Mathematical Study Research Articles

Research on the Cultivation Strategy of Primary School Students' Mathematical Language Expression Ability

Learning mathematics is a process of learning mathematical language, which is the external manifestation of mathematical thinking, the carrier of mathematical knowledge and mathematical ideas and methods, and at the same time, mathematical knowledge also needs mathematical language as a medium to disseminate and communicate. In the study of mathematics, whether students can learn mathematics well is related to whether students have good mathematical language skills, but many studies can show that there are obvious deficiencies in the current mathematical language expression skills of primary school students. Therefore, teachers need to strengthen students' thinking training and good study habits, so as to further cultivate students' mathematical language expression ability and improve students' core literacy. To this end, this paper will elaborate on the concept and classification of mathematical language, as well as the cultivation strategies of primary school students' mathematical language expression ability.

Synergizing teacher collaboration to foster active learning strategies, student engagement and achievement in physics: a unified analysis across nine countries

This investigation explores the nexus between professional teacher collaboration, active learning methodologies, teaching promoting student engagement in physics classes and physics achievement. Leveraging Trends in International Mathematics and Science Study Advanced 2015 data from 1729 physics instructors across nine countries, it probes the extent to which collaborative practices among teachers influence their teaching to enhance active learning and engagement. Analytical techniques include Spearman correlations, confirmatory factor analysis, and structural equation modeling. Results reveal a robust model, confirmed by a Comparative Fit Index of 0.989 and Tucker–Lewis Index of 0.988, suggesting a good fit with the empirical data, alongside a Root Mean Square Error of Approximation of 0.053. The analysis demonstrates that teacher collaboration (‘Collab’) significantly predicts active learning (‘Active’) with a notable effect (β = 0.344, p < 0.001), which in turn significantly enhances student engagement (‘Engage’) (β = 0.438, p < 0.001). Additionally, ‘Engage’ is also directly influenced by ‘Collab’ (β = 0.174, p < 0.001). Although the relationship between ‘Collab’ and physics achievement (‘Physics’) is weaker, it remains significant (β = 0.063, p = 0.019). These findings support the enhancement of teacher collaboration as a catalyst for active learning and student engagement, which could indirectly foster improvements in physics achievement globally.

Using international large-scale assessment for learning: Analyzing U.S. students’ geometry performance in TIMSS

The data from international large-scale assessments, such as The Trends in International Mathematics and Science Study (TIMSS), is often designed and used as an assessment of learning rather than an assessment for learning. This research employed TIMSS 2011 data, focusing on the geometric performance of fourth-grade students in the United States, to demonstrate how large-scale assessments can be utilized qualitatively to identify students' learning challenges, providing valuable insights to inform geometry teaching practices. Using van Hiele’s levels of geometric thinking as a guiding framework, the 24 released geometry items were analyzed to identify students’ struggle points in geometry nationwide. Our analysis revealed that students performed well on tasks that were visual, contextually grounded, and had clearly defined manipulative expectations.. However, students needed further support to solve items requiring a deep understanding of geometrical concepts, such as finding the perimeter of a square. Additionally, students excelled at solving tasks involving the identification of relative positions of objects on a map and recognizing lines of symmetry in regular shapes. However, they struggled with measurement-related tasks. Our analysis also identified a range of item features that might cause difficulties that impacted the way students responded to these items. Instructional implications for elementary mathematics education are discussed.

Identification of Dominant Factors Affecting Study Time and Waiting Time of Mathematics Undergraduate Using the Least Absolute Shrinkage And Selection Operator (LASSO)

Higher education is a level of education that is able to produce and prepare graduates so that college graduates are able to compete and are ready to face the world of work. The aim of this research is to determine the dominant factors that influence the study period and waiting time for undergraduates in Department of Mathmatics FST UIN Sumatera Utara Medan until they get their first job in less than 9 months. The Least Absolute Shrinkage And Selection Operator (LASSO) method was used in this research, where this method is expected to be able to provide high accuracy results in terms of determining the most dominant factors. From the results of calculations using the LASSO method, the three most dominant factors that greatly influence the study period and waiting time for undergraduates are working status (X3), organizational participation (X5), grade point average (X2), final assignment position (X2). By producing this most dominant factor, it is hoped that mathematics study programs will care more about their students by making major and minor improvements to future accreditation

Bayesian estimation of the prevalence of antimicrobial resistance: a mathematical modelling study.

