STEMにおけるジェンダーのギャップの長期的分析

Joseph R Cimpian^1,2, Jo R King²

¹Department of Applied Statistics, Social Science, and Humanities, New York University, New York, NY, USA.

Science (New York, N.Y.)

|November 21, 2024

まとめ

数学を重視する学校では STEM分野におけるジェンダーの格差は小さくなりますしかし,これらのギャップは他の機関で増加し,学校の焦点とジェンダーの平等の間の複雑な関係を示しています.

科学分野:

STEM教育について
科学社会学
ジェンダー研究

背景:

科学,技術,工学,数学 (STEM) の分野では男女の格差が続いている.
これらの格差に影響を与える要因を理解することは,公平性を促進するために極めて重要です.

研究の目的:

エンジニアリングとコンピュータサイエンスのプログラムにおけるジェンダーのギャップにおける学校の数学選択性の役割を調査する.
STEMにおけるジェンダー表現にどのように影響を与えるかを特定する.

主な方法:

異なるタイプの教育機関におけるエンジニアリングとコンピュータサイエンスプログラムのジェンダー分布の比較分析.
学校レベルの数学の重点と相関する入学データの統計的検討.

主要な成果:

数学専攻の大学では男女の格差が小さくなります
対照的に数学を専攻しない学校ではこの分野におけるジェンダーの格差が広がっています
STEMにおけるジェンダーの平等と学校の学問的重点との間に重要な相関関係があることを示唆しています

結論:

数学選択的な教育環境は,エンジニアリングとコンピュータサイエンスのジェンダーの平等を高める可能性があります.
STEMにおけるジェンダーのギャップを埋めるための介入は,制度的専門性の影響を考慮する必要があります.
これらの観察された差異の背後にあるメカニズムを探るにはさらなる研究が必要です.

さらに関連する動画

06:57

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Published on: August 9, 2016

11.3K

10:17

Improving Student Outcomes with an Adaptable Molecular Cloning Course-Based Undergraduate Research Experience

Published on: November 15, 2024

949

関連する概念動画

01:07

Multiple Bar Graph

5.1K

As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...

5.1K

01:30

Socioemotional Experience and Gender Development

Social-emotional experiences and cultural influences play significant roles in shaping gender development. During middle childhood, from ages 6 to 11, peer groups become dominant in reinforcing gender norms. Children in this age group often align with same-gender peer groups, which actively encourage behaviors that conform to traditional gender roles. For instance, boys may be discouraged from engaging in activities perceived as feminine, reinforcing culturally dictated norms about masculinity...

02:09

Stereotype Threat and Self-fulfilling Prophecies

37.5K

When we hold a stereotype about a person, we have expectations that he or she will fulfill that stereotype. A self-fulfilling prophecy is an expectation held by a person that alters his or her behavior in a way that tends to make it true. When we hold stereotypes about a person, we tend to treat the person according to our expectations. This treatment can influence the person to act according to our stereotypic expectations, thus confirming our stereotypic beliefs. Research by Rosenthal and...

37.5K

01:17

Two-Way ANOVA

2.6K

The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...

2.6K

01:22

Applications of Life Tables

Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...

01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...

関連する実験動画