Search Results (177)

Physics teaching faces challenges due to students’ limited understanding of fundamental concepts such as force and motion, as well as the restricted pedagogical strategies often employed by instructors and the limited variety of approaches to physical foundations. This difficulty is aggravated by the perception of physics as distant from everyday life and by the traditional approach focused on solving mathematical problems. Despite the importance of Newton’s second law, many students confuse the relationships between mass, force, and acceleration, which highlights the need to innovate in teaching practices toward active learning trends. To explore the state of teaching Newton’s second law, a systematic review of the literature was conducted using the PRISMA methodology, analyzing twenty-six articles from the Web of Science and Scopus databases. This revealed an increase in interest in teaching this law, especially in 2023. However, the limited number of studies (only 26) also indicates that research on this topic remains scarce and underexplored. Most studies focus on primary and secondary school students (43%) and employ quantitative methodologies (38%). Teaching strategies include problem-solving (40%), simulations (27%), practical activities (14%), and group discussions (12%). Furthermore, it was identified that Newton’s law is primarily represented in scalar form, with limited inclusion of vector approaches, which highlights the need to discuss didactic alternatives that consider both approaches. Full article

At the end of the 20th century, the rapid development of brain-like dynamics was attributed to the excellent modeling of numerous neurons and neural systems, which effectively simulated biological behaviors observed in the human brain. With the continuous advancement of research, memristive neural networks (MNNs) have been extensively studied. In recent years, the exploration of fractional-order MNNs (FMNNs) has attracted research interest, leading to the discovery of the system’s dynamical phenomena, including transient chaos, hyperchaos, multi-stability, and the coexistence of attractors. To facilitate comparative research and learning, a review of the newly proposed fractional-order chaotic system models in recent years is urgently needed. In this review, we first introduce the basic theoretical knowledge of chaotic dynamics, artificial neural networks, fractional order, and memristors. Then, we mathematically describe the fractional-order systems and detail the highly regarded FMNNs in recent years, making comparative discussions and studies. Finally, we discuss the application of these models across diverse domains and propose thought-provoking questions and future research directions. Full article

A wide variety of requirements can be placed on tasks that deal with education for sustainable development in mathematics lessons. They should be as authentic as possible, use real problems as the mathematical learning material, and stimulate action, among other qualities. This article discusses the role of self-efficacy and the experience of self-efficacy when working on modelling tasks that are geared towards a sustainable future. High school students in Germany worked in a STEM learning environment on different aspects of climate change and species extinction, including plastic waste, recycling, rainforests, and their deforestation. These aspects were analysed from a geographical, biological, physical, and mathematical perspective. In mathematics, specifically, tasks were used to address the learners’ self-efficacy. After completing the tasks, a questionnaire was distributed to assess the interest and motivation of the learners. The results show that even a slightly different use of self-efficacy, whether by focusing on what has already been achieved (sustainable successes that promote positive emotions) or on what can still be achieved, can influence the learners’ interest in completing the tasks. The learners’ experience of self-efficacy seems to have a positive influence on their willingness to solve tasks. Additionally, the results indicate a complex relationship between motivation and interest on the one hand and self-efficacy on the other. Full article

Science, technology, engineering, and mathematics (STEM) are crucial for economic development and play a significant role in achieving sustainable development goals. Despite this, there is a shortage of skilled STEM professionals and a declining interest in STEM education and careers. The Saudi Vision 2030 goal of economic diversification and sustainable development aims to transform Saudi Arabia into a knowledge-based economy driven by innovation and sustainability. This study investigates factors influencing adolescents’ attitudes toward STEM careers in Saudi Arabia, with comparative insights from Bahrain and Singapore. Structural equation models (SEM) were constructed for each country to analyze the influence of scientific self-concept, school belonging, and teacher effectiveness on students’ choices of science careers. Mediation analysis examined the interest and value of science as mediators in these relationships. Confirmatory factor analysis was conducted to validate model constructs before building SEM models. Data from TIMSS 2019 for eighth-grade students was used to develop model constructs based on relevant items from the student questionnaire. Findings reveal that students’ interest in and value of science significantly influence career decisions, with self-concept and teacher engagement playing crucial roles. Teacher effectiveness had the strongest impact on science interest in Saudi Arabia and Bahrain, while self-concept was most influential in Singapore. These results highlight the importance of fostering teacher engagement and self-concept to encourage students’ career paths in science. To support this, Saudi Arabia should enhance teacher training programs by integrating mentorship, active learning strategies, and technology driven instruction to improve student engagement. Adopting Singapore’s blended learning model can foster self-confidence and independence in STEM education, while hands-on learning and career exposure programs can strengthen students’ self-concept and long-term commitment to STEM fields. Additionally, expanding extracurricular STEM initiatives and industry partnerships will help connect classroom learning to real-world applications. By aligning STEM education reforms with these insights, Saudi Arabia can cultivate a skilled workforce that supports its economic transformation under Vision 2030. Full article

