest predictor (β = 0.36). Health-promoting lifestyle beliefs added significant incremental variance (ΔR² = 0.063, p = 0.023); health beliefs were the only statistically significant belief predictor (β = −0.24, p = 0.036), with lower health belief scores associated with higher PIU. Health literacy domains did not add statistically significant incremental variance (ΔR² = 0.028, p = 0.495), and no individual domain was a statistically significant predictor. Conclusions: Within the constraints of a cross-sectional design, health beliefs appear to play a more prominent role than health literacy in the covariate-adjusted explanation of PIU among gifted school-aged children. School health nurses may prioritize interventions that strengthen internalized health beliefs alongside information-based education, contributing to more equitable, tailored school health services for this population. Full article

Vision–language foundation models such as CLIP are increasingly used for zero-shot behavior recognition, yet their robustness to prompt variations remains poorly understood. This paper investigates prompt sensitivity as a critical robustness concern in CLIP-family models for zero-shot classroom behavior analysis, treating prompt wording as a controlled semantic perturbation. Five representative vision–language models (CLIP/OpenAI, OpenCLIP/LAION, SigLIP2, EVA02-CLIP, and DFN-CLIP) are evaluated on three public classroom behavior benchmarks under a strict symmetric protocol. We compare four generic prompt strategies with a training-free Class-Aware Prompt Ensemble (CAPE). Results show that minor prompt changes can cause catastrophic performance degradation. On SigLIP2, an alternative wording of CAPE reduces Hit@1 on TeacherBehavior from 85.5% to 31.4%, a 54.1 percentage-point drop that exceeds the differences between model backbones. Across all five models, action-oriented prompts improve Hit@1 by up to 54 percentage points compared with label-only prompts. We further demonstrate that the apparent superiority of zero-shot CLIP over supervised linear probes largely arises from metric asymmetry. While zero-shot methods achieve higher Hit@1, they consistently underperform linear probes in multi-label evaluation (Sample-F1: 60–66% vs. 88–90%; Macro-F1: 49–60% vs. 68–78%). Bootstrap confidence intervals and paired-bootstrap significance tests further show that several reported performance differences are not statistically significant. These findings reveal prompt sensitivity as a fundamental deployment risk for vision–language foundation models in domain-specific behavior analysis. Prompt variations involving only a few words can silently undermine recognition performance while remaining hidden by conventional evaluation metrics. We therefore recommend that future benchmark studies report prompt configurations, multi-label F1 scores, and uncertainty estimates alongside headline Hit@1 to provide a more complete and reliable assessment of model capability. Full article

The global water crisis represents one of humanity’s most pressing challenges, with over 2 billion people lacking access to safely managed drinking water. This study presents the implementation and evaluation of an innovative air-conditioning condensate recovery system at Building F of the Faculty of Electrical and Electronic Engineering (FIEE), Universidad Veracruzana, Mexico. The system integrates twenty-six 24,000 BTU air-conditioning units across twelve classrooms and two laboratories, recovering approximately 520 L of condensate water daily. An initial physicochemical characterization of the recovered condensate was conducted through pH, electrical conductivity (EC), and total dissolved solids (TDS) measurements. In addition, the dried residue obtained after evaporation of the condensate was examined using semi-quantitative X-ray fluorescence (XRF) analysis. The XRF results describe the relative elemental composition of the dried residue and must not be interpreted as aqueous concentrations or as evidence of compliance with water-quality standards. The recovery system includes a nominal 0.5 µm polypropylene sediment cartridge, activated-carbon filtration, and a Crystolite^® treatment medium. Because paired measurements before and after treatment were not performed, the removal efficiencies of these components were not determined. The recovered water is subsequently stored and processed in a dual-tank configuration: a primary 3300 L storage system and a secondary 200 L tank used to prepare fertilizer-amended condensate for ornamental-plant irrigation. A fully water-soluble monopotassium phosphate fertilizer (MKP, 0 (–52–34) was incorporated at a gravimetric proportion of 1:10 (1 g MKP per 10 g recovered condensate water). Full article

Mixed Reality (MR) serious games combine immersive technologies with game-based learning to support training, skill development, and decision-making across diverse disciplines in university education. Although previous research has demonstrated improvements in engagement, usability, and learning outcomes, less attention has been devoted to developing design recommendations and methodological approaches for studying MR serious games. A theory-derived Meta Quest 3-based MR serious game for diagnostic classification was developed by integrating learning theories, gameplay mechanics, and gamification features while functioning as both an educational intervention and a research instrument. The system was evaluated through two complementary studies involving undergraduate students: the first compared learning outcomes across instructional approaches, whereas the second examined learner performance and transfer using a mixed-method approach incorporating objective metrics, questionnaires, and interviews. The proposed system achieved learning outcomes comparable to expert-guided field training while significantly outperforming classroom instruction and self-directed study. Significant transfer to real-world diagnostic tasks was demonstrated, and complementary evidence was triangulated to derive preliminary design recommendations and a multi-dimensional methodological framework. These contributions provide an initial foundation for the systematic design and study of MR serious games in higher education. Full article

