Improving Data Literacy in Undergraduate Biology Education

提高本科生物教育的数据素养

基本信息

项目摘要

This project aims to serve the national interest by increasing data science literacy for life science undergraduate students using new teaching materials that will incorporate data analysis and visualization using computational tools. Undergraduate biology laboratory exercises will be improved by requiring greater critical and quantitative thinking in order to complete the exercises. These data-centric laboratory enhancements will occur at all levels of student learning. While the exercises will still illustrate the same biological concepts, data science will be integrated with the exercises by including data, descriptive statistics, spreadsheet applications, and data visualization tools in the learning activities. In the first and second years of the biology program, students will learn to manage, interpret, and visualize data. Students will be trained in using basic computing tools. As students progress through their biology training, the modules will integrate programming languages as well as more sophisticated data analytic activities. The exercises will facilitate the development and use of quantitative literacy and critical thinking that will also help students gain a deeper understanding of the foundational biological concepts. Findings from this project will guide further curricular improvements in undergraduate biology courses and can potentially be adapted by other STEM fields.Biology majors often avoid mathematics and statistics courses that could aid them in advancing their understanding in biological science while preparing them for data-rich careers in the biotechnology industry. The goal of this project is to transform the current biology curricula by introducing technology-enhanced data-centric laboratories in which students apply mathematical and statistical concepts. Students will benefit by learning how to use computational tools to visualize abstract concepts in biology and by learning the fundamentals of data science. Such changes will include the introduction of the concept of scale transformation, as biology often occurs on a log scale, as observed in bacterial growth, viral spread, the pH scale, and gene expression. Students will learn that log transformations can normalize these data for basic statistics. Students’ understanding and ability to apply and interpret data in a biological context will be enhanced through the introduction of the practical application of transformations and learning how to perform helpful parametric tests like analysis of variance and regression with more complex software. Such improvements will enhance biological concepts while also advancing quantitative and technological reasoning skills. The study will assess student learning by comparing pretests and posttests of biological and data understanding within sections, among sections, and among years so that student progression can be tracked as they advance through the curriculum. This project will lay the groundwork for institutional STEM education improvements that can be implemented in existing courses enabling adoption by other undergraduate institutions. 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.
该项目旨在通过使用新的教材提高生命科学本科生的数据科学素养来服务于国家利益,这些教材将使用计算工具进行数据分析和可视化。本科生物实验练习将通过要求更大的批判性和定量思维来完成练习。这些以数据为中心的实验室增强将发生在学生学习的各个层面。虽然练习仍然会说明相同的生物学概念,但数据科学将通过在学习活动中包括数据,描述性统计,电子表格应用程序和数据可视化工具来与练习相结合。在生物学课程的第一年和第二年,学生将学习管理,解释和可视化数据。学生将接受使用基本计算工具的培训。随着学生在生物学培训中的进步,这些模块将整合编程语言以及更复杂的数据分析活动。这些练习将促进定量素养和批判性思维的发展和使用,这也将帮助学生更深入地理解基本的生物学概念。该项目的研究结果将指导本科生物学课程的进一步课程改进,并可能适用于其他STEM领域。生物学专业的学生通常会避开数学和统计学课程,这些课程可以帮助他们提高对生物科学的理解,同时为生物技术行业中数据丰富的职业生涯做好准备。该项目的目标是通过引入技术增强的以数据为中心的实验室来改变目前的生物课程,学生在实验室中应用数学和统计概念。学生将通过学习如何使用计算工具来可视化生物学中的抽象概念以及学习数据科学的基础知识而受益。这些变化将包括引入规模转化的概念,因为生物学通常以对数规模发生,如在细菌生长、病毒传播、pH值规模和基因表达中所观察到的。学生将了解到对数转换可以将这些数据标准化以用于基本统计。学生的理解和能力,以应用和解释数据在生物学的背景下,将通过引入转换的实际应用和学习如何执行有用的参数测试,如方差分析和回归更复杂的软件得到增强。这些改进将增强生物学概念,同时也提高定量和技术推理技能。该研究将通过比较生物学和数据理解的前测和后测来评估学生的学习情况,这些生物学和数据理解在各部分之间以及各年之间进行,以便在学生通过课程前进时可以跟踪学生的进展。该项目将为机构STEM教育的改进奠定基础,这些改进可以在现有课程中实施,从而使其他本科院校能够采用。NSF IUSE:EHR计划支持研究和开发项目,以提高所有学生STEM教育的有效性。该奖项反映了NSF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。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Zachary Mitchell其他文献

THE WHEEL OF MISFORTUNE : USING GAMBLING TO ASSESS THE LINK BETWEEN RISKY BEHAVIOR AND ATTACHMENT
不幸之轮:利用赌博来评估危险行为和依恋之间的联系
  • DOI:
  • 发表时间:
    2003
  • 期刊:
  • 影响因子:
    0
  • 作者:
    J. Noval;Zachary Mitchell
  • 通讯作者:
    Zachary Mitchell

