Search Results (302)

Perishable products are usually priced in real-time to volatile market environments, thereby optimizing inventory control, minimizing resource wastage, and maximizing corporate profitability. Based on the public dataset from the 2023 Higher Education Press Cup National College Students Mathematical Modeling Competition, this paper addresses the challenge of multi-product joint pricing for perishable fresh produce and proposes an attention-augmented proximal policy optimization algorithm (termed ATT-PPO), which embeds an attention mechanism into the proximal policy optimization (PPO) framework. The integrated attention mechanism confers three core advantages to the model: first, it dynamically captures inter-product interdependencies, enabling an accurate reflection of cross-price elasticity and demand correlations; second, it reduces feature redundancy and computational overhead in multi-product collaborative pricing strategies; third, it enhances both the interpretability and computational efficiency of the model. Experimental results demonstrate that in the scenario of multi-product pricing, the ATT-PPO algorithm achieves competitive performance compared to PPO, DDPG (Deep Deterministic Policy Gradient), SAC (Soft Actor-Critic), and TD3 (Twin Delayed Deep Deterministic Policy Gradient), with the key advantage lying in its ability to provide interpretable attention weights that reveal dynamic cross-product dependencies in pricing decisions. This study not only expands the applicability of DRL (Deep Reinforcement Learning) to practical economic problems in the fresh produce sector but also provides valuable theoretical insights that can be generalized to other short-lifecycle product domains, including fashion apparel and consumer electronics. Full article

The rapid integration of artificial intelligence (AI) technologies into educational contexts has introduced innovative instructional approaches, particularly within Science, Technology, Engineering, and Mathematics (STEM) education. Although an increasing number of empirical studies have examined AI-supported instruction, existing findings remain heterogeneous, making it difficult to draw firm conclusions about its overall effectiveness. This study aims to systematically synthesize experimental and quasi-experimental research on AI-supported instructional interventions in STEM education, quantify their overall effects on student learning outcomes, and examine potential moderating factors, including educational level, STEM discipline, and intervention duration. A comprehensive systematic literature search was conducted across Web of Science, Scopus, ERIC, ScienceDirect, and Google Scholar, covering studies published between 2005 and 2025. A total of 35 studies meeting predefined inclusion criteria were included in the meta-analysis. Effect sizes were calculated using Hedges’ g, and a Random Effects Model (REM) was employed to account for heterogeneity among studies. Moderator analyses were conducted for educational level, STEM discipline, and intervention duration. Publication bias was assessed using multiple diagnostic methods. The meta-analysis revealed a statistically significant overall positive effect of AI-supported instruction on student learning outcomes in STEM education (g = 0.67, 95% CI [0.49, 0.85], p < 0.001). Moderator analyses indicated that AI interventions were most effective at the high school level. Although Science and Mathematics disciplines showed slightly higher effect sizes, the between-group difference was not statistically significant (Q = 4.85, df = 2, p = 0.088). Regarding intervention duration, the highest effect size was observed in interventions lasting more than one month and up to two months, though no consistent pattern of increasing effectiveness with longer durations was found. Publication bias analyses suggested minimal influence on the overall findings. AI-supported instructional interventions demonstrate a moderately to highly positive impact on student learning outcomes in STEM education. The effectiveness of these interventions varies according to educational level, disciplinary context, and intervention duration. These findings provide robust empirical evidence supporting the pedagogical value of AI in STEM education and offer guidance for educators and policymakers regarding effective implementation. Full article

