Collaborative Research: Understanding the Impact of Low Prerequisite Proficiency on Student Success in Computer Science

协作研究:了解低先决条件熟练程度对学生在计算机科学方面取得成功的影响

基本信息

项目摘要

This project aims to serve the national interest by studying the impact of low prerequisite proficiency on student success in computer science degree programs. It is a well-established fact that academia is not graduating enough students who are qualified to fill the growing employment needs in the technical workforce. In computer science, courses often require students to have completed preparatory courses called prerequisites. Little is known about the effect of variable prerequisite proficiency on student success in subsequent courses. Surprisingly, despite strong grades in prerequisite courses, many students are judged to have poor prerequisite proficiency. This project will focus on answering three key questions: what the impact of low prerequisite proficiency is on student success in later courses, what are the causes of low prerequisite proficiency, and what is the extent of the problem. These findings will result in a better understanding of how prerequisite proficiency impacts student success, which will be used to improve computer science degree programs and increase the number of students qualified to fill employment needs in the technical workforce.Studies of student performance have shown that students from underrepresented groups in computing (e.g., women, minorities, transfer students, first-generation students) may be disproportionately impacted by low prerequisite proficiency. The PIs will examine how students from groups underrepresented in computing are impacted relative to students from represented groups and will conduct a series of studies involving faculty and student interviews as well as designing and administering assessments of prerequisite preparation in multiple courses at each institution involved in the study. Using a combination of qualitative and quantitative methods, this multi-institutional project will determine the causes of low prerequisite preparation, the ubiquity of the problem, and the impact of low prerequisite preparation on student outcomes (exam scores, grades, retention). The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.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.
这个项目旨在通过研究低必备熟练程度对学生在计算机科学学位课程中成功的影响来服务于国家利益。一个公认的事实是,学术界没有培养出足够多的有资格满足技术劳动力日益增长的就业需求的学生。在计算机科学中,课程通常要求学生完成预科课程,称为先决条件。人们对不同的前提条件熟练程度对学生在后续课程中取得成功的影响知之甚少。令人惊讶的是,尽管必修课的成绩很好,但许多学生的必修课水平却很差。这个项目将集中回答三个关键问题:学前教育水平低对学生在后续课程中的成功有什么影响,造成学前教育水平低的原因是什么,以及问题的程度有多大。这些发现将有助于更好地理解必备熟练程度如何影响学生的成功,这将被用来改进计算机科学学位课程,并增加有资格满足技术工作人员就业需求的学生数量。对学生表现的研究表明,来自计算机领域代表性较低的群体(如女性、少数族裔、转学学生、第一代学生)的学生可能会受到较低的先决条件熟练程度的不成比例的影响。PIS将研究计算机专业代表性不足群体的学生相对于代表群体的学生受到的影响,并将进行一系列研究,包括对教职员工和学生的访谈,以及设计和管理对参与研究的每个机构的多门课程的先决条件准备情况的评估。使用定性和定量相结合的方法,这个多机构项目将确定低先决条件准备的原因、问题的普遍性,以及低先决条件准备对学生结果(考试成绩、成绩、保留率)的影响。NSF IUSE:EHR计划支持研究和开发项目,以提高所有学生的STEM教育的有效性。通过参与的学生学习路径,该计划支持有前景的实践和工具的创建、探索和实施。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
A Demographic Analysis on Prerequisite Preparation in an Advanced Data Structures Course
An Exploration of Student-Tutor Interactions in Computing
计算中师生互动的探索
Instructor Perspectives on Prerequisite Courses in Computing
教师对计算机必修课程的看法
Student Expectations of Tutors in Computing Courses
学生对计算机课程导师的期望
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Jelena Trajkovic其他文献

A Graph Based Algorithm for Data Path Optimization in Custom Processors
定制处理器中数据路径优化的基于图的算法
Early performance-cost estimation of application-specific data path pipelining
特定于应用程序的数据路径流水线的早期性能成本估计
Automatic Data Path Generation from C code for Custom Processors
从 C 代码自动生成定制处理器的数据路径
Impact of flipped labs and lectures on student outcomes during the pandemic for a lower division Computer Engineering course
疫情期间翻转实验室和讲座对低年级计算机工程课程学生成绩的影响
Generation of Custom Co-processor Structure from C-Code
从 C 代码生成自定义协处理器结构
  • DOI:
  • 发表时间:
    2008
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Jelena Trajkovic;D. Gajski
  • 通讯作者:
    D. Gajski

Jelena Trajkovic的其他文献

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{{ truncateString('Jelena Trajkovic', 18)}}的其他基金

Collaborative Research: Equity of Access to Computer Science: Factors Impacting the Characteristics and Success of Undergraduate CS Majors
合作研究:获得计算机科学的公平性:影响本科计算机科学专业特征和成功的因素
  • 批准号:
    2031911
  • 财政年份:
    2020
  • 资助金额:
    $ 3.41万
  • 项目类别:
    Standard Grant

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    10774081
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