Building STEM Skills by Integrating Data Literacy and Text Analytics in English Language Arts

通过在英语语言艺术中整合数据素养和文本分析来培养 STEM 技能

• 批准号: 2241483
• 负责人: Victor Lee
• 金额: $ 50万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2241483&HistoricalAwards=false
• 关键词: Building; STEM; Skills; Integrating; Data

Ubiquitous computing and new technologies are infusing fields that have not historically been identified as STEM-related, such as the humanities, with new tools and practices involving data. This data infusion is opening up new opportunities to use data literacy instruction to augment instruction already required for compulsory humanities subjects, such as English language arts. Additionally, the potential for improving students’ data literacy through the analysis of textual data is directly relevant to increasing future participation and success in STEM fields and professions. For example, word frequency visualizations and analyses are fundamental text analytics techniques that are at the core of many new language technologies, such as language model-based chatbots, and emerging STEM-related careers, such as data journalism. Text analytics techniques are also changing how language is understood and analyzed by digital humanists, who use such analytics to make patterns and tendencies in language use visible. The next generation workforce needs to be prepared to work with language and text as data. The goal of this project is to develop and research the potential of integrated data literacy and text analytics instruction in middle school English language arts. This integration may make fundamental data literacy practices more accessible for students who are not taking related STEM coursework, provide new resources to help students with reading comprehension, and create new pathways for greater participation in STEM fields and professions.Through co-design, classroom research, and participatory professional development with practicing English Language Arts teachers, this project will be developing text analytics tools, visualizations, and techniques that are well-suited and tested for use in English Language Arts middle school classrooms. Changes in student understandings related to data literacy and textual literacy as a result of using these new resources will be documented through clinical interviews and analyzed across multiple years of the project. Changes in student awareness and perception of STEM skills and data-intensive careers will also be surveyed and studied across multiple years. In addition to creating new pathways for STEM skill development, a key hypothesis of this project is that the introduction of data literacy activities in English Language Arts can also make text comprehension more accessible for students who are still developing their English language fluency. These data literacy activities provide graphical and visualization resources for recognizing important features of text and language. A majority Hispanic/Latino public school district with almost a third of its student body classified as English Language Learners, is partnering with the university-based research team, will be participating in yearlong teacher professional development activities, and will be providing cycles of feedback and improvement throughout the project. An estimated 3000 public middle school students will be using innovative data literacy integrations in their English language arts classes. This project is funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers.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.

普适计算和新技术正在将涉及数据的新工具和实践注入到历史上未被确定为与stem相关的领域，如人文学科。这种数据的注入为使用数据素养教学提供了新的机会，以增加已经要求的必修人文学科（如英语语言艺术）的教学。此外，通过文本数据分析提高学生数据素养的潜力与增加未来STEM领域和专业的参与和成功直接相关。例如，词频可视化和分析是基本的文本分析技术，是许多新语言技术（如基于语言模型的聊天机器人）和新兴的stem相关职业（如数据新闻）的核心。文本分析技术也改变了数字人文主义者理解和分析语言的方式，他们使用这种分析来显示语言使用的模式和趋势。下一代劳动力需要准备好将语言和文本作为数据来工作。该项目的目标是开发和研究综合数据素养和文本分析教学在中学英语语言艺术中的潜力。这种整合可能会使没有参加相关STEM课程的学生更容易获得基本的数据素养实践，为帮助学生阅读理解提供新的资源，并为更多地参与STEM领域和专业创造新的途径。通过与英语语言艺术教师的共同设计、课堂研究和参与式专业发展，该项目将开发适合英语语言艺术中学课堂使用的文本分析工具、可视化和技术。由于使用这些新资源，学生对数据素养和文本素养理解的变化将通过临床访谈进行记录，并在项目的多年中进行分析。学生对STEM技能和数据密集型职业的认识和看法的变化也将在数年内进行调查和研究。除了为STEM技能发展创造新的途径外，该项目的一个关键假设是，在英语语言艺术中引入数据素养活动也可以使仍在提高英语流利程度的学生更容易理解文本。这些数据素养活动为识别文本和语言的重要特征提供了图形化和可视化资源。一个以西班牙裔/拉丁裔为主的公立学区，几乎三分之一的学生被归类为英语学习者，正在与大学研究团队合作，将参与为期一年的教师专业发展活动，并将在整个项目中提供反馈和改进的循环。估计有3000名公立中学学生将在他们的英语语言艺术课程中使用创新的数据素养整合。该项目由“面向学生和教师的创新技术体验”（ITEST）项目资助，该项目支持有助于提高学生对科学、技术、工程和数学（STEM）以及信息和通信技术（ICT）职业的知识和兴趣的实践、项目要素、背景和过程的理解。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。