Gender gap in STEM: a cross-sectional study of primary school students’ self-perception and test anxiety in mathematics

Contribution: Significant gender differences are observed on primary school students’ perception of self-efficacy and test anxiety in mathematics. Girls perceive themselves to be significantly worse than boys in mathematics and report higher test anxiety toward mathematics exams. Gender differences in self-efficacy become more pronounced as students grow up, and test anxiety increases for all students. However, the present study shows that teachers’ do not perceive differences in self-efficacy in mathematics between boys and girls. Background: The low presence of women in science, technology, engineering, and mathematics (STEM) might be explained by the attitude of young students toward mathematics. Different studies show that girls are less interested in STEM areas than boys during secondary school. A study on the reasons for this fact pointed out that the early years of education can provide a relevant insight to reverse the situation. Research Questions: Is there any age-dependent gender difference in primary school students in aspects related to mathematics? Are teachers aware of students’ perceptions? Methodology: This work presents a study of over 2000 primary school students (6–12 years old) and 200 teachers in Aragón (Spain). The study consists of a survey on aspects that influence the experience of female and male students with mathematics and Spanish language for comparison purposes and teacher’s awareness of students’ perception. Findings: The present study shows that during primary school, girls are more likely to experiment a negative attitude toward mathematics than boys as they grow up, and teachers may not perceive girls’ situation. La baja presencia de mujeres en ciencia, tecnología, la ingeniería y las matemáticas (STEM) podrían explicarse por la actitud de las niños y niñas hacia las matemáticas. Diferentes estudios muestran que las niñas están menos interesadas en las áreas STEM que niños cuando cursan educación secundaria. Además, un estudio sobre los motivos para este hecho señaló que los primeros años de educación podrían proporcionar una visión relevante para revertir la situación. Por ello, este trabajo parte de las siguientes preguntas de investigación, ¿Existe alguna diferencia de género que sea dependiente de la edad en estudiantes de educación primaria en aspectos relacionados con las matemáticas? ¿Conoce el profesorado la autopercepción de sus estudiantes? Las principales contribuciones de este trabajo son que las diferencias significativas de género se observan en la percepción de autoeficacia de los estudiantes de primaria y ansiedad ante los exámenes en matemáticas. Las niñas se perciben a sí mismas significativamente peor que los niños en matemáticas e indican mayor ansiedad ante los exámenes de matemáticas. Las diferencias de género en la autoeficacia se vuelven más pronunciada a medida que los estudiantes crecen, mientras que la ansiedad ante los exámenes aumenta para todos los estudiantes. Pese a estos resultados, el presente estudio muestra que los profesores no perciben diferencias en la autoeficacia en matemáticas entre niños y niñas. Este estudio se basa en las encuestas realizadas a más de 2000 escolares (6-12 años) y 200 profesores en Aragón (España). El estudio consiste en una encuesta a los estudiantes sobre aspectos que pueden influir en la experiencia de los niños y niñas con las matemáticas, así como con la lengua española para disponer de una materia que permita establecer comparaciones y una encuesta al profesor que incluye cuestiones sobre su percepción de los estudiantes. El principal hallazgo del estudio es que, durante la escuela primaria, es más probable que las niñas experimenten una actitud negativa hacia matemáticas que los niños a medida que crecen, y que los maestros pueden no ser conscientes de la situación de las niñas

A machine learning emulator for Lagrangian particle dispersion model footprints: a case study using NAME

Lagrangian particle dispersion models (LPDMs) have been used extensively to calculate source-receptor relationships (“footprints”) for use in applications such as greenhouse gas (GHG) flux inversions. Because a single model simulation is required for each data point, LPDMs do not scale well to applications with large data sets such as flux inversions using satellite observations. Here, we develop a proof-of-concept machine learning emulator for LPDM footprints over a ∼ 350 km × 230 km region around an observation point, and test it for a range of in situ measurement sites from around the world. As opposed to previous approaches to footprint approximation, it does not require the interpolation or smoothing of footprints produced by the LPDM. Instead, the footprint is emulated entirely from meteorological inputs. This is achieved by independently emulating the footprint magnitude at each grid cell in the domain using gradient-boosted regression trees with a selection of meteorological variables as inputs. The emulator is trained based on footprints from the UK Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME) for 2014 and 2015, and the emulated footprints are evaluated against hourly NAME output from 2016 and 2020. When compared to CH4 concentration time series generated by NAME, we show that our emulator achieves a mean R-squared score of 0.69 across all sites investigated between 2016 and 2020. The emulator can predict a footprint in around 10 ms, compared to around 10 min for the 3D simulator. This simple and interpretable proof-of-concept emulator demonstrates the potential of machine learning for LPDM emulation.</p

Serial Quantitative PCR Assay for Detection, Species Discrimination, and Quantification of Leishmania spp. in Human Samples▿

The Leishmania species cause a variety of human disease syndromes. Methods for diagnosis and species differentiation are insensitive and many require invasive sampling. Although quantitative PCR (qPCR) methods are reported for leishmania detection, no systematic method to quantify parasites and determine the species in clinical specimens is established. We developed a serial qPCR strategy to identify and rapidly differentiate Leishmania species and quantify parasites in clinical or environmental specimens. SYBR green qPCR is mainly employed, with corresponding TaqMan assays for validation. The screening primers recognize kinetoplast minicircle DNA of all Leishmania species. Species identification employs further qPCR set(s) individualized for geographic regions, combining species-discriminating probes with melt curve analysis. The assay was sufficient to detect Leishmania parasites, make species determinations, and quantify Leishmania spp. in sera, cutaneous biopsy specimens, or cultured isolates from subjects from Bangladesh or Brazil with different forms of leishmaniasis. The multicopy kinetoplast DNA (kDNA) probes were the most sensitive and useful for quantification based on promastigote standard curves. To test their validity for quantification, kDNA copy numbers were compared between Leishmania species, isolates, and life stages using qPCR. Maxicircle and minicircle copy numbers differed up to 6-fold between Leishmania species, but the differences were smaller between strains of the same species. Amastigote and promastigote leishmania life stages retained similar numbers of kDNA maxi- or minicircles. Thus, serial qPCR is useful for leishmania detection and species determination and for absolute quantification when compared to a standard curve from the same Leishmania species