We expand on prior qualitative research about mathematics identity development using data from a large national US survey of 10,437 students in 336 college calculus classes. Multinomial logistic regression models find that a stronger mathematics identity predicts higher student interest in pursuing certain STEM careers when compared with non-STEM careers, particularly those in physical and computer sciences, engineering, and mathematics, or in science or mathematics teaching. Multiple linear regression models identify that certain instructional practices employed by high school mathematics teachers predict higher levels of students’ mathematics identity. These include a high amount of interaction within the classroom, a focus on mathematics connections, and activities involving conceptual learning. Surprisingly, the role of the textbook, ways of organizing students (individual, small group, whole class), forms of assessment, and use of calculators or computers did not significantly predict students’ mathematics identity.

我们使用来自美国对 336 个大学微积分课程的 10,437 名学生进行的大型全国性调查的数据，扩展了先前关于数学身份发展的定性研究。多项逻辑回归模型发现，与非 STEM 职业，尤其是物理和计算机科学、工程和数学，或者科学或数学教学中的职业相比，更强的数学身份预测学生对追求某些 STEM 职业的兴趣更高。多元线性回归模型确定高中数学教师采用的某些教学实践预测学生的数学认同水平更高。其中包括课堂内的大量互动、对数学联系的关注以及涉及概念学习的活动。出人意料的是，教科书的作用，