A Learning Ecosystem for Teaching High School Students Machine Learning Concepts and Data Science Skills in Healthcare and Medicine

用于向高中生教授医疗保健和医学领域的机器学习概念和数据科学技能的学习生态系统

• 批准号: 2148451
• 负责人: Kathryn Eller
• 金额: $ 149.95万
• 依托单位: EAST BAY EDUCATIONAL COLLABORATIVE
• 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: 2022-08-01 至 2025-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2148451&HistoricalAwards=false
• 关键词: Learning; Ecosystem; Teaching; Students; Machine

Healthcare is rapidly changing into a multidisciplinary field. Data science and artificial intelligence (AI) have become integral for healthcare and medical services. Machine Learning (ML), a branch of AI, is broadly applicable for developing predictive models that drive research, development and healthcare practices. Unintentional bias within the datasets and computer programs used for ML creates healthcare outcomes which benefit some people more than others. This project will develop an innovative and inclusive learning and teaching ecosystem for high school students. The ecosystem consists of educational agencies and teachers, cross-disciplinary expertise from data scientists and medical clinicians, community members and college students from diverse background as mentors. Authentic cross-cultural discussions amongst community members and students will be a key component of students’ learning experience. The ecosystem will provide a data science and ML laboratory course and an annual datathon. Computer science teachers will facilitate the course, and will also receive professional development in problem-based data science approaches. Students in the course will explore student-led, inquiry-based strategies on how to navigate and visualize large healthcare sets using the same programming languages and tools that data scientists use. During the datathon, students will team with their local community members and Science, Technology, Engineering, and Mathematics (STEM) teachers to solve authentic data-driven healthcare issues which are important and personal to them. Community members will share their experiences to ensure all voices are heard. Datathon participants will be introduced to culturally-responsive methods. The project is funded by the Innovative Technology Experiences for Students and Teachers (ITEST), which seeks to engage underrepresented students in technology-rich learning environments, including skills in data literacy, and increase students’ knowledge and interest in information and communication technology (ICT) careers.During the project period, researchers will develop and study a semester-long program that engages up to 1000 Rhode Island high school students, with an emphasis on recruiting racial minorities and young women from 12 Title 1 schools. The researchers will investigate how students engage in the program and datathon, the usability and sustainability of this program, and the enactment of the innovative learning ecosystem. The following questions will guide this study: 1) How do the data laboratory and datathon contribute to student learning and efficacy in data science, and their interest in data science and healthcare careers? 2) What are teachers’ perspectives about the usability and effectiveness, including challenges, of the materials, curriculum, and supports? 3) How do teachers take up and enact the activities and tools to support student learning and interests in data science? Researchers will collect and analyze data using mixed methods, including data from a digital learning platform, surveys, interviews, assessments, and observations. The outcome will include a novel pedagogy for teaching high school students about rapidly evolving technologies. Deliverables will consist of annual professional development for the teachers; a public website for all Rhode Island district leaders, teachers, and parents; a vetted data science and ML laboratory course; and designs of the multidisciplinary, cross-cultural datathon. These will be freely shared and promoted online, presented at professional conferences, and published as research articles in peer-reviewed literature.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.

医疗保健正迅速转变为一个多学科领域。数据科学和人工智能(AI)已成为医疗保健和医疗服务不可或缺的一部分。机器学习(ML)是人工智能的一个分支，广泛适用于开发推动研究、开发和医疗实践的预测模型。ML使用的数据集和计算机程序中的无意偏见会产生医疗保健结果，使一些人比其他人受益更多。该项目将为高中生开发一个创新的、包容性的学习和教学生态系统。该生态系统由教育机构和教师、来自数据科学家和医学临床医生的跨学科专业知识、社区成员和来自不同背景的大学生作为导师组成。社区成员和学生之间真实的跨文化讨论将是学生学习体验的关键组成部分。该生态系统将提供数据科学和ML实验室课程和一年一度的数据马拉松。计算机科学教师将促进这门课程，并将在以问题为基础的数据科学方法中获得专业发展。本课程的学生将探索学生主导的、基于探究的策略，了解如何使用数据科学家使用的相同编程语言和工具导航和可视化大型医疗保健集。在数据马拉松期间，学生将与当地社区成员和科学、技术、工程和数学(STEM)教师合作，解决真正的数据驱动的医疗保健问题，这些问题对他们来说是重要的和个人的。社区成员将分享他们的经验，以确保所有声音都能被听到。将向数据马拉松参与者介绍响应文化的方法。该项目由学生和教师创新技术体验(ITEST)资助，旨在让未被充分代表的学生在技术丰富的学习环境中学习，包括数据素养方面的技能，并增加学生对信息和通信技术(ICT)职业的知识和兴趣。在项目期间，研究人员将开发和研究一个为期一学期的计划，吸引多达1000名罗德岛高中学生，重点是从12所第一标题学校招收少数族裔和年轻女性。研究人员将调查学生如何参与该计划和数据马拉松，该计划的可用性和可持续性，以及创新学习生态系统的建立。以下问题将指导本研究：1)数据实验室和数据马拉松如何促进学生在数据科学方面的学习和效率，以及他们对数据科学和医疗保健职业的兴趣？2)教师对材料、课程和支持的可用性和有效性，包括挑战有什么看法？3)教师如何采取和实施活动和工具，以支持学生对数据科学的学习和兴趣？研究人员将使用混合方法收集和分析数据，包括来自数字学习平台的数据、调查、访谈、评估和观察。其成果将包括一种新的教学方法，向高中生传授快速发展的技术。交付成果将包括教师的年度专业发展；面向罗德岛州所有地区领导人、教师和家长的公共网站；经过审查的数据科学和ML实验室课程；以及多学科、跨文化的数据马拉松的设计。这些奖项将在网上免费分享和推广，在专业会议上发表，并作为同行评议文献的研究文章发表。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。