Search Results (209)

This paper describes a small, quasi-experimental, mixed method study investigating teacher and child outcomes for a preschool data science intervention condition compared to a business-as-usual comparison condition. The intervention included both hands-on activities and a free digital tool called the Preschool Data Toolbox to engage young children in foundational data science activities. The intervention activities aligned with a learning blueprint that articulated a set of early childhood data science learning goals based on K-12 computer science and early mathematics standards. The intervention supports teachers to implement foundational data science investigations using an intuitive tablet app that scaffolds the DS process through structured and open-ended instructional experiences. Findings from classroom observations, teacher surveys, and interviews indicate high feasibility and engagement, with teachers reporting ease of use, developmental appropriateness, and positive impacts on children’s data acumen and math skills (n = 217). After controlling pre-test scores, children who participated in the intervention demonstrated statistically higher post-test scores (p = 0.001) compared to those in the comparison group, highlighting the effectiveness of the program in fostering early STEM skills. The study underscores the potential of developmentally appropriate DS experiences to foster early learning, support teacher confidence, and prepare children for future academic success, while highlighting the need for further research and professional development to scale such interventions effectively. Full article

Although Latin American countries have seen major advances in coverage and school attendance, there are still important geographical differences in educational quality, leading to inequalities. The objective of this study is to determine the influence of geographical context on academic achievement among primary school students in Chile. The methodology involves the estimation of spatial econometric models, specifically, an analysis of spatial dependence including the Moran index, New-GI tests and substantive and residual autocorrelation tests. The data used correspond to standardized test scores obtained from 4030 schools in Chile between 2014 and 2017. The results demonstrate the existence of spatially dependent effects on academic performance for both reading and math. The main indirect spatial effects arise from the concentration of indigenous and immigrant populations. There is also evidence of high spatial inequality in educational quality, as measured through Education Quality Measurement System (SIMCE) tests. Full article

Conventional teacher assessment is labor-intensive and subjective. Prior LLM-based systems improve scale but rely on post hoc rationales and lack built-in trust controls. We propose an explainable-by-design framework that couples (i) Dual-Lens Hierarchical Attention—a global lens aligned to curriculum standards and a local lens aligned to subject-specific rubrics—with (ii) a Trust-Gated Inference module that combines Monte-Carlo-dropout calibration and adversarial debiasing, and (iii) an On-the-Spot Explanation generator that shares the same fused representation and predicted score used for decision making. Thus, explanations are decision-consistent and curriculum-anchored rather than retrofitted. On TeacherEval-2023, EdNet-Math, and MM-TBA, our model attains an Inter-Rater Consistency of 82.4%, Explanation Credibility of 0.78, Fairness Gap of 1.8%, and Expected Calibration Error of 0.032. Faithfulness is verified via attention-to-rubric alignment (78%) and counterfactual deletion tests, while trust gating reduces confidently wrong outputs and triggers reject-and-refer when uncertainty is high. The system retains 99.6% accuracy under cross-domain transfer and degrades only 4.1% with 15% ASR noise, reducing human review workload by 41%. This establishes a reproducible path to trustworthy and pedagogy-aligned LLMs for high-stakes educational evaluation. Full article

Transportation systems are particularly vulnerable to disruptions such as pandemics, which create significant challenges for maintaining efficiency, safety, and service quality. This study focuses on rail passenger transport in the Slovak Republic and develops a simulation framework to evaluate system performance under crisis conditions. Weekly data from the national rail operator for the period 2019–2021 were combined with information on governmental restrictions, standardized into a five-level framework. A nonlinear autoregressive model with exogenous inputs (NARX), implemented and validated in MATLAB R2021b (MathWorks, Natick, MA, USA), was applied to simulate the impact of restrictive measures on passenger demand. The results revealed a strong relationship between the severity of measures and ridership levels, with the most significant effects observed in education, workplace access, movement limitations, and retail. For instance, during complete school closures, passenger volumes declined by up to 75% relative to the pre-pandemic baseline. Based on the simulation outcomes, recommendations were formulated for adapting railway operations, including dynamic adjustments of transport capacity (10–40%) according to restriction levels. The proposed modelling and simulation approach offers transport authorities a cost-effective tool for scenario testing, disruption management, and the design of resilient passenger rail systems capable of adapting to crises and uncertainties. Full article

