Aligning Measurement of Personal Attributes for Predicting STEM Education Outcomes

人格心理学，经济学和儿童发展的学者报告了各种实证关系，将人格特质，执行功能（EF）和经济偏好与小学和中学教育中的科学，技术，工程和数学（STEM）课程的入学和表现以及大学STEM专业的选择联系起来。如何衡量这些属性及其与STEM教育成果的关系的相似性和差异尚未得到严格审查。这项跨学科的多国研究将调查儿童特征，EF技能和偏好的演变和共同演变，以确定这些属性如何预测STEM成就以及STEM专业和职业的选择。特别是，它将使用来自美国和中国三个地点的样本的通用测量框架，以及澳大利亚家庭，收入和劳动力动态调查的数据，以了解个人属性如何与STEM教育成就，大学成绩和STEM专业选择相关。它将提供一个更好地了解特定年龄的机会窗口，促进干成就和追求干专业的干预措施。这项研究将产生一套核心的独特属性，用于评估学生和学校，这将是一个有用的诊断工具，教师和学校管理人员辅导学生，促进个性化学习。学生可以获得教学干预的学术和社会效益，重点是特质，技能和偏好的影响。这项研究的主要目标是确定特征，EF技能和偏好的组合，以预测STEM成绩（成绩，考试成绩，教师评级以及大学专业和职业的选择）。为此，首先，本研究将通过线性和非线性因素分析来确定特质、EF技能和偏好之间的关系和维度，从而生成一个简约的个人属性测量系统。其次，它将在不同的实验条件下收集这些个人属性的数据，以量化激励，框架和背景因素如何影响其测量，以便提出预测STEM结果的个人属性的标准化测量。第三，它将纵向检查特质，技能和偏好的增长，从4年级到12年级，无论是平均变化还是预测STEM结果的变化。最后，它将研究包括地理文化环境在内的背景因素如何调节STEM结果与特征，EF技能和偏好之间的关系。本研究对STEM教育研究做出了五个主要贡献：（1）纵向测量进一步加深了我们对特定年龄的干预机会窗口的理解，这些干预措施促进了对STEM感兴趣的属性;（2）检查引发措施之间的关系，可以确定哪些测量策略捕获了相同的结构，并为个体差异的研究增加了真正的新维度。（3）对于每一个结构，使用实验操作使得能够调查激励和环境标准化的定量重要性;（4）通过研究各种引发的，标准化的措施预测干成绩，大学成绩，干专业和职业选择的力量，这项研究将支持学校和教师促进个性化教育和提高学生成绩;和（5）研究跨文化、种族、民族、性别和家庭背景的测量的相似性和差异，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Scholars in personality psychology, economics, and child development report a variety of empirical relationships connecting personality traits, executive functions (EF), and economic preferences to enrollment and performance in science, technology, engineering, and math (STEM) courses in elementary and secondary education and choice of STEM majors in college. The similarities and differences in how these attributes are measured and their relationships to STEM education outcomes have not been rigorously examined. This interdisciplinary multi-country study will investigate the evolution and co-evolution of traits, EF skills, and preferences in children to determine how those attributes predict STEM achievement and the choice of STEM majors and careers. In particular, it will use a common measurement framework across samples from three sites in the United States and China, as well as data from the Australian Household, Income and Labour Dynamics Survey, to find out how personal attributes are related to STEM education achievement, college attainment and STEM major choice. It will provide a better understanding of age-specific windows of opportunity for interventions promoting STEM achievement and the pursuit of STEM majors. This study will produce core sets of distinct attributes to use in assessing students and schools, which will be useful as a diagnostic tool for teachers and school administrators to counsel students and to facilitate personalized learning. Students may reap the academic and social benefits of teaching interventions focused on the impact of traits, skills, and preferences. The main goal of this study is to identify the combination of traits, EF skills, and preferences for predicting STEM achievement (grades, test scores, teacher ratings, as well as choice of college major and career). To do this, first the study will generate a parsimonious measurement system of personal attributes by conducting linear and nonlinear factor analysis to determine the relationship and dimensionality of traits, EF skills, and preferences. Second, it will collect data on these personal attributes in different experimental conditions to quantify how incentives, frames and contextual factors affect their measurement, in order to propose standardized measures for personal attributes that predict STEM outcomes. Third, it will examine trait, skill, and preference growth longitudinally, spanning cohorts from 4th to 12th grade, for both mean change and change in the prediction of STEM outcomes. Finally, it will examine how background factors including geo-cultural settings moderate the relationship between STEM outcomes and traits, EF skills, and preferences. This study makes five major contributions to STEM education research: (1) The longitudinal measurement furthers our understanding of age-specific windows of opportunity for interventions promoting attributes that drive interest in STEM; (2) Examination of relationships across the elicited measures enables determination of which measurement strategies capture the same constructs, and which add genuine new dimensions to the study of individual differences. This study will use a common set of measures across countries; (3) For each construct, use of experimental manipulations enables investigation of the quantitative importance of standardizing for incentives and environments; (4) By studying the power of the various elicited, standardized measures for predicting STEM achievement, college attainment, and STEM major and career choices, this study will support schools and teachers to promote personalized education and improve student outcomes; and (5) The study of similarities and differences in measures across cultural, race, ethnicity, gender, and family background, allows for optimal prediction of STEM outcomes across distinct contexts.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.