Estimates of the prevalence of antimicrobial resistance (AMR) underpin effective antimicrobial stewardship, infection prevention and control, and optimal deployment of antimicrobial agents. Typically, the prevalence of AMR is determined from real-world antimicrobial susceptibility data that are time delimited, sparse, and often biased, potentially resulting in harmful and wasteful decision-making. Frequentist methods are resource intensive because they rely on large datasets. To determine whether a Bayesian approach could present a more reliable and more resource-efficient way to estimate population prevalence of AMR than traditional frequentist methods. Retrospectively collected, open-source, real-world pseudonymized healthcare data were used to develop a Bayesian approach for estimating the prevalence of AMR by combination with prior AMR information from a contextualized review of literature. Iterative random sampling and cross-validation were used to assess the predictive accuracy and potential resource efficiency of the Bayesian approach compared with a standard frequentist approach. Bayesian estimation of AMR prevalence made fewer extreme estimation errors than a frequentist estimation approach [n = 74 (6.4%) versus n = 136 (11.8%)] and required fewer observed antimicrobial susceptibility results per pathogen on average [mean = 28.8 (SD = 22.1) versus mean = 34.4 (SD = 30.1)] to avoid any extreme estimation errors in 50 iterations of the cross-validation. The Bayesian approach was maximally effective and efficient for drug-pathogen combinations where the actual prevalence of resistance was not close to 0% or 100%. Bayesian estimation of the prevalence of AMR could provide a simple, resource-efficient approach to better inform population infection management where uncertainty about AMR prevalence is high.

ETHNOMATHEMATICS EXPLORATION OF THE ISTIQLAL MOSQUE JAKARTA

Ethnomathematics is a branch of mathematical study that was developed in a cultural context. This research aims to explore the concept of ethnomathematics in the Istiqlal Mosque in Jakarta. The method used is descriptive exploratory with an ethnographic approach, which aims to explore information thoroughly to describe and analyze cultural elements in society. Data collection techniques include two types, namely library data collection through literature studies and field data collection through observation and documentation. The collected data is then analyzed using the data reduction method, which involves sorting, centralizing, and simplifying information. The results of the study show that the structure and ornaments of the Istiqlal Mosque Jakarta contain various flat and spatial structures, such as half-spheres, tubes, rectangles, and circles. Keywords: Ethnomathematics, Geometry, Istiqlal Mosque Jakarta

Mass incarceration as a driver of the tuberculosis epidemic in Latin America and projected impacts of policy alternatives: A mathematical modeling study.

Tuberculosis incidence is increasing in Latin America, where the incarcerated population has nearly quadrupled since 1990. The full impact of incarceration on the tuberculosis epidemic, accounting for effects beyond prisons, has never been quantified. We calibrated dynamic compartmental transmission models to historical and contemporary data from Argentina, Brazil, Colombia, El Salvador, Mexico, and Peru, which comprise approximately 80% of the region's incarcerated population and tuberculosis burden. Using historical counterfactual scenarios, we estimated the transmission population attributable fraction (tPAF) for incarceration and the excess population-level burden attributable to increasing incarceration prevalence since 1990. We additionally projected the impact of alternative incarceration policies on future population tuberculosis incidence. Population tuberculosis incidence in 2019 was 29.4% (95% UI, 23.9-36.8) higher than expected without the rise in incarceration since 1990, corresponding to 34,393 (95% UI, 28,295-42,579) excess incident cases across countries. The incarceration tPAF in 2019 was 27.2% (95% UI, 20.9-35.8), exceeding estimates for other risk factors like HIV, alcohol use disorder, and undernutrition. Compared to a scenario where incarceration rates remain stable at current levels, a gradual 50% reduction in prison admissions and duration of incarceration by 2034 would reduce population tuberculosis incidence by over 10% in all countries except Mexico. The historical rise in incarceration in Latin America has resulted in a large excess tuberculosis burden that has been under-recognized to-date. International health agencies, ministries of justice, and national tuberculosis programs should collaborate to address this health crisis with comprehensive strategies, including decarceration. National Institutes of Health.

Viral Rebound After Antiviral Treatment: A Mathematical Modeling Study of the Role of Antiviral Mechanism of Action.