This study examines how domain-general (processing speed and receptive vocabulary) and domain-specific (symbolic and non-symbolic comparison) cognitive skills contribute to early informal mathematical thinking in preschoolers. The aim was to assess the invariance of these predictive relationships across two sociocultural contexts: Chilean and Spanish samples. A total of 130 children participated, and structural equation modeling was used to estimate latent structures and test multigroup invariance. The results revealed a consistent latent structure across samples and a significant contribution of symbolic and non-symbolic comparison to early math performance, while processing speed and vocabulary showed context-specific variations. These findings indicate that although foundational mathematical competencies rely on common cognitive mechanisms, cultural and educational contexts modulate the strength of these associations. This study contributes to understanding the cognitive architecture underlying early numeracy and highlights the importance of culturally sensitive assessment and intervention strategies. Full article

Mathematical performance in early education is influenced by a complex interplay of cognitive and affective factors, including language skills, working memory, and anxiety. This study investigated whether working memory and math anxiety, in both explicit numerical situations (ENS) and general classroom situations (GCS), mediate the relationship between general and math-specific vocabulary and math performance in a sample of 467 second-grade students in Chile. Structural equation modelling was employed to test a dual-pathway model in which both working memory and math anxiety served as mediators between vocabulary knowledge and math performance. Results indicated that both general and math-specific vocabulary positively predicted working memory and negatively predicted math anxiety in ENS. In turn, working memory and ENS significantly predicted math outcomes, whereas GCS was not a significant predictor. Indirect effects supported a dual mediation structure, with vocabulary influencing math performance through both cognitive and affective mechanisms. Math-specific vocabulary exerted a slightly stronger total effect than general vocabulary, consistent with its closer alignment to the semantic demands of mathematical tasks. These findings suggest that vocabulary supports early mathematical learning not only by enhancing cognitive processing capacity but also by reducing anxiety in task-specific contexts. Full article

This study investigates the effectiveness of integrating explicit learning-strategy instruction into gatekeeper mathematics courses to foster a math growth mindset, self-regulated learning (SRL), and improved academic performance among underrepresented minority students. The intervention was implemented across four key courses—College Algebra I/II and Calculus I/II—and incorporated evidence-based cognitive, metacognitive, and behavioral learning strategies through course materials, class discussions, and reflective assignments. Grounded in a conceptual framework linking learning-strategy instruction, growth mindset, SRL, and performance—while accounting for students’ social identities—the study explores both direct and indirect effects of the intervention. Using an explanatory sequential mixed-methods design, we first collected quantitative data via pre- and post-surveys/tests and analyzed performance outcomes, followed by qualitative focus groups to contextualize the findings. Results showed no significant effects of the intervention on growth mindset or SRL, nor evidence of mediation through these constructs. The direct effect of the intervention on performance was negative, though baseline mindset, SRL, and pre-course preparedness strongly predicted outcomes. No moderation effects were detected by student identities. The findings suggest that while explicit learning-strategy instruction may not independently shift mindset or SRL in the short term, pre-existing differences in these areas are consequential for performance. Qualitative findings provided further context for understanding how students engaged with the strategies and how instructor implementation shaped outcomes. These insights inform how learning strategies might be more effectively embedded in introductory math to support success and equity in STEM pathways, particularly in post-COVID educational contexts. Full article

This study investigates the causes of mathematics anxiety, its effect on mathematical performance, and the influence of field placement among preservice teachers enrolled in a field-based elementary mathematics methods course. To investigate this, the study utilized four tools: the Abbreviated Mathematics Anxiety Rating Scale (A-MARS), concept maps, Praxis Core test score, and questionnaires. The quantitative analysis indicates a negative correlation between anxiety and math performance. The analysis of concept maps and questionnaires indicate that key contributors to math anxiety include pop-up quizzes, tests, exams, memorization of mathematical ideas, and past negative experiences with school mathematics. The qualitative data analysis revealed that reduced mathematics anxiety during field placements was primarily due to practical teaching experience, constructive feedback, positive student interactions, and opportunities for observation and reflection. Understanding the root causes of mathematics anxiety is essential for supporting preservice teachers and improving their teaching effectiveness. Additionally, field placements play a crucial role in reducing math anxiety by providing hands-on teaching experience and building confidence. It is important to alleviate math anxiety in preservice teachers to have a more positive impact on their future students. Full article

