Exploring Students' Learning of Data Analysis in a Physics for Life Sciences Laboratory Environment

本项目旨在通过研究本科生如何学习数据分析概念和技能来服务于国家利益。具体而言，它将研究学生在健康科学专业的生命科学入门物理课程中的学习情况。为了促进学生的学习，课程整合了科学实践、学科核心思想和横切概念。目前关于学生如何学习数据分析概念和技能的研究有限。为了帮助填补这一空白，该项目旨在了解学生如何分析和解释数据，这是一种对stem相关职业至关重要的科学实践。在这个项目中，数据分析的过程将包括数据收集、清理、操作、处理和解释。该项目的研究部分将深入了解学生对数据分析过程的学习和参与。该项目旨在推进关于数据分析本质的新知识，学生如何从事数据分析，以及如何在其他本科课程中成功实施数据分析教育。此外，它将发展学生如何学习数据科学的理论，从而有助于对数据科学教育的理论理解。该项目的总体目标是促进对本科生如何学习和发展数据分析技能的理解，同时对复杂的生物和物理系统进行推理。为此，它将建立在数学教育、计算思维和本科实验教学方面的互补学术基础上。在这些环境中，分析和解释数据可能是知识构建的主要实践，其目的是通过数据收集、清理、操作、数学和解释的迭代来构建概念理解。本项目将通过课堂观察和访谈，对学生的学习和数据分析的制定进行新的理论认识。这种新的认识将指导基于任务的评估工具和学生态度和看法调查的设计和验证。来自评估和调查的信息将反过来用于洞察学生的观念转变。本计画预期的结果是：1)建立一个关于学生如何学习分析和解释资料的新的理论认识；2)通过建立新的评估工具和调查，提高如何开展数据分析和解释研究的知识；3)在本科生实验室中，提供关于强调分析和解释数据的教学技术实施的新知识；4)在这些环境中为所有学生提供新的学习机会。本项目由美国国家科学基金会改善本科STEM教育计划：教育与人力资源项目支持，该项目支持研究和开发项目，以提高所有学生STEM教育的有效性。该项目处于“参与学生学习”轨道，通过该轨道，该项目支持有前途的实践和工具的创建、探索和实施。该奖项反映了美国国家科学基金会的法定使命，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。

This project aims to serve the national interest by studying how undergraduate students learn data analysis concepts and skills. Specifically, it will study student learning in an introductory physics for life sciences course for health-science majors. To promote student learning, the course integrates scientific practices, disciplinary core ideas, and crosscutting concepts. Limited research currently exists about how students learn data analysis concepts and skills. To help fill this gap, this project seeks to understand how students analyze and interpret data, a scientific practice that is crucial for STEM-related careers. For this project, the process of data analysis will involve data collection, cleaning, manipulation, treatment, and interpretation. The project’s research component will provide insight into students' learning about and engagement with the data analysis process. This project aims to advance new knowledge about the nature of data analysis, how students engage in data analysis, and how to successfully implement data analysis education in other undergraduate courses. In addition, it will develop a theory of how students learn data science, and thus contribute to theoretical understanding of data science education. The overall goal of this project is to advance understanding of how undergraduate students learn and develop data analysis skills while reasoning about complex biological and physical systems. To do so, it will build on complementary scholarship in mathematics education, computational thinking, and undergraduate laboratory instruction. Analyzing and interpreting data can be a primary practice for knowledge building in these settings, where the intention is to build a conceptual understanding through iterations of data collection, cleaning, manipulation, mathematics, and interpretation. This project will use class observations and interviews to develop a new theoretical understanding of students' learning and enactment of data analysis. That new understanding will guide the design and validation of a task-based assessment tool and a student attitudes and perceptions survey. Information from the assessment and survey will in turn be used to gain insight into students' conceptual shifts. The results of this project are expected to: 1) build a new theoretical understanding of how students learn about analyzing and interpreting data; 2) advance knowledge about how to conduct research on analyzing and interpreting data through the building of a new assessment tool and survey; 3) contribute new knowledge about the implementation of instructional techniques that emphasize analyzing and interpreting data in undergraduate labs; and 4) promote new learning opportunities for all students in these settings. This project is supported by the NSF Improving Undergraduate STEM Education Program: Education and Human Resources, which supports research and development projects to improve the effectiveness of STEM education for all students. This project is in the Engaged Student Learning track, through which 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.