Collaborative research: Evaluating a Data-Driven Approach to Teaching Technical Writing to STEM Majors

合作研究：评估 STEM 专业技术写作教学的数据驱动方法

• 批准号: 1708360
• 负责人: Stefanie Wulff
• 金额: $ 10.56万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1708360&HistoricalAwards=false
• 关键词: Collaborative; research; Evaluating; Data; Driven

With continuing demands for a diverse professional workforce with strong content knowledge in Science, Technology, Engineering, and Mathematics (STEM) and solid communication skills, the research project seeks to improve the quality of writing instruction for STEM students. Recognizing that English is a global language and that many students lack the required written communication competencies when they graduate, the project engages students in data driven learning (DDL). Through this teaching approach, students learn how to write in ways that conform to the writing practices of the STEM disciplines in which they earn degrees. The team will develop and test a set of DDL instructional units for writing-intensive service courses and STEM-focused content courses. Content knowledge and students' application of scientific and technical writing skills will be validated by faculty and peer review of written products and tested through a research design that compares the written products of four diverse student populations: students in technical and scientific writing intensive courses who are taught writing using DDL materials; students in technical and scientific writing intensive courses who are taught writing without DDL materials; students in upper-level physiology courses; and students in upper level ecology courses.The exploratory study aims to shed light on how data-driven instruction and corpus linguistic analyses of writing by STEM professionals (1) improve disciplinary writing skills and (2) affect student engagement and learning. The quasi-experimental design investigates how and to what degree -- instructional materials, (DDL/non-DDL instructional materials), different course contexts, and different student characteristics -- influence the development of the rhetorical and linguistic patterns of scientific and technical writing. The mixed effects models that are developed will allow the R&D team to predict student mastery of discipline-specific writing skills as evident in written products. Instructional materials and tools to assess the degree to which students write in ways that conform to the practices of STEM disciplines will be available online to STEM faculty teaching writing intensive courses and faculty teaching scientific and technical writing service courses.

随着对具有丰富的科学、技术、工程和数学(STEM)内容知识和扎实的沟通能力的多样化专业劳动力的持续需求，该研究项目旨在提高STEM学生的写作教学质量。认识到英语是一种全球语言，而且许多学生在毕业时缺乏必要的书面交流能力，该项目让学生参与数据驱动学习(DDL)。通过这种教学方法，学生学习如何以符合他们获得学位的STEM学科的写作实践的方式写作。该小组将为写作密集型服务课程和以STEM为重点的内容课程开发和测试一套DDL教学单元。内容知识和学生对科技写作技能的应用将通过教师和同行对书面产品的审查来验证，并通过一项研究设计进行测试，该设计比较了四个不同学生群体的书面产品：技术和科学写作密集型课程的学生使用DDL材料进行写作；技术和科学写作密集型课程的学生不使用DDL材料进行写作；高级生理学课程的学生和高级生态课程的学生。探索性研究旨在揭示STEM专业人员对写作的数据驱动教学和语料库语言分析如何(1)提高学科写作技能，(2)影响学生的参与和学习。准实验设计考察了教材、(DDL/非DDL教材)、不同的课程背景和不同的学生特征如何以及在多大程度上影响了科技写作的修辞和语言模式的发展。所开发的混合效应模型将使研发团队能够预测学生对特定学科写作技能的掌握程度，这在书面产品中是显而易见的。评估学生以符合STEM学科实践的方式写作的程度的教学材料和工具将在线提供给STEM教授写作强化课程的教师和教授科学和技术写作服务课程的教师。