Journal ArticleDOI
Why Students Choose STEM Majors Motivation, High School Learning, and Postsecondary Context of Support
TLDR
In this article, a conceptual framework for understanding the entrance into science, technology, engineering, and mathematics (STEM) majors by recent high school graduates attending 4-year institutions was proposed.Abstract:
This study draws upon social cognitive career theory and higher education literature to test a conceptual framework for understanding the entrance into science, technology, engineering, and mathematics (STEM) majors by recent high school graduates attending 4-year institutions. Results suggest that choosing a STEM major is directly influenced by intent to major in STEM, high school math achievement, and initial postsecondary experiences, such as academic interaction and financial aid receipt. Exerting the largest impact on STEM entrance, intent to major in STEM is directly affected by 12th-grade math achievement, exposure to math and science courses, and math self-efficacy beliefs—all three subject to the influence of early achievement in and attitudes toward math. Multiple-group structural equation modeling analyses indicated heterogeneous effects of math achievement and exposure to math and science across racial groups, with their positive impact on STEM intent accruing most to White students and least ...read more
Citations
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Unintended Short- and Longer-Term Consequences of Assignment to College Readiness Courses: Evidence from Florida:
TL;DR: High school course selection can affect student outcomes in high school, college, and beyond as mentioned in this paper, and policymakers therefore must consider whether policies affecting course selection may have unintended consequences on student outcomes.
Journal ArticleDOI
Motivation Toward Novel Learning Content: Testing the Predictive Validity of School-Based Motivation
Julia Gorges,Enya M. Weidner +1 more
TL;DR: This paper investigated the predictive validity of school-subject-specific self-concepts of ability, intrinsic task values, and cost (operationalized as task effort) for motivation regarding unclas...
How to Develop Alaska Native STEM Students in Middle School and High School
TL;DR: Michele Yatchmeneff as discussed by the authors is an Assistant Professor of Civil Engineering at the University of Alaska Anchorage who worked in Alaska's construction and engineering industry specializing in water and sewer projects in remote villages across the state.
Journal ArticleDOI
Examining the relationship between AP STEM course‐taking and college major selection: Gender and racial differences
Elizabeth Jewett,Rong Chen +1 more
TL;DR: To meet the growing demand for the science, technology, engineering, and mathematics (STEM) workforce and remain competitive in the fields of science and technology over the next decade, the United States is expected to need more than 1 million additional STEM professionals as mentioned in this paper .
References
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Book
Principles and Practice of Structural Equation Modeling
TL;DR: The book aims to provide the skills necessary to begin to use SEM in research and to interpret and critique the use of method by others.
Book
Structural Equation Modeling With Mplus: Basic Concepts, Applications, And Programming
TL;DR: Structural Equation Models: The Basics using the EQS Program and testing for Construct Validity: The Multitrait-Multimethod Model and Change Over Time: The Latent Growth Curve Model.
Book
Structural equation modeling with AMOS: basic concepts, applications, and programming
TL;DR: In this article, the EQS program is used to test the factorial verifiability of a theoretical construct and its invariance to a Causal Structure using the First-Order CFA model.
Book
Social Foundations of Thought and Action: A Social Cognitive Theory
TL;DR: In this paper, models of Human Nature and Casualty are used to model human nature and human health, and a set of self-regulatory mechanisms are proposed. But they do not consider the role of cognitive regulators.
Journal ArticleDOI
Power analysis and determination of sample size for covariance structure modeling.
TL;DR: In this article, a framework for hypothesis testing and power analysis in the assessment of fit of covariance structure models is presented, where the value of confidence intervals for fit indices is emphasized.