The development of antiviral treatments for SARS-CoV-2 was an important turning point for the pandemic. Availability of safe and effective antivirals has allowed people to return back to normal life. While SARS-CoV-2 antivirals are highly effective at preventing severe disease, there have been concerning reports of viral rebound in some patients after cessation of antiviral treatment. In this study, we use a mathematical model of viral infection to study the potential of different antivirals to prevent viral rebound. We find that antivirals that block production are most likely to result in viral rebound if the treatment time course is not sufficiently long. Since these antivirals do not prevent infection of cells, cells continue to be infected during treatment. When treatment is stopped, the infected cells will begin producing virus at the usual rate. Antivirals that prevent infection of cells are less likely to result in viral rebound since cells are not being infected during treatment. This study highlights the role of antiviral mechanism of action in increasing or reducing the probability of viral rebound.

Unpacking the factors that influence secondary students’ attitudes towards physics in nine different countries - a cross-cultural analysis

It is crucial to comprehend the factors that affect attitudes towards physics to improve academic performance in the subject. This research aims to identify significant predictors of these attitudes by analysing the Trends in International Mathematics and Science Study Advanced 2015 data from nine countries: the USA, Italy, Lebanon, Norway, Portugal, Russia, Slovenia, Sweden, and France. The study employs multiple regression analyses to examine the influence of predictive variables on students’ attitudes toward physics, taking into account weighting features a large-scale survey. The findings suggest that there are significant differences across cultures. In all the countries surveyed, female students appear to have a less favourable attitude toward physics than male students, making gender a significant factor. In most countries, students who spend more time on physics homework have a more positive attitude, highlighting the importance of outside-of-class engagement. The perception of engaging teaching and the value that students place on physics are solid and positive determinants of attitudes, emphasizing the role of instructional quality and relevance of the subject matter. These findings provide a better understanding of how different factors contribute to students’ attitudes toward physics and can help design targeted educational interventions.

Primary education students’ beliefs about the nature of mathematics, self-efficacy, and mathematics educators; a descriptive study from Indonesia

ABSTRACT The significant role of mathematical belief studies has been recognised several decades later. These beliefs have been found to be significantly associated with performance and problem-solving. However, empirical studies of these beliefs in Indonesia are scarce. This study investigates students’ beliefs on the nature of mathematics, self-efficacy, and their perceptions of mathematics educators. The findings of this study revealed that Indonesian students strongly believe anyone could attain proficiency in mathematics. At the same time, students had less confidence that they could understand difficult mathematical tasks and less confidence in showing that they were good at mathematics. Notably, boys’ students exhibit stronger beliefs in their understanding of the course, including the most challenging topics in mathematics, compared to their girls’ counterparts. The implications of this study significantly contribute to the existing literature and inform teaching practices.

Looking beyond enrollment rates: The long-term influence of preschool science curricula on children’s science achievement

This study provides evidence that what happens in preschool (i.e., preschool curriculum) can have long-term consequences years later. In the current study, we seek to answer the question of whether the inclusion of science in the preschool curriculum is associated with fourth- and eighth grade science achievement scores. Based on science achievement data from the Trends in International Mathematics and Science Study (TIMSS 2015 and 2019 cycles), the quality of preschool science learning opportunities showed long-term impacts on science achievement at the fourth grade level. Even after controlling for enrollment rates, the quality of the preschool curricula was a statistically significant predictor of fourth grade science scores with a high effect size (d=0.74). The observed impacts, however, appeared to diminish by eighth grade. Results from this study suggest that science concepts and skills should be an integral part of preschool curricula and curricular frameworks, and policy makers should allocate resources to provide well-designed preschool education programs with high quality curricula.

Students' Classroom Participation during Mathematics and Its Effect on Their Analytical Skills in Problem-solving in Secondary Schools in Fako Division, South West Region of Cameroon

In today's scientific and technological age, for any nation to meet up with the current global challenges, it needs to consider the study of mathematics very seriously. This makes mathematics pivotal in reshaping the future and students' attitude towards mathematics an area of concern. The objective of the study is to determine if students' classroom participation during mathematics has an effect on their analytical skills in problem-solving. The mixed research method with convergent parallel design was used.