Understanding advanced pre-tertiary mathematics, particularly three-dimensional vectors, demands robust spatial reasoning skills that many students find challenging to develop through traditional pedagogical methods. This study proposes and evaluates an innovative educational tool that leverages large vision models to automate the conversion of handwritten vector equations into accurate 3D graphical representations. By interpreting students’ handwritten input using advanced computer vision, the system provides immediate, interactive visual feedback to bridge the cognitive gap between abstract symbolic notation and tangible geometric concepts. We evaluated the system using a dataset of 1000 handwritten vector equations typical of the Singapore-Cambridge GCE ‘A’ Level H2 Mathematics syllabus. Our findings demonstrate that while GPT-4o serves as a capable baseline, achieving 84.6% accuracy with multi-shot prompting, newer variants such as GPT-4.1-mini offer superior performance, reaching 91.4% accuracy with significantly higher computational efficiency. The results confirm that AI-powered visualisation tools can effectively interpret complex spatial mathematical layouts when guided by optimal prompt engineering. Implementing such technology in educational settings presents a viable, scalable, and cost-effective method to democratise learning support, fostering independent study and enhancing students’ conceptual comprehension of spatial mathematics. Full article

Cariño (care) should be central to equity-centered transformation in science, technology, engineering, and mathematics (STEM) higher education. Yet, relational leadership practices that prioritize culturally grounded care—such as cariño—are often absent in STEM initiatives, leaving unexamined how Women of Color (WOC) enact these practices to advance equity for historically marginalized students. Employing a qualitative methodology grounded in Chicana Feminist Epistemology, in-depth interviews were conducted with five WOC leading a multi-institutional, federally funded STEM initiative. Analysis revealed four interrelated dimensions of what we are calling “Cariño Competence”: (1) relational attunement grounded in moral obligation, (2) protective action when project systems fail students, (3) boundary-setting as care and resistance to extractive labor, and (4) community-sustained resilience through networks of WOC leaders. The findings offer a data-driven theorization of Cariño Competence, capturing how WOC operationalize culturally grounded care as a strategic, protective, and resistive praxis. By centering students as the moral and epistemic anchor of leadership decisions, this study demonstrates how relational, culturally sustaining practices can humanize bureaucratic systems, buffer harm, and advance systemic transformation in STEM higher education. These insights contribute to scholarship on culturally responsive leadership and provide a practical framework for advancing equity, inclusion, and empowerment in higher education contexts. Full article

This study examines failure in introductory programming courses, commonly known as CS1, in Chilean higher education by combining academic trajectory analysis with early-risk prediction models. We analyzed a cohort of 994 students from a Chilean technical university enrolled during the first academic semester of 2025, with a 46% failure rate, integrating pre-university academic and admission variables (e.g., mathematics and language indicators, as well as baseline diagnostic measures when available), sociodemographic information, and within-semester performance indicators. Group differences were assessed using non-parametric tests, and predictive performance was evaluated under two realistic information-availability scenarios: (i) pre-university variables only and (ii) variables available up to the first major written examination (C1). The results show statistically significant differences between students who passed and those who failed, with indicators of quantitative preparedness and, most notably, C1 performance emerging as the strongest signals of risk. In the pre-university scenario, models achieved acceptable discrimination (AUC ≈ 0.77), whereas incorporating C1 substantially improved discriminative performance (AUC ≈ 0.92) and increased precision in identifying at-risk students while reducing false positives. These findings support a staged institutional strategy: broad, low-cost preventive support before the semester begins, followed by more targeted and intensive interventions after C1, thereby enabling more efficient early-warning systems in high-stakes first-year courses. Full article

The emergence of massive knowledge in online learning systems has increased the difficulty for learners to acquire the necessary information. Due to unclear information expression and excessive knowledge redundancy, learners face challenges in identifying relevant knowledge. Furthermore, the presence of substantial unstructured knowledge in subject domains also hinders the effective transmission and application of knowledge. To address these issues, a framework for constructing a subject domain knowledge graph is proposed in this work. The framework primarily aims to visualize isolated information and connect knowledge into graph structures. The knowledge graph can help learners quickly and efficiently acquire the knowledge they need. The novel framework is constructed with three steps. The first step is to design the ontology rules based on the domain-specific subject knowledge from the perspective of classification, and also to construct the schema layer of the knowledge graph. The second step is to propose a domain-optimized BiLSTM-ResNet-CRF model for subject domain entity recognition, which introduces residual blocks to enhance fine-grained local contextual feature extraction for multi-word technical terms, addressing the limitations of traditional BiLSTM-CRF models in educational text processing. The BERT relation extraction model is used to extract relations between knowledge entities. Then the data layer is constructed. Finally, the third step is to achieve knowledge fusion through entity linking and two-layer entity alignment against results stored in a database. The result comparisons on the dataset show that the novel BiLSTM-ResNet-CRF model has higher scores than several other classical models, achieving an F1-score of 80.26%. The proposed framework’s effectiveness is rigorously validated using high school mathematics as a representative case study with a well-structured knowledge system. Full article

Studies explaining the variation in educational outcomes across countries, when based on “black box” models that provide high accuracy but struggle to present the decision-making mechanism transparently, carry the risk of producing limited interpretations for policy discussions. This study examines the system-level relational patterns through which the subcomponents of the Heritage Foundation Index of Economic Freedom distinguish country-average low–medium–high PISA performance profiles in mathematics, reading, and science, and interprets these patterns using machine learning and explainable artificial intelligence (XAI). The analysis draws on approximately twenty years of nominal country-year records covering 76 countries. The study design proceeds through a classification approach, treating country performance as low–medium–high profiles; thus, model outputs are presented on an interpretable reference plane for cross-country comparisons. The findings indicate that the models demonstrate consistent generalization ability in distinguishing performance profiles and that the XAI layer produces explanations that make the model’s reasoning visible in a verifiable manner. The explanation results indicate that components representing institutional trust (such as government integrity and property rights) produce strong, recurring signals alongside higher performance profiles in all three areas; while components such as public expenditure and tax burden can emerge as balancing/suppressing signals in some scenarios. Rather than offering causal policy implications, these findings transparently reveal the structural areas that stand out in distinguishing performance profiles in cross-country comparisons, thus providing an explainable, replicable evidence base for comparative analysis and further research. Full article

This study examined how gender-based leadership in Science, Technology, Engineering and Mathematics (STEM) higher education (HE) is understood, managed, and reappraised within the contrasting settings of the UK and Ghana. A comparative qualitative approach was deployed in the study using insights from twenty semi-structured interviews comprising women STEM academics, and four focus groups consisting of two mixed staff UK-Ghana, and two Ghana student groups. The study adopted Braun and Clarke’s reflexive thematic method, combined with an abductive, cross-context reading to grasp both convergence and difference in participants’ perspectives. Findings produced three inter-connected themes: Power and Prestige in academic leadership, Progression Pathways and Barriers, and Institutional Disruption and Emerging Change Agents. Emerging tides of change such as deliberate recruitment of women, sponsorship beyond traditional mentoring, fairer decision-making panels, and flexible work arrangements, were also found to help recast leadership as more relational, inclusive, and dynamic. While both contexts shared structural roots of inequity, their emphasis diverged. In the UK, policy frameworks often outpaced cultural change; in Ghana, resource limitations remained pressing. This investigation develops an empirically grounded, cross-context articulation of the Power-Progression-Disruption framework, showing how gendered power, progression barriers, and institutional change practices interact to shape STEM leadership, and concludes with a set of adaptable strategies. Full article

Students entering engineering programs often exhibit insufficient mathematics knowledge and considerable variability in prior training, which can create learning gaps and challenges for higher education integration. This study aims to characterize students’ mathematics proficiency at the Coimbra Institute of Engineering and to develop strategies to address these gaps. A diagnostic test was designed based on the Portuguese primary and secondary education syllabus and the guidelines of the European Society for Engineering Education. Data were collected from students enrolling in engineering degrees between the 2013/14 and 2021/22 academic years. Based on the diagnostic results, a targeted intervention was implemented to motivate students and enhance their learning in mathematics. This intervention includes complementary teaching methodologies applied to Differential and Integral Calculus, a mandatory first-year course across all engineering programs. The analysis demonstrates that the combined approach of diagnostic assessment and targeted support improves student engagement and addresses disparities in prior knowledge. This study contributes to the development of evidence-based strategies that support equitable learning opportunities in engineering education and offers a model for integrating diagnostic assessment with active learning practices in foundational STEM courses. Full article

There is limited understanding regarding whether and how teachers’ talent beliefs evolve across career stages. While most prior research conceptualizes talent beliefs across domains, emerging frameworks emphasize field-specific talent beliefs. An established multidimensional model of talent beliefs provides a theoretically grounded structure for capturing these domain-specific perceptions. Yet comparative evidence across teacher career stages remains limited. Our study examines if verbal and mathematical talent beliefs among in-service teachers and pre-service teachers differ in terms of sources, structure and levels. A total of 307 in-service teachers and 215 pre-service teachers completed validated six-dimensional talent beliefs instruments for both domains and reported sources of their beliefs. Participants—especially pre-service teachers—most strongly attributed their talent beliefs to personal school experiences, while educational science and subject-didactic coursework played a marginal role. Both the mathematical and verbal talent belief scales demonstrated configural and metric invariance, supporting equivalent factor structures and factor loadings across pre-service teachers and in-service teachers. Latent mean comparisons showed that pre-service teachers hold systematically different talent beliefs in comparison to in-service teachers. In-service teachers emphasize talent beliefs concerning domain-specific skills and, for verbal talent, passion—consistent with contemporary talent development frameworks—whereas pre-service teachers focus on external teacher influence and, for mathematical talent, on internal factors. These findings reinforce theoretical claims that talent beliefs are experience-sensitive, multidimensional constructs shaped through socialization in educational contexts. Teacher (further) education should deliberately address the dominance of personal schooling experiences by fostering structured reflection, explicitly targeting belief formation in practice-based courses, and ensuring coherence between higher-education instruction and school-based experiences. Teachers’ impact on their students’ talent development should especially be reflected in further education, since in-service teachers assess their own influence as lower than pre-service teachers do; additionally, passion as a key driver of talent development and the relevance of talent domains should already be highlighted in initial teacher education. Full article

The present study investigated the relationship between resilience, self-esteem, and academic performance in primary school students. The sample comprised 124 pupils (59 males and 65 females) enrolled in the 5th and 6th grades. Psychometric assessment was conducted using the Resilience Scale and the Rosenberg Self-Esteem Scale, while academic achievement was evaluated based on students’ grades in Language, Mathematics, Science, English, and Physical Education. Data analysis was performed using ANOVAs and Pearson correlation coefficients. The results indicated that higher academic performance was positively correlated with both increased resilience and self-esteem. Furthermore, a strong positive correlation was observed between self-esteem and resilience. Regarding gender, no significant differences were found in resilience or self-esteem levels. However, academic performance variations were identified exclusively in English language proficiency; specifically, for male students, higher performance in English was significantly associated with greater resilience. In conclusion, these findings suggest that integrating self-esteem and resilience-building activities into the educational curriculum is essential. Such interventions may enhance students’ capacity to manage adversity and facilitate the attainment of their academic goals. Full article

Artificial intelligence (AI) tools are increasingly integrated into mathematics education, yet most reviews emphasize achievement rather than how AI shapes mathematical thinking. This scoping review mapped literature published between 2020 and 2026 on AI-supported mathematics learning through three cognition frameworks: APOS (Action–Process–Object–Schema), Sfard’s process–object duality and reification, and Conceptual Image theory. Searches were conducted in Scopus, Web of Science, ERIC, PsycINFO, Education Source, and IEEE Xplore, followed by duplicate removal and Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR)-aligned screening. Twenty-one peer-reviewed studies met inclusion criteria (18 empirical studies plus three theoretically oriented studies). Evidence growth accelerated after 2022, with most studies situated in secondary and higher education. Large language models (LLMs) and Intelligent Tutoring Systems (ITS) were the most frequently investigated modalities. Across studies, AI commonly supported theoretically inferred action-level execution and procedural management (APOS) via adaptive feedback, hinting, and stepwise scaffolding, and it often broadened learners’ conceptual images through multiple representations and generated explanations. However, these interpretations were necessarily cautious, because very few studies directly operationalized theory-linked conceptual mechanisms such as process internalization, object encapsulation, reification, or alignment between conceptual images and formal definitions. In LLM-supported contexts, gains in explanation quality coexisted with risks of procedural outsourcing when students relied on generated solutions without prior reasoning. By contrast, ITS-based environments more often supported tightly structured procedural engagement, suggesting that different AI modalities afford different forms of cognitive support and risk. Overall, AI’s conceptual impact appears to depend less on tool availability and more on instructional orchestration (task design, prompting, and teacher mediation). The findings also suggest that sustainability-related dimensions—particularly learner agency, transparency of AI support, and equitable participation—are closely connected to whether AI use promotes durable conceptual learning rather than superficial performance gains. Future research should operationalize cognitive transitions, assess structural understanding, and report AI-use conditions transparently to support cumulative, theory-driven synthesis. Full article