The Advanced Encryption Standard (AES) symmetric encryption algorithm plays a crucial role in data encryption. To address the limitations of the fixed Substitution-box (S-box) and static key expansion strategy in AES. This paper proposes an improved AES scheme that integrates a dynamic S-box structure with a key expansion mechanism based on dynamic perturbations. The dynamic S-box is generated by selecting affine transformation pairs and irreducible polynomials, and its cryptographic properties are tested in SageMath9.3 to obtain a set of S-boxes superior to the standard AES. In the key expansion process, the perturbation values generated by the hash function will be incorporated into the round key generation process to reduce the correlation between round keys. The improved AES algorithm, when applied to Over-the-Air (OTA) systems, not only achieves significant savings in storage resources of in-vehicle Electronic Control Units (ECUs) but also enhances the security of OTA communications. Furthermore, it consumes only a small amount of ECU computational resources, thereby effectively meeting the lightweight requirements of in-vehicle electronic control units. Full article

Background/Objectives: The objective of this study was to validate a new parent-reported scale for tracking Pediatric Acute-onset Neuropsychiatric Syndrome (PANS). PANS is a condition characterized by a sudden and severe onset of neuropsychiatric symptoms. To meet diagnostic criteria, an individual must present with either obsessive–compulsive disorder (OCD) or severely restricted food intake, accompanied by at least two additional cognitive, behavioral, or emotional symptoms. These may include anxiety, emotional instability, depression, irritability, aggression, oppositional behaviors, developmental or behavioral regression, a decline in academic skills such as handwriting or math, sensory abnormalities, frequent urination, and enuresis. The onset of symptoms is usually triggered by an infection or an abnormal immune/inflammatory response. Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcal Infections (PANDAS) is a subtype of PANS specifically linked to strep infections. Methods: We developed a 101-item PANS/PANDAS and Related Inflammatory Brain Disorders Treatment Evaluation Checklist (PTEC) designed to assess changes to a patient’s symptoms over time along 10 subscales: Behavior/Mood, OCD, Anxiety, Food intake, Tics, Cognitive/Developmental, Sensory, Other, Sleep, and Health. The psychometric quality of PTEC was tested with 225 participants. Results: The internal reliability of the PTEC was excellent (Cronbach’s alpha = 0.96). PTEC exhibited adequate test–retest reliability (r = 0.6) and excellent construct validity, supported by a strong correlation with the Health subscale of the Autism Treatment Evaluation Checklist (r = 0.8). Conclusions: We hope that PTEC will assist parents and clinicians in the monitoring and treatment of PANS. The PTEC questionnaire is freely available at neuroimmune.org/PTEC. Full article

The Every Student Succeeds Act requires that U.S. states provide a public evaluation of the performance of each public school while providing broad discretion in how states devise performance frameworks. One common method consists of states assigning each school an A–F letter grade based on English and math proficiency rates and other measures of academic performance. Proponents of the summary letter-grade system cite its simplicity as a virtue, while detractors contend that the system is simplistic to a fault. To bring greater clarity to these ongoing debates, we solicited opinions from parents regarding state letter-grade systems. We conducted semi-structured focus groups with parents in Arizona, North Carolina, and Texas (three focus groups per state). These conversations revealed that most parents were not aware that the state grades schools. Once the performance framework was explained, most parents expressed a belief that it is overly simplistic and insufficiently deferential to what they perceive as the subjective nature of school quality. Parents also revealed substantial tension between their conception of school quality and the way it is operationalized in the report card, with the latter ascribing much greater importance to state test scores. Full article

Plant responses to heat stress include complex transcriptional networks and protein regulations in which BTB/POZ-MATH (BPM) proteins participate as a part of ubiquitin-mediated protein degradation. Arabidopsis thaliana contains six BPM genes involved in responses to environmental changes, including heat. Seedlings overexpressing BPM1 (oeBPM1), seedlings with downregulation of BPM1, 4, 5, and 6 (amiR-bpm) and wild type were exposed to 37 °C for 6 h. Treatment caused stronger decline of photosynthesis in oeBPM1 than in amiR-bpm and wild type, although all seedlings recovered after 24 h at 24 °C. The activity of the antioxidant enzymes catalase, guaiacol peroxidase, and ascorbate peroxidase remained unchanged in oeBPM1, but increased in amiR-bpm and wild type. Heat stress induced HSP70 and HSP90 in all seedlings but expression remained notably higher in amiR-bpm after recovery. DREB2A and HSFA3 expression increased in all seedlings immediately after stress, with the strongest induction in amiR-bpm. In amiR-bpm and wild type, BPM2 expression was induced immediately after exposure, while BPM1, BPM3, BPM4, and BPM6 were upregulated in wild type after recovery. In oeBPM1 seedlings, BPM4 expression decreased and BPM6 expression increased immediately after treatment at 37 °C for 6 h. The results suggest that BPM proteins modulate heat stress response by influencing photosynthesis, activation of antioxidant enzymes, accumulation of HSPs, and expression of heat-responsive genes, thus contributing to the different physiological strategies observed in A. thaliana lines with altered expression of BPM genes. Full article

Understanding how students’ online homework behaviors relate to their academic success is increasingly important, especially in elementary education where such research is still emerging. In this study, we examined three years of online homework data from fourth-grade students enrolled in an after-school math program. Our goal was to see whether certain behaviors—like how soon students started their homework, how many times they tried to solve problems, or whether they uploaded their written work—could help explain differences in homework completion and test performance. We used multiple regression analyses and found that some habits, such as beginning homework soon after class and regularly attending lessons, were consistently linked to better homework scores across all curriculum levels. Test performance, however, was harder to predict and showed fewer consistent patterns. These findings suggest that teaching and encouraging specific online study behaviors may help support younger students’ academic growth in digital learning environments. Full article

Dealing with mathematics can induce significant anxiety, affecting academic performance: this phenomenon is known as Math Anxiety (MA). While math anxiety scales were mostly developed in English, some have been translated and validated for Italian populations (e.g., the Abbreviated Math Anxiety Scale). This study translated the 3-factor MAS-UK scale into Italian, producing a new tool, MAS-IT, which was validated in a sample of 324 Italian psychology undergraduates. Confirmatory Factor Analysis (CFA) tested the original MAS-UK 3-factor model and revealed that it did not fit the MAS-IT data. A subsequent Exploratory Graph Analysis (EGA) identified four distinct factors of math anxiety in MAS-IT. The “Passive Observation MA” factor remained stable across the analyses, whereas the “Evaluation MA” and “Everyday/Social MA” items showed poor stability. These quantitative findings suggest potential cultural or contextual differences in the expression of math anxiety among today’s psychology undergraduates, highlighting the need for more appropriate assessment tools tailored to this population. Full article

This study explored innovative methods for teaching mathematics to seventh-grade students with persistently low performance by using an AI-driven neural network approach, specifically focusing on solving first-degree inequalities. Guided by the Response to Intervention (RTI) framework, the intervention aimed to reduce math anxiety and build academic resilience through the development of cognitive and metacognitive strategies. A rigorous pre- and post-test design was employed to evaluate changes in performance, anxiety levels, and resilience. Fifty-six students participated in the 12-week program, receiving personalized instruction tailored to their individual needs. The AI tool provided real-time feedback and adaptive problem-solving tasks, ensuring students worked at an appropriate level of challenge. Results indicated a marked decrease in math anxiety alongside significant gains in cognitive skills such as problem-solving and numerical reasoning. Students also demonstrated enhanced metacognitive abilities, including self-monitoring and goal setting. These improvements translated into higher academic performance, particularly in the area of inequalities, and greater resilience, highlighting the effectiveness of AI-based strategies in supporting learners who struggle persistently in mathematics. Overall, the findings underscore how AI-driven teaching approaches can address both the cognitive and emotional dimensions of mathematics learning. By offering targeted, adaptive support, educators can foster a learning environment that reduces stress, promotes engagement, and facilitates long-term academic success for students with persistently low performance in mathematics. Full article