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发现多个发现领域的关键进展。但是，需求 数据科学的巨大创新，与其他STEM领域一样，关键群体明显不足 在当前和预计的劳动力中代表，尤其是女性和代表性不足的少数群体 （西班牙裔或黑色）。此外，来自粗糙社区和较低社会经济背景的个人是 从事STEM职业和研究数据科学的可能性较小。我们假设为学生提供 课程专注于使用人群级的大数据进行社区健康需求评估，计划， 分析，评估和应用将提高学生对科学和大型重要性的理解 超越实验室或教室的数据。我们设想这样的计划将通过制作科学来吸引学生 更适用。解决文献中的差距以及缺乏实用工具来教学学生如何 使用和应用基于人群的大数据，我们将针对我们的新SEPA实现以下特定目标 程序，数据侦探：使用实际数据解决实际社区健康问题：1）实施 小说，基于问题的，经验丰富的学习课程，教导不足的中学生 科学和数学内容和数据科学原则，直接应用于社区的健康 问题; 2）通过衡量学生知识，吸引力，自我的衡量标准，对计划进行强有力的评估 功效，并追求未来的STEM职业； 3）准备大量分发课程 通过佐治亚州和美国。该计划将为K-12学生使用真实数据为 解决着专注于改善社区健康成果的真正问题。拟议的SEPA计划 符合三个裸体优先领域：a）教学学生使用大数据灌输需要的计算和 定量技能； b）课程通过使用基于问题的学习来展示对现实世界的适用性 （PBL）挑战学生使用基于人群的数据解决真正的社区级别的卫生问题；和 c）该计划遵循强大的混合方法评估计划，以衡量定量和定性 结果。研究教育计划计划针对三个特定目标，包括 改编基于问题的学习模型；与MS NGSS对齐的详细课程；清除 识别要使用的基于人群的数据集； PBL方案的明确示例；彻底的多样性 招聘计划，可以使用一个大型的学生申请人池；并明确了专家社区的意见 合作伙伴和评估专家。传播计划将共享佐治亚州的课程和材料 美国有能力在一群中学生中评估该课程的能力，以衡量其效果 关于未来STEM职业的潜力，然后最终在全国范围内将其传播给学校和非正式科学 教育计划有能力以新的和重要的方式影响K-12教育方法。