Zachary Mitchell的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

相似国自然基金

Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
  • 批准号:
  • 批准年份:
    2024
  • 资助金额:
    万元
  • 项目类别:
    合作创新研究团队
Data-driven Recommendation System Construction of an Online Medical Platform Based on the Fusion of Information
  • 批准号:
  • 批准年份:
    2024
  • 资助金额:
    万元
  • 项目类别:
    外国青年学者研究基金项目
Development of a Linear Stochastic Model for Wind Field Reconstruction from Limited Measurement Data
  • 批准号:
  • 批准年份:
    2020
  • 资助金额:
    40 万元
  • 项目类别:
基于Linked Open Data的Web服务语义互操作关键技术
  • 批准号:
    61373035
  • 批准年份:
    2013
  • 资助金额:
    77.0 万元
  • 项目类别:
    面上项目
Molecular Interaction Reconstruction of Rheumatoid Arthritis Therapies Using Clinical Data
  • 批准号:
    31070748
  • 批准年份:
    2010
  • 资助金额:
    34.0 万元
  • 项目类别:
    面上项目
高维数据的函数型数据(functional data)分析方法
  • 批准号:
    11001084
  • 批准年份:
    2010
  • 资助金额:
    16.0 万元
  • 项目类别:
    青年科学基金项目
染色体复制负调控因子datA在细胞周期中的作用
  • 批准号:
    31060015
  • 批准年份:
    2010
  • 资助金额:
    25.0 万元
  • 项目类别:
    地区科学基金项目
Computational Methods for Analyzing Toponome Data
  • 批准号:
    60601030
  • 批准年份:
    2006
  • 资助金额:
    17.0 万元
  • 项目类别:
    青年科学基金项目

相似海外基金

Collaborative Research: Strengthening the OOI Data Labs Community of Practice (CoP) to enhance undergraduate data literacy
协作研究:加强 OOI 数据实验室实践社区 (CoP),以提高本科生的数据素养
  • 批准号:
    2316075
  • 财政年份:
    2023
  • 资助金额:
    $ 39.08万
  • 项目类别:
    Standard Grant
Collaborative Research: Critical Data Stories: Co-Designing Remixing Tools with Teachers to Support Critical Data Literacy with Middle School Youth
合作研究:关键数据故事:与教师共同设计混音工具,以支持中学生的关键数据素养
  • 批准号:
    2302659
  • 财政年份:
    2023
  • 资助金额:
    $ 39.08万
  • 项目类别:
    Standard Grant
Developing an Online Textbook Integrating English, Economics and Data Literacy: Collaboration between Japan, China and Korea
开发一本整合英语、经济学和数据素养的在线教材:日本、中国和韩国之间的合作
  • 批准号:
    23K02464
  • 财政年份:
    2023
  • 资助金额:
    $ 39.08万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
Collaborative Research: Strengthening the OOI Data Labs Community of Practice (CoP) to enhance undergraduate data literacy
协作研究:加强 OOI 数据实验室实践社区 (CoP),以提高本科生的数据素养
  • 批准号:
    2316077
  • 财政年份:
    2023
  • 资助金额:
    $ 39.08万
  • 项目类别:
    Standard Grant
Collaborative Research: Strengthening the OOI Data Labs Community of Practice (CoP) to enhance undergraduate data literacy
协作研究:加强 OOI 数据实验室实践社区 (CoP),以提高本科生的数据素养
  • 批准号:
    2316076
  • 财政年份:
    2023
  • 资助金额:
    $ 39.08万
  • 项目类别:
    Standard Grant
Collaborative Research: Integrating Students’ Interests, Identities and Ways of Knowing with Network Visualization Tools to Explore Data Literacy Concepts
协作研究:将学生的兴趣、身份和认知方式与网络可视化工具相结合,探索数据素养概念
  • 批准号:
    2241706
  • 财政年份:
    2023
  • 资助金额:
    $ 39.08万
  • 项目类别:
    Standard Grant
Data Stories: Co-Designing Curricula and Tools with Teachers to Support Middle School Youth’s Critical Data Literacy through Remixing
数据故事:与教师共同设计课程和工具,通过重新混合来支持中学生的关键数据素养
  • 批准号:
    2302657
  • 财政年份:
    2023
  • 资助金额:
    $ 39.08万
  • 项目类别:
    Standard Grant
Collaborative Research: Critical Data Stories: Co-Designing Remixing Tools with Teachers to Support Critical Data Literacy with Middle School Youth
合作研究:关键数据故事:与教师共同设计混音工具,以支持中学生的关键数据素养
  • 批准号:
    2302658
  • 财政年份:
    2023
  • 资助金额:
    $ 39.08万
  • 项目类别:
    Standard Grant
Promoting Students' Data Literacy through the Creation of Interactive Multimodal Representations of Biometric Data
通过创建生物识别数据的交互式多模态表示来提高学生的数据素养
  • 批准号:
    2241751
  • 财政年份:
    2023
  • 资助金额:
    $ 39.08万
  • 项目类别:
    Standard Grant
Air Pollution Visualizations for Promoting Data Literacy with Middle Schoolers and the Public
空气污染可视化,促进中学生和公众的数据素养
  • 批准号:
    2314109
  • 财政年份:
    2023
  • 资助金额:
    $ 39.08万
  • 项目类别:
    Standard Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了