Search Results (1,628)

The rapid integration of artificial intelligence (AI) into educational systems is transforming how student performance is analysed and how educational policies are informed by large-scale data. Within this context, machine learning techniques are increasingly used to identify patterns associated with academic success and educational inequality. However, the use of predictive algorithms in education also raises important questions regarding transparency, fairness, and potential algorithmic bias. This study examines the predictive performance and fairness implications of machine learning models used to identify academically resilient students using data from the Programme for International Student Assessment (PISA) 2022. The analysis is based on a dataset containing more than 600,000 student observations across multiple national education systems. Academic resilience is operationalised following the OECD framework, identifying students who belong to the lowest quartile of the socioeconomic status index (ESCS) within their country while simultaneously achieving mathematics performance in the top quartile (PV1MATH). A predictive framework incorporating six supervised learning algorithms—Logistic Regression, Random Forest, Gradient Boosting, XGBoost, LightGBM, and CatBoost—was implemented. The modelling pipeline includes data preprocessing, missing value imputation, class imbalance correction using SMOTE, and model evaluation through multiple classification metrics, including accuracy, F1-score, and the area under the ROC curve (AUC). In addition, fairness diagnostics are conducted to examine potential disparities in prediction outcomes across gender groups, while feature importance analysis and SHAP-based explanations are used to interpret the contribution of key predictors. The results indicate that ensemble-based models achieve the highest predictive performance, particularly those based on gradient boosting techniques. At the same time, the analysis reveals that socioeconomic status, migration background, and school repetition constitute the most influential predictors of academic resilience. Although gender displays relatively low predictive importance, measurable differences in positive prediction rates across gender groups suggest the presence of potential algorithmic disparities. These findings highlight the importance of integrating fairness evaluation, transparency, and interpretability into educational data science workflows. The study contributes to ongoing discussions on the responsible use of artificial intelligence in education by emphasising the need for governance frameworks capable of ensuring that algorithmic systems support equity-oriented educational policies. Full article

In Vietnamese primary mathematics education, geometry instruction often emphasizes rote calculation and formula memorization rather than meaningful contextualization, leaving students disconnected from abstract concepts and lacking opportunities to connect learning with cultural identity. This quasi-experimental study investigates how integrating generative AI tools (ChatGPT, DALL·E, Canva) with the culturally grounded Vietnamese folktale Bánh Chưng—Bánh Giầy can support Grade 5 students’ understanding of circle geometry. Employing a mixed-methods design with 30 students divided into experimental (AI + storytelling) and control (traditional instruction) groups, the study measured cognitive and affective learning outcomes through pre/post-tests, a validated 25-item questionnaire, interviews, and classroom observations. Quantitative results revealed significant improvements in the experimental group across all measured dimensions, learning interest, attentional focus, conceptual understanding, mathematics passion, and cultural preservation awareness, with large effect sizes. Qualitative findings confirmed enhanced engagement, multimodal conceptual clarity, and cultural affective resonance. The study demonstrates that low-cost, teacher-mediated generative AI can effectively support learning in resource-constrained primary settings when anchored in local narratives. Implications for ethical AI integration and teacher professional development in Vietnamese contexts are discussed. 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

Mathematics is a diverse discipline that requires a variety of cognitive abilities and presents varying levels of difficulty. Understanding how different cognitive profiles relate to specific patterns of mathematical performance is important for developing effective educational interventions. This study extends our previous research, in which we identified subgroups of children with mathematical difficulties based on their cognitive abilities. We examined 146 Israeli elementary school children in grades 3 and 4, classified into four subgroups: Reading Accuracy Difficulties (RAD), Mild Mathematical Difficulties (MMD), Non-Verbal Reasoning Difficulties (NVRD), and Typically Developing children (TD). Participants were assessed on arithmetic facts, computational fluency, procedural skills, estimation, and numeration. We observed varied performance patterns among subgroups. The RAD group showed the most severe impairments across all mathematical domains, along with reading comorbidity and cognitive difficulties. The MMD group, which maintained intact cognitive skills, faced notable challenges in computation, performing significantly below the TD group but better than the RAD group. The NVRD group, despite limitations in nonverbal reasoning, outperformed other difficulty groups on fact retrieval and estimation. Performance on multiplication and division tasks consistently followed a hierarchical pattern across all difficulty groups, with the RAD group facing the greatest challenges. These findings demonstrate that mathematical difficulties vary across cognitive profiles and that distinguishing between profiles through targeted assessment enables the development of differentiated interventions tailored to each learner’s specific cognitive profile. Full article

This study investigates the relationship between creative personality and academic achievement in first-year secondary education students, as well as the predictive capacity of creative personality on performance across different subject areas. The sample comprised 125 students who completed Garaigordobil’s Creative Personality Scale, and their academic grades were collected as performance indicators. Academic achievement was analyzed by distinguishing between STEM subjects (biology, technology, and mathematics) and non-STEM subjects (Spanish language, geography, arts, physical education, French, and English). The findings saw a positive association between creative personality and academic achievement in both STEM and non-STEM domains. Moreover, statistically significant sex differences emerged: female students obtained higher scores than male students on creative personality traits associated with problem identification and problem solving, as well as on dimensions related to enjoyment of diverse games and openness to new experiences. These results underscore the relevance of creative personality as a determinant of academic achievement across both scientific and non-scientific areas. They also highlight the importance of fostering creativity as an educational strategy aligned with sustainability goals. This study offers practical implications for the design of evidence-based psycho-pedagogical interventions that incorporate creativity as a means to promote responsible, equitable, and sustainable learning. Full article

Digital signal processing (DSP) methods and artificial intelligence (AI) serve as a unifying platform across diverse research areas and educational courses based on analysis of signals acquired by appropriate sensors and their time-synchronized systems. Autonomous sensor systems having their own batteries, memories, and possibilities of wireless communication form the core of modern technological systems. The interconnection of sensors for data acquisition, methods for advanced analysis of signal features, and collaborative evaluation promotes both theoretical learning and practical problem solving in professional practice. This paper emphasizes a common mathematical foundation for the processing of data acquired by different sensor systems, and it presents the integration of DSP and AI, enabling the use of similar theoretical methods in different applications, including robotics, digital twins, neurology, augmented reality, and energy optimization. Through selected case studies, it shows how a combination of sensor technology for data acquisition and the use of similar computational methods, visualization, and real-world case studies strengthens interdisciplinary collaboration. Findings of this paper demonstrate how integrating AI with DSP supports innovative research and teaching strategies, redefines the field’s educational role in the digital era, and points to the development of new digital technologies. Full article

Measurement and assessment in Science, Technology, Engineering, and Mathematics (STEM) education is one central topic within STEM education scholarship. While there has been an increase in validation-related scholarship within STEM education, there are few guides for users to conduct validation work. Providing guidance for a broad readership, not just methodologists, offers potential for scholars from more backgrounds to engage in validation. To that end, the purpose of this paper is to build upon past scholarship and both articulate and situate validation as a methodology. Guideposts are provided to support readers as they engage in validation scholarship. A strategy is also provided to give readers support as they engage in validation scholarship. One key outcome from this paper is foundational work that scholars can leverage and extend, challenge, and generate new validation-related work, which in turn moves assessment practice and scholarship forward. Full article

Research on academic achievement highlights the combined role of cognitive abilities and motivational beliefs. Grounded in the CHC framework, this study examined how three broad cognitive abilities—verbal, numeric, and spatial—and academic self-concept jointly predict achievement in Portuguese and mathematics. A sample of 3034 students from the third cycle (grades 7–9) and secondary education (grades 10–12) completed the BAC-AB cognitive battery and a validated academic self-concept scale. Using multigroup structural equation modelling, we tested whether the predictive patterns differed across educational stages. Academic self-concept emerged as the most consistent predictor across subjects and levels. Cognitive contributions displayed clear developmental differentiation: verbal ability was more strongly associated with Portuguese (and increasingly with Mathematics) in secondary education, whereas numeric and spatial abilities were comparatively more relevant for Mathematics in the third cycle. These patterns support the view that linguistic, quantitative, and visuospatial processes contribute to achievement in distinct and developmentally sensitive ways. Overall, the findings underscore the importance of instructional approaches that build on quantitative and spatial strengths in earlier grades while progressively supporting advanced verbal comprehension and reasoning in later schooling. Full article

Computational thinking has been increasingly recognised as a core component of mathematics education, particularly in areas where analytical reasoning and computational practices intersect. However, limited empirical research has examined how computational verification supports mathematical reasoning in postgraduate mathematics education, where teaching often emphasises either analytical derivations or software implementation without explicitly connecting the two. This study investigates the integration of computational thinking within a postgraduate Mathematics of Signal Processing module through a structured coursework design combining analytical problem solving with computational verification. Over three academic years, students solved discrete-time signal and convolution problems analytically and then verified their solutions computationally. Performance data were analysed using descriptive and non-parametric statistical methods to examine differences between analytical and computational performance. Across cohorts, computational verification resulted in statistically significant performance improvements, with mean gains ranging from +1.20 to +2.00 marks (Wilcoxon signed-rank test, p < 0.05) and moderate-to-strong effect sizes (r = 0.56–0.59). Strong positive correlations were also observed between analytical and computational marks (0.61 ≤ r ≤ 0.96), indicating alignment between mathematical understanding and computational validation. The findings suggest that verification-driven learning can improve solution accuracy, reduce conceptual errors and strengthen computational thinking practices in advanced mathematics education. This study contributes empirical evidence from postgraduate mathematics education and highlights the value of integrating analytical reasoning with computational validation in technical modules. Full article

