A population level analysis of the gender gap in mathematics: Results on over 13 million children using the INVALSI dataset

The impact of math anxiety and self-efficacy in middle school STEM choices: A 3-year longitudinal study

INTRODUCTION

In today's world, which is progressively oriented towards science and technology and facing a growing demand for skilled professionals, it becomes essential to identify the factors that encourage individuals to pursue careers in STEM fields (Science, Technology, Engineering and Mathematics). Previous research has shown that affective-motivational factors, math performance and gender influence STEM occupational and academic choices in adulthood. However, few studies examined how these factors may influence STEM choices as early as middle school. This study aims to assess how math anxiety, math self-efficacy, math performance and gender influence STEM school choices during middle school.

METHODS

We longitudinally assessed a group of 109 students (Year 6) over three school years, with measurements taken on three different occasions.

RESULTS

Findings indicated that individuals who made an STEM school choice experienced lower math anxiety, higher self-efficacy and math performance and were predominantly male. Furthermore, the results indicated that both math anxiety in Year 7 and self-efficacy in Year 6 made the most substantial unique contributions to the STEM school choice.

CONCLUSION

Math anxiety and math self-efficacy seem to be both crucial in influencing middle school students' STEM choices, offering new perspectives for early interventions aimed at promoting more informed school choices.

Progress and trends in neurological disorders research based on deep learning

In recent years, deep learning (DL) has emerged as a powerful tool in clinical imaging, offering unprecedented opportunities for the diagnosis and treatment of neurological disorders (NDs). This comprehensive review explores the multifaceted role of DL techniques in leveraging vast datasets to advance our understanding of NDs and improve clinical outcomes. Beginning with a systematic literature review, we delve into the utilization of DL, particularly focusing on multimodal neuroimaging data analysis-a domain that has witnessed rapid progress and garnered significant scientific interest. Our study categorizes and critically analyses numerous DL models, including Convolutional Neural Networks (CNNs), LSTM-CNN, GAN, and VGG, to understand their performance across different types of Neurology Diseases. Through particular analysis, we identify key benchmarks and datasets utilized in training and testing DL models, shedding light on the challenges and opportunities in clinical neuroimaging research. Moreover, we discuss the effectiveness of DL in real-world clinical scenarios, emphasizing its potential to revolutionize ND diagnosis and therapy. By synthesizing existing literature and describing future directions, this review not only provides insights into the current state of DL applications in ND analysis but also covers the way for the development of more efficient and accessible DL techniques. Finally, our findings underscore the transformative impact of DL in reshaping the landscape of clinical neuroimaging, offering hope for enhanced patient care and groundbreaking discoveries in the field of neurology. This review paper is beneficial for neuropathologists and new researchers in this field.

Talent denied: Equity and excellence gaps in STEMM

In principle, there could be STEMM talent everywhere if there were sufficient and adequate opportunities and learning resources everywhere. The reality, however, is that the likelihood of developing one's talent in STEMM is tied to membership in social groups. In this contribution, we explore the implications of this statement with multiple examples for different social groups and for different stages of talent development. We propose an educational framework model for analyzing equity gaps in STEMM talent development that identifies and systematizes the unequal and inequitable distribution of resources and opportunities as the proximal cause of the emergence of such equity gaps. Furthermore, we discuss important aspects for closing equity gaps in STEMM talent development. We argue that-similar to public health approaches-the focus in establishing equity in STEMM talent development should be on prevention rather than intervention. We discuss the importance of the cooperation of societal subsystems and argue for the use of adequate methods of disparity detection for creating equal opportunities. We also outline why preventive strategies are crucial for the creation of resource parity and explain why outcome standards should be considered obligatory.

The Impasse on Gender Differences in Intelligence: a Meta-Analysis on WISC Batteries

This meta-analysis reviews 79 studies (N = 46,605) that examined the existence of gender difference on intelligence in school-aged children. To do so, we limited the literature search to works that assessed the construct of intelligence through the Wechsler Intelligence Scales for Children (WISC) batteries, evaluating eventual gender differences in indices and subtests. The theoretical framework we adopted is the cross-battery approach which locates cognitive abilities into different levels, also considering the possible mediating effect of the version of the WISC being used. As for broad abilities, a notable discrepancy emerged in favour of males for visual and crystallized intelligence, while female/male differences on fluid intelligence were negligible. Conversely, females’ performance on the processing speed factor was superior. Interesting results emerged at the subtest levels, albeit with less pronounced differences in performance. Results generally showed that older versions of WISC batteries displayed larger gender differences compared to the most recent ones.

What does gender has to do with math? Complex questions require complex answers

Whether mathematics is a gendered domain or not is a long-lasting issue bringing along major social and educational implications. The females' underrepresentation in science, technology, engineering, and mathematics (STEM) has been considered one of the key signs of the math gender gap, although the current view largely attributes the origin of this phenomenon to sociocultural factors. Indeed, recent approaches to math gender differences reached the universal conclusion that nature and nurture exert reciprocal effects on each other, establishing the need for approaching the study of the math gender issue only once its intrinsic complexity has been accepted. Building upon a flourishing literature, this review provides an updated synthesis of the evidence for math gender equality at the start, and for math gender inequality on the go, challenging the role of biological factors. In particular, by combining recent findings from different research areas, the paper discusses the persistence of the "math male myth" and the associated "female are not good at math myth," drawing attention to the complex interplay of social and cultural forces that support such stereotypes. The suggestion is made that longevity of these myths results from the additive effects of two independent cognitive biases associated with gender stereotypes and with math stereotypes, respectively. Scholars' responsibility in amplifying these myths by pursuing some catching lines of research is also discussed.

Learning a new geometric concept: The role of working memory and of domain-specific abilities

It has been suggested that not only domain‐specific factors but also working memory (WM) may play a crucial role in mathematical learning included Geometry, but the issue has not been deeply explored. In the present study, we examined the role of domain‐specific factors and of verbal versus visuospatial WM on geometric learning of a new geometrical figure (trapezoid), never presented previously by the teachers participating to the study, after a lecture also involving manipulatives. Results on 105 children in their Year 4 indicated that not only some domain‐specific components (geometric declarative knowledge and calculation) but also visuospatial working memory had a significant specific impact on the ability of solving geometric problems requiring to calculate the perimeter and the area of the new figure. On the contrary, verbal WM and geometrical mental imagery did not offer a specific contribution. These findings could have important educational implications, stressing the importance of taking into account the main different aspects supporting the acquisition of geometry.