The non-parametric version of Amari’s dually affine Information Geometry provides a practical calculus to perform computations of interest in statistical machine learning. The method uses the notion of a statistical bundle, a mathematical structure that includes both probability densities and random variables to capture the spirit of Fisherian statistics. We focus on computations involving a constrained minimization of the Kullback–Leibler divergence. We show how to obtain neat and principled versions of known computations in applications such as mean-field approximation, adversarial generative models, and variational Bayes. Full article

Rural Spain has strengths but also presents notable problems. Education is a necessary way to improve rural communities. A better educated population will ensure they do not disappear and enhance their quality of life. This article explores the educational potential of rural Spain by focusing on a practical case study at the Grouped Rural School (CRA) Las Cogotas in Ávila, Castilla and León. It highlights the strengths and challenges of rural areas, including depopulation, aging, and lack of services, and emphasizes the importance of education in addressing these issues. The study implements STEAM (Science, Technology, Engineering, Arts, and Mathematics) education through outdoor activities, utilizing local rural and natural resources to enhance students’ learning experiences and foster environmental stewardship. The methodology includes structured interviews and a Likert scale survey analyzed with the ATLAS.ti tool to evaluate the effectiveness of the activities. The results indicate that students developed a stronger connection to their environment and showed increased interest in STEAM subjects. The findings underscore the value of integrating rural resources into formal education to improve the quality of life and sustainability of rural communities. The article aims to highlight the educational resources that rural areas offer to develop STEAM education. Full article

Integrated STEM (i-STEM) education is attracting attention from educators and researchers worldwide to improve student achievement and engagement in STEM subjects and encourage the take-up of STEM-related careers. Multiple models of STEM integration have been proposed, and how i-STEM is interpreted and enacted in school contexts appears to vary considerably. This article reports the perceptions and practices of a group of Australian secondary school teachers with a commitment to implementing i-STEM in their schools but who have not received any specific professional development in this domain. Through individual, qualitative interviews, the study revealed considerable variation in how the teachers interpreted and enacted i-STEM in their schools. Teachers tended to develop learning activities that prioritized the subject area of their particular expertise and that had only tenuous links to mathematics. They considered i-STEM more engaging for their students than traditional subjects but were constrained in their planning by their various school regimes concerning assessment, curricula, and timetables. These structural and systemic impediments represent a core challenge for STEM teachers and teaching as greater numbers of schools and teachers in Australia are expected to implement some form of i-STEM education. Insights from this study point to the importance of developing support structures that allow for variations in context, as well as teacher interest and experience, yet that embrace a coherent and cohesive view of i-STEM, in the absence of a formal STEM curriculum and available professional development opportunities. Full article

(1) Background: One of the greatest challenges students face when studying quantum mechanics is the lack of daily experience and intuition about its concepts. This article introduces a holistic activity designed to present some foundational ideas of quantum mechanics in a new pedagogical approach to enhance students’ motivation. Using real open data from CERN, the activity connects classical concepts of dynamics and electromagnetism to their quantum counterparts, emphasizing both their similarities and differences. Teaching physics must consider the way the brain learns. That is why the activity is based on observed neuroscientific principles of physics learning. The approach maintains the rigor and precision required for these abstract concepts. (2) Methods: To evaluate the activity’s impact by gender, intrinsic motivation was assessed using a Likert-type scale with 81 undergraduate students from fields including artificial intelligence systems engineering, computer engineering, mathematical engineering, and architecture. (3) Results: a Mann–Whitney U test analysis indicates the activity significantly enhances students’ intrinsic motivation to study quantum mechanics, with improvements observed in both male and female students. (4) Conclusions: This result highlights the potential of the activity to promote greater interest in physics, both in men and women, since no significant differences have been observed between both samples. Full article