Exploring the digital divide in primary education: A comparative study of urban and rural mathematics teachers’ TPACK and attitudes towards technology integration in post-pandemic China

Exploring the digital divide in primary education: A comparative study of urban and rural mathematics teachers’ TPACK and attitudes towards technology integration in post-pandemic China

The cloak of equality in STEM education: institutionalised masking of gender differences in STEM expectations, task values, and participation

ABSTRACT Do gender parity and egalitarian values in STEM education mask institutionalised gender inequalities in STEM? This study uses student background questionnaire data from the 2015 Trends in International Mathematics and Science Study (TIMSS) supplemented by national level data on gender parity in educational enrolment and female participation in the STEM workforce from the United Nations and International Labor Organization to empirically examine the evidence. Using two-level hierarchical linear models, we analyse the effects of students’ STEM expectancies and task values on their anticipated STEM education and career participation. Results confirm that gender equity and egalitarian values dominate the cross-national expectancies and task values for STEM education, both across genders as well as for female and male students separately, even though there is wide variation and significant gendered inequality that persists in both educational access and STEM workforce participation. Thus, the ‘cloak of equality’ hypothesis is supported by the evidence presented.

Etnomatematika Terhadap Konsep Geometri Pada Ukiran Toraja

The conscious or unconscious entry of mathematics into various aspects of life is certainly interesting to study such as from a cultural point of view. What can bridge the gap between culture and mathematics is ethnomathematics. Ethnomathematics is a study of the relationship between mathematics and culture. The study of mathematics in culture needs to be developed further to be able to provide an overview to the public about the role of mathematics in culture. one of the tribes that is rich in culture is Toraja, for example in Toraja carving which is a cultural heritage that is passed down from generation to generation until now there is the concept of geometry. Therefore, the purpose of this research is how the concept of geometry in Toraja carving using qualitative research methods ethnographic approach.

Soft Corners and Sharp Edges: Exploring Inaccurate Geometric Shapes in Children's Picture Books

Given the inaccuracy of children's picture books on shapes published in English, there was a need to analyse Turkish picture books on shapes. In order to carry out the study on the accuracy of Turkish picture books, a rating rubric for English picture books was translated into Turkish. The Traffic Light Rating Rubric rates books as red (explicit inaccuracies), yellow (implicit inaccuracies or missing concepts) and green (correct concepts). As a result of the research, it was found that the findings for the Turkish books were consistent with the previous findings for the English books. In the majority of the books analysed, the geometric shapes were rated as incorrect (red). Given the importance of early mathematical study for long-term success, the Traffic Light Rating Rubric-Turkish Version is intended to provide guidance to book selectors such as librarians, parents and teachers, and ultimately authors and publishers who need to produce texts with the correct form.

The science-specific home learning environment of elementary school children – how are science experiences and science talk associated with the children’s science achievement?

ABSTRACT Background The home learning environment (HLE) is central to children’s development. Activities in the HLE are assumed to mediate the effects of distal factors such as family socio-economic status (SES) and parental beliefs and attitudes on children’s development. However, little is known about the science-specific HLE and the relationship between different parent-child science activities and elementary school children’s science learning. Purpose This study aims to investigate a) how different science activities in the HLE relate to children’s science achievement and b) how they mediate the associations of family SES and parents’ beliefs and attitudes on children’s science achievement. Sample Data from the German subsample of the Trends in Mathematics and Science Study (TIMSS) 2019 was analyzed, including 3,437 4th-grade children (Mage = 10.39 years, 50% boys). Design and methods The study has a cross-sectional design. We used structure equation modeling (SEM) to examine associations and test for indirect effects. Results We identified two different patterns of science activities in the HLE: science experiences (e.g. reading science books, experimenting) and science talk. SEM revealed that science talk was associated with children’s science achievement, whereas science experiences were not. Further, science talk mediated associations of family SES and parental beliefs and attitudes on the children’s science achievement. Conclusions Our results might indicate that discursive elements in science activities, such as parent-child science talk, are crucial for children’s science learning, whereas a mere provision of science experiences may not adequately foster the children. We suggest that future research should focus on analyzing the quality of parent-child interactions in different science activities to understand possible learning processes in these activities better. Overall, although effect sizes were small, our results emphasize the crucial role of the HLE for elementary school children’s science learning.

