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  • 标题:Factors contributing to students and instructors experiencing a lack of time in college calculus
  • 本地全文:下载
  • 作者:Jessica Ellis Hagman ; Estrella Johnson ; Bailey K. Fosdick
  • 期刊名称:International Journal of STEM Education
  • 印刷版ISSN:2196-7822
  • 出版年度:2017
  • 卷号:4
  • 期号:1
  • 页码:12-26
  • DOI:10.1186/s40594-017-0070-7
  • 摘要:BackgroundCalculus is a foundational course for STEM-intending students yet has been shown to dissuade students from pursuing STEM degrees. In this report, we examine factors related to students and instructors reporting a lack of time in class for students to understand difficult ideas and relate this to students’ and instructors’ perceptions of opportunities to learn using a hierarchical linear model. This work is part of the US national study on college calculus, which provides an ideal landscape to examine these questions on a large scale. ResultsWe find a number of student factors associated with students experiencing negative opportunities to learn, such as student gender, lacking previous calculus experience, and reports of poor and non-student-centered teaching. Factors weakly associated with instructor reports of lack of time were a common final and reporting that approximately half of the students lacked the ability to succeed in the course. ConclusionsThis analysis offers insight into how we might create more positive opportunities to learn in our own classrooms. This includes preparing students before they enter calculus, so they feel confident in their abilities, as well as weakening the internal framing of the course by engaging in teaching practices that provide students opportunities to communicate and influence their learning (e.g., discussion and group work). We argue that this is especially important in introductory college calculus courses that are packed with material, taught to a diverse population of students in terms of demographics, mathematical preparation, and career goals.
  • 关键词:Post secondary ; Pacing and coverage ; Internal and external framing ; Opportunities to learn ; Quantitative analysis; hierarchical linear modeling
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