Stall events in axial compressor systems have been a limiting factor for efficiency of such systems and a source of safety concerns. The detection of the onset of stall, and in many cases the precursor of the onset of stall, have been of interest in the axial compressor community for many decades. As such, development of algorithms along with active control could lower cost, reduce emissions, improve safety, and increase market competitiveness. To gain an understanding of these stall phenomena, past and current research has focused on modeling axial compressors as dynamic systems, with a focus on obtaining descriptive formulations of the physical aspects of stall. Some of these approaches allow for active control measures that extend the stall margin of the compressor system to increase safety and efficacy. This paper reviews the major contributions in these listed pursuits and presents the latest methods and algorithms for stall precursor detection in low-speed axial compressors. In particular, a review is presented in the types and characteristics of stalls, the major mathematical models used to describe these systems, influences of physical attributes such as tip clearance, guide vanes, and groove casing—operating as passive control elements—but also active control utilities such as air injection are discussed along with a detailed review of existing stall precursor detection algorithms. In addition, a forward-looking projection is presented that includes the use of machine learning algorithms to further the understanding and the capability of stall precursor detection. Full article

School-age children and being outdoors are intrinsically linked. Education and the outdoors offer unique opportunities to extend the learning potential of children at this age in a more engaging way. From the point of view of mathematics, this way of learning is very suitable and certainly motivating for the pupils since mathematics, especially geometry, is not very popular among pupils. Therefore, in this article, we describe how we used an out-of-school learning experience for second-level primary school pupils (sixth to ninth grade) to link mathematics to outdoor learning. Activities that we solved with pupils in the outdoor environment were solved in the teaching process using only modeling. Practical applications were illustrated through interesting topographic fieldwork, which we analyzed for their appropriate integration into the teaching process by means of a priori analysis. The inclusion of these practical problems was preceded by research on 781 pupils of primary schools who solved application problems related to the circle and the square. It was clear from the research that pupils have a significant problem with the geometric interpretation of simple geometric concepts, which can be improved with the use of mathematical modeling and the linking of similar problems that can be carried out in a non-school environment. Full article

Social constructivism theory embraces peer-to-peer communication that helps students understand, examine, and discern the process of knowledge construction. The Social Constructivism Flipped-Classroom Model (SCFCM) inverts the traditional classroom paradigm by providing content outside of class, often through online materials, and devoting in-class time to active learning and discussion. This study aims to investigate the impact of the SCFCM on the conceptual understanding of photocatalysis, a crucial process in environmental science and chemistry, particularly in relation to sustainability and sustainable development. Photocatalysis, being a self-sustained process, holds potential for addressing global challenges such as renewable energy and pollution reduction, both of which are central to achieving sustainable development goals. A quasi-experimental pre-test–post-test design was employed at a public sector university, involving forty-three (43) students in each of the flipped- and non-flipped-classroom groups. Assessment tools, including pre- and post-tests and an interest survey, were used to gauge students’ conceptual understanding of photocatalysis and their degree of learning interest. The same chemistry teacher, one who had eight years of teaching experience, taught both groups. The analysis of covariance (ANCOVA) results comparing students’ performance showed a significant difference in the performance of students in the experimental group compared to the control group. The multivariate analysis of variance (MANOVA) results, however, revealed substantial differences in attention, relevance, confidence, and satisfaction between the experimental and control groups. The findings highlight that the SCFCM improved students’ understanding of complex photocatalysis concepts and demonstrated its relevance to sustainable development, offering valuable insights into the potential of this teaching approach for Science, Technology, Engineering, Mathematics (STEM) education, especially in addressing sustainability challenges. Full article