This study examined pre-service elementary school teachers’ perceptions of a semester-long professional learning programme in Iran, focusing on perceived shifts in their concerns, drawing on the Concerns-Based Adoption Model (CBAM) as the theoretical framework. Adopting a CBAM-informed, directed (deductive–inductive) qualitative content analysis of the written reflections of 30 pre-service teachers across two provinces, the study traced perceived shifts in participants’ concerns throughout their professional learning journey. Consistent with CBAM’s developmental stages, the analysis of participants’ reflections suggested a perceived transition from self-focused concerns to concerns about teaching effectiveness. This shift—from self-oriented concerns toward task- and effectiveness-oriented concerns—aligns with the model’s prediction that individuals move through self, task, and effectiveness concerns as they adopt new practices. The findings also suggest that this progression is associated with the development of professional identity, personal beliefs, and the capacity for lifelong learning. Although the training process placed many participants in unfamiliar contexts, it provided valuable experiences that encouraged active engagement in curriculum design. Despite facing uncertainty and challenges, participants developed the psychological readiness to continue their professional journey. Because the study is based on self-reported written reflections rather than classroom observation, teaching performance, or pupil outcomes, the findings are presented as perceived shifts in pre-service teachers’ reflections, concerns, and professional readiness, rather than as demonstrated impact on attitudes, practices, or teaching outcomes. Overall, these findings underscore the relevance of CBAM for understanding and informing the design of professional development for pre-service teachers. Full article

This study used archival data from 30,453 students enrolled in 289 adult education programs in the state of Georgia, United States during the 2018–2019 school year. We explored how student-, teacher-, and classroom-level factors predict student attendance and academic performance (reading, language, and math) over time. Results indicated several student-level factors predict attendance and educational performance, including age, gender, race/ethnicity, previous educational attainment, low-income status, special needs status, reported barriers to education, and dual enrollment. Depending on the academic subject, age, race, previous education, special needs, and dual enrollment predicted academic growth over time. Teacher-level factors such as teacher experience predicted educational performance; classroom factors such as whether the course was an Integrated Education and Training (IET) class predicted attendance, and distance classes predicted attendance and performance. Certification predicted growth over time for language, and employment status and designation as a distance class predicted growth over time for math. These findings will help state officials improve the delivery of services to further enhance and improve student outcomes. Full article

Indoor positioning using commodity Wi-Fi infrastructure is attractive for smart buildings and Internet of Things applications, but the practical accuracy of Wi-Fi Fine Timing Measurement (FTM) remains limited by non-line-of-sight propagation, multipath delay, access-point-dependent ranging bias, and unstable anchor geometry. This paper proposes a measurement-reliability learning and geometry-constrained fusion framework, termed MRL-GCF, for robust horizontal Wi-Fi FTM indoor localization. MRL-GCF learns the reliability of each access-point observation from a multi-factor representation that includes Received Signal Strength Indicator (RSSI), logarithmic FTM range, short-window range stability, RSSI fluctuation, access-point visibility, abnormal-range tendency, and coarse anchor geometry. A lightweight heteroscedastic neural calibrator estimates both range bias and observation uncertainty. A supervised reliability-regime head is further trained from residual-regime soft targets, and its entropy is used as a propagation-ambiguity measure. The learned uncertainty is fused with propagation ambiguity, map obstruction, material-aware obstruction cues, and anchor geometry to select reliable anchors and construct a trust-weighted nonlinear least-squares localization objective. To avoid overestimating performance from repeated scans at identical survey points, both scan-level and point-held-out protocols were adopted. Experiments were conducted in a lobby, a classroom, and a dormitory using 4410 synchronized RSSI-FTM scans. On 882 scan-level test queries, MRL-GCF achieved mean absolute errors of 0.88 m, 0.55 m, and 1.20 m, with sub-3 m success rates of 98.0%, 99.0%, and 96.5%, respectively. Additional replay-based dynamic, temporal, cross-device, AP-density, uncertainty-calibration, map-availability, and coefficient-sensitivity analyses were included to examine deployment-oriented robustness. These results indicate that learning measurement reliability while preserving geometric constraints provides a practical and interpretable solution for robust Wi-Fi FTM indoor positioning. Full article

Low-latitude plateau classrooms, such as those in Kunming, experience intense solar radiation that often causes insufficient far-window illumination, excessive near-window brightness, and viewing-direction glare under side-lighting conditions. To investigate this spatial imbalance, field surveys of nine classrooms were used to define realistic parameter ranges, while 100 parametric design cases were evaluated through annual simulation, machine learning, and SHAP analysis. The viewing-direction glare model achieved a test-set R² of 0.819, indicating adequate predictive performance for factor interpretation. Compared with simply increasing the window-to-wall ratio (WWR), coordinated control of classroom geometry, window configuration, and surface reflectance produced a more balanced luminous environment. Daylight availability and excessive illuminance were primarily governed by WWR and window reveal depth, whereas glare was more strongly influenced by seating position, viewing direction, window width, and orientation. Classroom-wide averages may therefore conceal localized glare experienced by students. A moderate WWR of 0.26–0.40 combined with a window reveal depth of 0.75–1.17 m emerged as a preferable strategy within the investigated design space. These findings support desktop-level daylight assessment and student-perspective glare evaluation in the design and renewal of ordinary side-lit classrooms in Kunming and comparable low-latitude plateau regions. Full article