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Board 17: Teaching STEM Undergraduates Discipline-specific Writing Skills: A Data-driven Learning Approach

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2019 ASEE Annual Conference & Exposition


Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

NSF Grantees Poster Session

Tagged Topic

NSF Grantees Poster Session

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Paper Authors


Ryan K. Boettger University of North Texas

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Ryan K. Boettger is an associate professor and assistant chair in the Department of Technical Communication at the University of North Texas. His research areas include data-driven learning, content analysis, and technical editing. His research in STEM education is currently funded by the National Science Foundation. He can be contacted at

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Stefanie Wulff

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Our research funded by the NSF Division of Undergraduate Education seeks to improve the quality of technical writing instruction for undergraduate STEM students. Specifically, our project proposes a data-driven learning approach for teaching STEM students writing patterns specific to their respective disciplines. Data-driven learning (DDL) contrasts with traditional deductive, lecture-based methods and promotes students' active engagement through computer-assisted tools and a databank of authentic writing. Instruction is typically designed to (1) foster students' active engagement, (2) encourage inductive activities that allow students to explore a topic on their own terms, (3) promote interaction between students, and (4) provide students with output-focused activities to apply this new knowledge (Chujo, Anthony, Oghigian & Yokota, 2013).

In our poster, we will provide an introduction to the basic tenets of DDL and present our current work on developing and testing a set of instructional writing units for writing-focused courses in technical communication that typically enroll more than 60% of STEM majors, as well as STEM-focused content courses in ecology and computer science and engineering. Testing is currently ongoing and will continue through 2020, so we will have a substantial amount of data to report on by the conference date. Our quasi-experimental 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. We will assess these two outcomes through qualitative and quantitative analysis of all written student assignments prior to, during, and after completion of the DDL units to evaluate uptake and writing development; user statistics gathered through the online learning management system in use, Canvas, to gauge level of engagement with materials; and a post-completion students survey to solicit students’ perception of the usefulness of DDL.

We hope to solicit discussion and feedback from the ASEE attendees on how to continue our efforts on several fronts, including what content areas to focus on as we develop additional instructional units, and what kinds of training resources instructors would like to have access to as they consider implementing DDL in their own STEM-focused or disciplinary writing courses.

Boettger, R. K., & