CAREER: Creatively Reimagining Engagements with Data in Biology Learning Environments

人们普遍呼吁高中理科学生以更复杂的方式处理数据，以更好地配合和支持专业科学家和工程师的工作。然而，高中生对科学数据的分析和解释往往在范围、复杂性和真实目的上受到限制。本项目旨在通过采用一种新的方法：贝叶斯数据分析方法，支持和促进学生在高中生物课堂上使用生态数据的工作。这些方法包括表达最初的想法或信念，并用学生访问或记录的数据对其进行定量更新。该项目将使20名高中教师和他们的大约1200名学生能够理解课堂内外的数据。它还将涉及以开放和可访问的方式共享研究成果、用于贝叶斯数据分析的教育技术工具和课程资源。教师通常希望计划和开展更多的数据密集型课堂活动。然而，学生的数据工作与教师希望学生理解的核心科学思想之间需要更多的联系。其结果是，学生处理数据的工作可以与学生对科学的理解分离开来。由于认知科学、数据科学和贝叶斯数据分析工具和方法以及科学课程标准的进步，有机会为科学教师提供实用的工具和教学策略，让学生在科学课堂上更雄心勃勃地使用数据。本项目涉及为20名高中生物教师设计并实施一项多年期专业发展计划，重点关注与生态系统相关的核心科学理念和与当地相关的生态现象和问题。该项目包括与Tremont的大烟山研究所（Great Smoky Mountains Institute）合作，该研究所是美国生物多样性最丰富的国家公园内的体验和户外教育中心。该计划侧重于策略和一个专门设计和开发的统计软件工具，使贝叶斯数据分析更容易为高中学习者使用。本研究还包括一项实地实验，在多年的课堂实施中使用定量和定性措施评估几位教师和学生的成果。该项目有可能提供一套研究成果和策略，使分析和解释数据的科学实践更有利于科学教师和学习者。CAREER奖由探索研究preK-12项目（DRK-12）资助，该项目旨在通过研究和开发创新资源、模型和工具，显著提高preK-12学生和教师对科学、技术、工程和数学（STEM）的学习和教学。DRK-12计划中的项目建立在STEM教育的基础研究和先前的研究和开发工作的基础上，为拟议的项目提供了理论和实证依据。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。

There have been prominent and widespread calls for high school science students to work with data in more complex ways that better align with and support the work of professional scientists and engineers. However, high school students' analysis and interpretation of scientific data is often limited in scope, complexity, and authentic purpose. This project aims to support and advance students' work with ecological data in high school biology classrooms by embracing a new approach: Bayesian data analysis methods. Such methods involve expressing initial ideas or beliefs and updating them quantitatively with data that students access or record. This project will empower 20 high school teachers and their approximately 1,200 students to make sense of data within and beyond classroom contexts. It also will involve sharing research findings, an educational technology tool for Bayesian data analysis, and curricular resources in open and accessible ways.Teachers commonly desire to plan and carry out more data-intensive classroom activities. However, there needs to be more connection between students' work with data and the core science ideas teachers want their students to understand. The result is that students' work with data can be isolated from the sense-making students are doing about science. Because of advances in cognitive science, data science and Bayesian data analysis tools and methods, and science curricular standards, there is an opportunity to provide science teachers with practical tools and teaching strategies for students to use data in science classrooms more ambitiously. This project involves designing and carrying out a multi-year professional development program for 20 high school biology teachers focused on ecosystems-related core science ideas and locally-relevant ecological phenomena and questions. The program includes a collaboration with the Great Smoky Mountains Institute at Tremont, an experiential and outdoor education center in the nation's most biodiverse national park. The program focuses on strategies and a specially designed and developed statistical software tool to make Bayesian data analysis more accessible for high school learners. This study also involves a field experiment that assesses several teacher and student outcomes using quantitative and qualitative measures over multiple years of classroom implementation. This project has the potential to provide a set of research findings and strategies for making the science practice of analyzing and interpreting data more empowering for both science teachers and learners.The CAREER award is funded by the Discovery Research preK-12 program (DRK-12) which seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects.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.