Data Detectives: Using Real Data to Solve Real Community Health Problems

数据侦探：使用真实数据解决真实的社区健康问题

• 批准号: 10450211
• 负责人: THERESA W GILLESPIE
• 金额: $ 27万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10450211
• 关键词: Active Learning; Address; Area; Attitude; Big Data; Biological; Biomedical Technology; Black race; COVID-19 pandemic; Communities; Community Health; Computerized Medical Record; Creativeness; Data; Data Science; Data Set; Data Sources; Digital Libraries; Economics; Educational Curriculum; Educational process of instructing; Environment; Evaluation; Exercise; Female; Foundations; Future; Genomics; Geography; Goals; Health; Health Status; Hispanic; Image; Individual; K-12 student; Knowledge; Laboratories; Life Style; Literature; Malignant Neoplasms; Measures; Mentors; Methods; Middle School Student; Minority; Minority Groups; Modeling; Motivation; National Institute of General Medical Sciences; Nature; Needs Assessment; Outcome; Patient Care; Physiological; Policies; Population; Positioning Attribute; Problem-Based Learning; Program Evaluation; Proteomics; Qualitative Evaluations; Quantitative Evaluations; Reproducibility; Research; Rural; Rural Community; STEM career; STEM field; Schools; Science; Self Efficacy; Students; Translating; Underrepresented Minority; Women' s Group; base; career; clinical care; cohort; community involvement; complex data; direct application; education research; experience; health assessment; health disparity; health equity; improved; innovation; junior high school; mathematics content; metabolomics; novel; population based; programs; recruit; science education; skills; social health determinants; socioeconomics; student participation; tool

Abstract Data sciences represent key advances for multiple areas of discovery in science and health. However, despite such vast innovations in data science, as is the case with other STEM fields, key groups are significantly under- represented in the current and projected workforce, particularly female and under-represented minority groups (Hispanic or Black). In addition, individuals from rural communities and lower socio-economic backgrounds are less likely to pursue STEM careers and study data sciences. We hypothesize that providing students with a curriculum focused on using population-level Big Data for community health needs assessment, planning, analysis, evaluation, and application will improve students’ understanding of the importance of science and Big Data beyond the laboratory or classroom. We envision such a program will engage students by making science more applicable. To address the gaps in the literature and the lack of practical tools to teach students how to both use and apply population-based Big Data, we will pursue the following Specific Aims for our new SEPA program, Data Detectives: Using Real Data to Solve Real Community Health Problems: 1) to implement a novel, problem-based, experiential learning curriculum to teach under-represented middle school students science and mathematics content and data science principles with direct application to community-based health issues; 2) to conduct a robust evaluation of the program with measures of student knowledge, attitudes, self- efficacy, and pursuit of future STEM careers; and 3) to prepare for broad dissemination of the curriculum throughout Georgia and the US. This program will provide the foundation for K-12 students to use real data to solve real problems focusing on improving health outcomes for communities. The proposed SEPA program meets three NIGMS priority areas: A) teaching students to use Big Data instills needed computational and quantitative skills; B) the curriculum demonstrates applicability to the real world by using problem-based learning (PBL) to challenge students to solve real community-level heath problems using real population-based data; and C) the program follows a robust mixed methods evaluation plan to measure both quantitative and qualitative outcomes. The Research Education Program plan addresses the three Specific Aims and includes rationale for adaptation of the Problem-Based Learning model; a detailed curriculum aligned with MS NGSS; clear identification of population-based datasets to be used; explicit examples of PBL scenarios; a thorough diversity recruitment plan with access to a large, diverse student applicant pool; and clear input from expert community partners and evaluation experts. The Dissemination Plan will share the curriculum and materials across Georgia and the U.S. The ability to evaluate this curriculum in a cohort of middle school students, to measure its effect on potential for future STEM careers, and then ultimately disseminate it nationally to schools and informal science education programs, has the capacity to impact K-12 educational approaches in new and important ways.

抽象的 数据科学代表了科学和健康领域多个发现领域的关键进展。然而，尽管 数据科学领域如此巨大的创新，与其他 STEM 领域的情况一样，关键群体却严重不足。 在当前和预计的劳动力队伍中的代表性，特别是女性和代表性不足的少数群体 （西班牙裔或黑人）。此外，来自农村社区和社会经济背景较低的个人 不太可能从事 STEM 职业和学习数据科学。我们假设为学生提供 课程重点是使用人口层面的大数据进行社区健康需求评估、规划、 分析、评估和应用将提高学生对科学和大科学重要性的理解 实验室或教室之外的数据。我们设想这样的项目将通过科学创造来吸引学生 更适用。解决文献中的空白和缺乏实用工具来教学生如何 为了使用和应用基于人口的大数据，我们将为新的 SEPA 追求以下具体目标 计划，数据侦探：使用真实数据解决真实的社区健康问题：1) 实施 新颖、以问题为基础的体验式学习课程，用于教授代表性不足的中学生 科学和数学内容以及数据科学原理，直接应用于社区健康 问题; 2）对课程进行全面的评估，衡量学生的知识、态度、自我 效率以及对未来 STEM 职业的追求； 3) 为课程的广泛传播做好准备 整个乔治亚州和美国。该计划将为 K-12 学生使用真实数据提供基础 解决注重改善社区健康状况的实际问题。拟议的 SEPA 计划 满足 NIGMS 的三个优先领域：A) 教学生使用大数据，灌输所需的计算和 定量技能； B）课程通过基于问题的学习展示了对现实世界的适用性 (PBL) 挑战学生使用基于真实人群的数据解决真实的社区级健康问题；和 C) 该计划遵循稳健的混合方法评估计划来衡量定量和定性 结果。研究教育计划解决了三个具体目标，并包括以下理由： 基于问题的学习模型的适应；与 MS NGSS 一致的详细课程；清除 确定要使用的基于人口的数据集； PBL 场景的明确示例；彻底的多样性 招聘计划可以接触到庞大、多样化的学生申请者群体；以及专家群体的明确意见 合作伙伴和评估专家。传播计划将在佐治亚州共享课程和材料 和美国 在一群中学生中评估该课程以衡量其效果的能力 未来 STEM 职业的潜力，然后最终在全国范围内传播到学校和非正式科学 教育计划有能力以新的重要方式影响 K-12 教育方法。