Enhancing Numerical Simulations through Advanced Data Analysis In Computational Mathematics

Improving numerical models through advanced data analysis in computational mathematics is a major step forward in many areas of science and engineering. The main goal of this study is to find ways to make accuracy, speed, and the ability to predict better by combining advanced data analysis techniques with computer simulation techniques. The study looks at how machine learning algorithms, statistical methods, and traditional computer methods can work together to solve hard, multidimensional problems. Algorithms for machine learning, like neural networks and support vector machines, are used to find trends and connections in very big datasets. These algorithms can give us information that other methods might miss. These new ideas make it easier to make models and programs that are more realistic. Statistical methods, like regression analysis and Bayesian reasoning, are used to measure error and make computer results more reliable. The goal of the study is to lower the cost of computing and raise the rate at which computer models converge by using these methods. A big part of this study is using these advanced methods to solve problems in the real world, like in material science, fluid dynamics, and climate models. Case studies show how combining computer simulations and data analysis can help make predictions that are more accurate and help people make better decisions. The study also looks at the problems that come with how hard these advanced methods are to compute and how much data they need, and it suggests ways to fix these problems. This study shows how mixing advanced data analysis with computer models in computational mathematics can change things. The results show how important it is to use methods from different fields to solve difficult science and engineering issues. This makes it possible for more accurate and useful models that can lead to new ideas and discoveries in many areas. This combination not only makes numerical models more powerful, but it also creates new study and use opportunities in computer mathematics.

Long-Term Health Impacts of Post-COVID Syndrome: Genetic and Epigenetic Modifications Increasing Cancer Risk and Public Health Challenges

The COVID-19 pandemic has had a profound and lasting impact on global health, extending beyond the immediate effects of the acute respiratory illness it initially caused. Post-COVID syndrome, or long COVID, has emerged as a significant health concern, characterized by a wide range of persistent symptoms and complications affecting multiple organ systems. This syndrome poses substantial challenges to both individuals and healthcare systems, with potential long-term consequences that warrant urgent attention and research. Among the most alarming potential outcomes is the risk of a significant increase in oncological diseases. The chronic inflammation, immune dysregulation, and genetic and epigenetic changes induced by prolonged COVID are factors that have long been associated with cancer development. The possibility that these factors could lead to a substantial rise in cancer incidence over the next 5-7 years is a critical area of investigation. Chronic inflammation, a hallmark of long COVID, is a well-established risk factor for cancer. Persistent inflammatory responses can cause DNA damage, promote the activation of oncogenes, and suppress tumor suppressor genes, thereby creating an environment conducive to cancer development. The oxidative stress associated with chronic inflammation can result in significant genetic alterations, including DNA mutations and chromosomal aberrations, further increasing cancer risk. Additionally, epigenetic changes such as DNA methylation, histone modifications, and alterations in non-coding RNA expression can disrupt normal gene regulation, leading to inappropriate activation or silencing of genes involved in cell growth, apoptosis, and immune responses. These epigenetic disruptions can create a cellular environment that favors malignant transformation and cancer progression. Telomere shortening, another consequence of chronic stress and inflammation observed in long COVID patients, is associated with increased cancer risk. Accelerated telomere shortening due to chronic inflammation and oxidative stress can lead to cellular senescence, genomic instability, and increased susceptibility to cancer. Expert opinions from leading oncologists, geneticists, and epidemiologists underscore the urgency of addressing the potential long-term oncological impacts of postCOVID syndrome. Mathematical models and epidemiological studies support concerns that cancer incidence could potentially double due to long COVID, indicating a significant public health challenge.In addition to oncological diseases, long COVID is associated with an increased risk of several other chronic conditions, including cardiovascular diseases, neurological diseases, metabolic disorders, respiratory diseases, autoimmune diseases, and mental health disorders. Addressing these potential increases requires a comprehensive approach, including continued research, enhanced screening and monitoring, public health initiatives, and targeted therapeutic interventions. This abstract highlights the multifaceted impacts of long COVID, emphasizing the need for ongoing vigilance and a coordinated response to mitigate its long-term health consequences. Understanding and addressing these challenges is crucial for improving the quality of life for individuals affected by long COVID and for protecting global health in the post-pandemic era.

A proposal for the study of mathematics teachers’ beliefs through the analysis of their practices

A proposal for the study of mathematics teachers’ beliefs through the analysis of their practices