Background: This exploratory study investigates whether STEM (science, technology, engineering, and mathematics) students’ increasing reliance on off-campus resources (e.g., online platforms, private tutors) reflects an authentic preference for autonomous learning or a compensatory response to perceived deficiencies in on-campus instruction. Methodology: Using a mixed-methods design, data were collected from 118 engineering and science students. A model was developed to examine the relationship between the intensity of student criticism and their declared preference for off-campus learning. Findings: The model revealed a significant negative relationship between the intensity of criticism and the preference for off-campus instruction. This suggests that for highly critical students, external resources function primarily as a compensatory mechanism for “needs frustration” rather than a preferred alternative. The results imply that these students continue to value the frontal model but find its current implementation insufficient to meet their pedagogical needs. Conclusion: These findings challenge the assumption that digital trends signify a voluntary abandonment of the classroom. Instead, reliance on external resources is positioned as a reactive, compensatory strategy. Higher education institutions should prioritize revitalizing frontal instruction through enhanced clarity and focus to reduce dependency on off-campus platforms and restore the value of the campus experience. Full article

This study examines the efficiency and productivity of public education expenditure in 20 countries using Data Envelopment Analysis (DEA) and the Malmquist Productivity Index over the period 2011–2023. Focusing on science and mathematics performance at the primary and lower-secondary levels, the results show that higher public spending does not necessarily lead to better educational outcomes, highlighting the importance of efficient resource allocation. The DEA estimates reveal substantial cross-country heterogeneity in efficiency, while the Malmquist results indicate positive total factor productivity growth across all countries, driven mainly by technical progress rather than efficiency catch-up. Countries such as Morocco, Japan, Turkey, and Iran exhibit sustained productivity improvements, particularly in 2019–2023. Persistent disparities in efficiency and productivity are closely associated with differences in education policies, governance, and socio-economic contexts. Overall, the findings stress the need for efficiency-oriented education reforms to enhance performance and promote sustainable growth. Full article

The affective domain exercises significant influence over the learning of mathematics and, therefore over the future praxis of teachers. This is why the affective domain needs to be included in teacher training. Therefore, our aim is to show how the praxis carried out in the classroom by teacher educators promotes the evolution of beliefs about mathematics and thus that their specialist knowledge—specifically, the KFLM subdomain—could change. We used a Likert-type questionnaire divided into three sections with four response options, answered by 59 teachers. The results show the evolution of some student beliefs towards a higher percentage in favor and the stability of others depending on the praxis implemented by the teacher educator. The favorable evolution is related to the beliefs about the role of teachers in their training and their own learning. This could promote a change in the KFLM of future teachers and thus in teacher–student interactions. Full article

This study examines how justice-oriented modeling lessons promote elementary students’ capacity to mathematize complex situations, develop civic empathy, and take action to address inequities and injustices in their communities. Through qualitative methods using multiple data sources including teacher interviews, lesson transcripts, student work, and classroom artifacts we share cases of modeling tasks that use mathematics as an empowerment tool to address empathy, representation, access, fairness and taking action. Findings illustrated critical moment-to-moment instructional decisions teachers made to elicit students’ justice-oriented reasoning. The modeling tasks involved addressing food waste in the school cafeteria, creating an inclusive play area, diversifying the school library collections, and choosing items for a sensory space to positively impact students’ individual and community well-being. Implications for teachers and teacher educators will be discussed. Full article

The purpose of this research is to understand how to better equip pre-service teachers (PSTs) to engage marginalized learners and implement culturally responsive teaching (CRT) practices in elementary Science, Technology, Engineering, Arts, and Mathematics (STEAM) education. This was attempted through a module on CRT and a STEAM Biography assignment, which aimed to heighten teacher candidates’ awareness of the contributions of individuals from marginalized/underrepresented groups, generate discourse on equitable teaching practices, and foster culturally responsive teaching practices. This research study examines data collected by the researchers, who also served as course creators and instructors, from teacher candidate participants enrolled in a STEAM methods course in which this assignment was implemented. Data were collected through a survey instrument and analyzed using content analysis methodology (qualitative and quantitative). Preliminary findings suggest that PSTs developed strong emerging equity-oriented mindsets and recognized the importance of belongingness and connection to meet the needs of all learners. In addition, since most PSTs reported the need for more practical CRT examples for use in their future classrooms, the biography assignment helped to foster the development of positive dispositions toward culturally responsive teaching in the STEAM disciplines. Full article

Confidence in mathematics is a key factor for academic success, being influenced by emotional, behavioral, and technological aspects. The integration of digital tools, such as GeoGebra, has shown potential to promote engagement and develop mathematical skills. This study investigates how affective and behavioral engagement, confidence in the use of technology, and the perception of GeoGebra use relate to and contribute to explaining the confidence in mathematics of future teachers. The sample comprised 54 undergraduate students in Basic Education from a higher polytechnic institution. Participants engaged in learning activities involving real functions of a real variable using both traditional methods and GeoGebra. Data were analyzed using partial least squares structural equation modeling. The results indicate that behavioral engagement positively influences affective engagement, which, in turn, enhances confidence in mathematics. Confidence in the use of technology also has a positive effect on confidence in mathematics. The perception of GeoGebra use significantly influences behavioral engagement and confidence in the use of technology, but not affective engagement. These findings highlight the importance of the critical integration of digital technologies in mathematics education and emphasize the need to design pedagogical strategies that promote active participation and strengthen future teachers’ confidence in using technological tools. Full article