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: 1708362
• 负责人: Ryan Boettger
• 金额: $ 18.51万
• 依托单位: University of North Texas
• 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=1708362&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.

随着对具有科学，技术，工程和数学（STEM）丰富内容知识和扎实沟通技能的多元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性语言，许多学生在毕业时缺乏所需的书面沟通能力，该项目让学生参与数据驱动学习（Data Driven Learning，简称DATA）。 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。 该团队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可扩展的教学单元。 内容知识和学生的科学和技术写作技能的应用将由教师和同行审查的书面产品进行验证，并通过一项研究设计进行测试，比较四个不同的学生群体的书面产品：学生在技术和科学写作密集课程谁教写作使用电子材料;在技术和科学写作密集课程的学生谁教写作没有电子材料;学生在高级生理课程;和学生在高水平的生态课程。探索性研究旨在阐明数据驱动的教学和语料库语言分析的写作STEM专业人士（1）提高学科写作技能和（2）影响学生的参与和学习。准实验设计调查如何以及在何种程度上-教学材料，（非/非非教学材料），不同的课程背景，和不同的学生特点-影响科技写作的修辞和语言模式的发展。 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。 教学材料和工具，以评估学生写的方式符合STEM学科的做法的程度将在线提供给STEM教师教学写作密集课程和教师教学科学和技术写作服务课程。