Collaborative Research: Improving Student Learning Outcomes in Computer Science Theory Courses Using Conceptual Models

协作研究：使用概念模型提高计算机科学理论课程中学生的学习成果

• 批准号: 2135431
• 负责人: Lane Hemaspaandra
• 金额: $ 16.28万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2135431&HistoricalAwards=false
• 关键词: Collaborative; Research; Improving; Student; Learning

This project aims to serve the national interest by improving college students’ understanding of concepts in computer science theory courses. These foundational courses are designed to help students learn how to formalize computational problems, build efficient algorithms to solve problems, and argue convincingly, when a problem is so complex that efficient algorithms are unlikely to exist. However, students have difficulty in trying to understand the theoretical concepts in these courses, which can create a significant barrier to student success. While conceptual models have been widely adopted in primary and secondary STEM education to help students make sense of important concepts, their use in computer science education has not been well explored. This project will study the impact of teaching students how to create their own conceptual models of theories. It is expected that the project will result in measurable improvements in student understanding of theoretical concepts and ability to create well-conceived conceptual models. This project will also provide insights for instructors on how to effectively integrate conceptual models in existing computer science theory courses. Students who understand the theoretical concepts will be better prepared to address the complex computational problems that they will encounter in the computing workforce. The goal of this project is to improve student learning outcomes related to the theory of computing, particularly with respect to understanding, skill sets, and ability to apply theory to new situations. This study is grounded in constructivist theories of learning and previous work on the use of modeling in STEM education. After presenting a theoretical concept in a class, a set of open-ended questions will be posed to students who will be asked to draw a conceptual model and then iteratively revise it as they address the open-ended questions. The research questions that will be addressed by this study are: (1) What is the quality of models produced by students? (2) How do the models impact student understanding of theoretical concepts? (3) How do the models affect instructors’ teaching? To answer these questions, the project team will use mixed research methods to analyze qualitative and quantitative data including student survey responses, assessments of student models, student performance on exams, and weekly discussions with instructors. Educational materials will be made available to the STEM education community through a public online repository and project results will be presented at computer science education conferences. 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.

该项目旨在通过提高大学生对计算机科学理论课程中概念的理解来服务于国家利益。这些基础课程旨在帮助学生学习如何形式化计算问题，构建有效的算法来解决问题，并在问题如此复杂以至于高效算法不太可能存在的情况下进行令人信服的辩论。然而，学生在试图理解这些课程中的理论概念方面存在困难，这可能会对学生的成功造成重大障碍。虽然概念模型在中小学STEM教育中被广泛采用，以帮助学生理解重要的概念，但在计算机科学教育中的应用还没有得到很好的探索。这个项目将研究影响教学的学生如何创建自己的概念模型的理论。预计该项目将在学生对理论概念的理解和创建构思良好的概念模型的能力方面产生显着的改善。该项目还将为教师提供关于如何有效地将概念模型整合到现有计算机科学理论课程中的见解。理解理论概念的学生将更好地准备解决他们在计算工作中将遇到的复杂计算问题。这个项目的目标是提高学生与计算理论相关的学习结果，特别是在理解、技能设置和将理论应用于新情况的能力方面。本研究基于建构主义学习理论和前人在STEM教学中使用建模的研究成果。在课堂上提出理论概念后，将向学生提出一组开放式问题，学生将被要求绘制概念模型，然后在回答开放式问题时反复修改模型。本研究将解决的研究问题是：(1)学生制作的模型的质量如何？(2)模型如何影响学生对理论概念的理解？(3)模型如何影响教师的教学？为了回答这些问题，项目团队将使用混合研究方法来分析定性和定量数据，包括学生调查答复、对学生模型的评估、学生在考试中的表现以及每周与教师的讨论。将通过公共在线储存库向STEM教育界提供教育材料，并将在计算机科学教育会议上介绍项目成果。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。