In the study and design of planar mechanisms, graphical techniques for solving kinematic analysis/synthesis and kinetostatics problems have regained interest due to the availability of advanced drawing tools (e.g., CAD software). These techniques offer a deeper physical understanding of a mechanism’s behavior, which can enhance a designer’s intuition and help students develop their skills. Geometric Constraint Programming (GCP) is the term used to describe this modern approach to implementing these techniques. GeoGebra is an open-source platform designed for the interactive learning and teaching of mathematics and related STEM disciplines. It offers an object-oriented programming language and a wide range of geometric tools that can be leveraged to implement GCP. This work presents a systematic technique for studying and designing planar linkages, based on Assur’s groups and GeoGebra’s tools. Although some kinematic analyses and syntheses of planar linkages using GeoGebra have been previously introduced, the proposed systematic approach is novel and could serve as a guide for implementing similar problem-solving methods in other graphical environments. Several case studies will be presented to illustrate this novel approach in detail. Full article

This longitudinal study examines the long-term impact of critical pedagogy on the academic and social development of students from a marginalized Mexican American borderland community, with a specific focus on their trajectories in STEM (Science, Technology, Engineering, and Mathematics) fields. Drawing on in-depth interviews with three students who participated in a critical pedagogical approach during their formative elementary years, this case study explores how power, agency, and curiosity were cultivated in the classroom and how these elements influenced the students’ pursuit of higher education and careers in STEM ten years later. The findings reveal that an equitable distribution of power and the fostering of student agency through critical-based pedagogies empowered students to engage critically with their learning and to challenge systemic barriers. Furthermore, this study highlights the role of early curiosity in sustaining students’ interest in STEM, despite encountering obstacles in higher education environments dominated by market-driven influences. By situating this research within the broader context of critical pedagogy and its emphasis on social justice, the study underscores the transformative potential of education in shaping the futures of minoritized students in STEM disciplines. Full article

This paper proposes causal economic machine learning (CEML) as a research agenda that utilizes causal machine learning (CML), built on causal economics (CE) decision theory. Causal economics is better suited for use in machine learning optimization than expected utility theory (EUT) and behavioral economics (BE) based on its central feature of causal coupling (CC), which models decisions as requiring upfront costs, some certain and some uncertain, in anticipation of future uncertain benefits that are linked by causation. This multi-period causal process, incorporating certainty and uncertainty, replaces the single-period lottery outcomes augmented with intertemporal discounting used in EUT and BE, providing a more realistic framework for AI machine learning modeling and real-world application. It is mathematically demonstrated that EUT and BE are constrained versions of CE. With the growing interest in natural experiments in statistics and causal machine learning (CML) across many fields, such as healthcare, economics, and business, there is a large potential opportunity to run AI models on CE foundations and compare results to models based on traditional decision-making models that focus only on rationality, bounded to various degrees. To be most effective, machine learning must mirror human reasoning as closely as possible, an alignment established through CEML, which represents an evolution to truly “human AI”. This paper maps out how the non-linear optimization required for the CEML structural response functions can be accomplished through Sequential Least Squares Programming (SLSQP) and applied to data sets through the S-Learner CML meta-algorithm. Upon this foundation, the next phase of research is to apply CEML to appropriate data sets in various areas of practice where causality and accurate modeling of human behavior are vital, such as precision healthcare, economic policy, and marketing. Full article

Graph neural networks (GNNs) are deep learning algorithms that process graph-structured data and are suitable for applications such as social networks, physical models, financial markets, and molecular predictions. Bibliometrics, a tool for tracking research evolution, identifying milestones, and assessing current research, can help identify emerging trends. This study aims to map GNN applications, research directions, and key contributors. An analysis of 40,741 GNN-related publications from the Web Science Core Collection reveals a rising trend in GNN publications, especially since 2018. Computer Science, Engineering, and Telecommunications play significant roles in GNN research, with a focus on deep learning, graph convolutional networks, neural networks, convolutional neural networks, and machine learning. China and the USA combined account for 76.4% of the publications. Chinese universities concentrate on graph convolutional networks, deep learning, feature extraction, and task analysis, whereas American universities focus on machine learning and deep learning. The study also highlights the importance of Chemistry, Physics, Mathematics, Imaging Science & Photographic Technology, and Computer Science in their respective knowledge communities. In conclusion, the bibliometric analysis provides an overview of GNN research, showing growing interest and applications across various disciplines, and highlighting the potential of GNNs in solving complex problems and the need for continued research and collaboration. Full article