This study examined the effectiveness of an intensive learning model in core mathematics courses within engineering education. The model restructures the academic semester so that students study one course at a time in concentrated learning blocks, rather than studying several courses in parallel, with the aim of improving academic achievement and student engagement in engineering mathematics courses. The research employed a quantitative, quasi-experimental, longitudinal design and included 66 undergraduate engineering students who completed three mathematics courses: Linear Algebra, Calculus II, and Differential Equations. Academic performance and learning behavior data were analyzed using mixed-design ANOVA, multiple linear regression, and MANOVA analyses. The findings indicate that students who studied under the intensive learning model achieved significantly higher final grades compared with students in the traditional parallel-course structure. Engagement variables emerged as strong predictors of academic success, particularly class attendance and assignment submission. Academic performance remained stable across the three mathematics courses, and prior academic background variables did not significantly predict achievement. Overall, the results suggest that restructuring mathematics instruction into intensive learning blocks may enhance student engagement and academic performance in demanding quantitative courses, thereby supporting student success and persistence in engineering education. Full article

Do teacher education programs improve students’ confidence in their field practice teaching skills? Despite a growing interest in how student teachers’ practice self-efficacy (PSE) develops, we know little about the impact of the various components of teacher education programs on PSE. The present study examined whether the first year of teacher education, and particularly the field practice in schools which is directed at training and learning teacher practices, is associated with practice self-efficacy using a targeted measure of PSE for student teachers. Using independent sample t-tests and one-way analysis of variance with survey data from 338 students, we show that second-year students have higher PSE than first-year students on most PSE dimensions, with the largest differences being on the PSE dimensions of Planning and preparation, Teaching in itself, and Evaluation and development. In contrast, first-year students scored higher on Adult collaboration PSE. Further exploratory analyses showed that English majors had lower Planning and preparation and Teaching in itself PSE than other majors, whereas Mathematics majors had higher Adult collaboration PSE. We also conducted item analysis for the purpose of validating the PSE for both first- and second-year students. The findings advance our knowledge of differences in practice self-efficacy over the first year of teacher education. Full article

Mathematical knowledge is essential for adult learners’ advancement in academic and professional settings; however, instructional strategies for adult learners in higher education often emphasize memorizing procedures while neglecting their personal and professional experiences. Such approaches limit opportunities to leverage these experiences for developing meaningful mathematical understanding. Grounded in the Models and Modeling Perspective, this exploratory qualitative case study examines how a Model Development Sequence (MDS) supports the development of mathematical knowledge of adult learners returning to higher education. The participants were a group of seven first-year business adult learners enrolled in the Applied Mathematics in Business course at a higher education institution. Data were analyzed using protocol coding to describe the types of mathematical models the participants constructed. Findings indicate that participants progressed from creating models requiring redirection, grounded in proportional reasoning, to developing more sophisticated models based on linear and exponential functions. The MDS supported learners in refining, extending, and adapting their models, strengthening their conceptual understanding of variation, linear and exponential functions, and covariational reasoning. Moreover, the participants’ personal and professional experiences were central to model development. This study contributes to research on adult mathematics education by demonstrating the potential of MDS to support meaningful mathematical learning. Full article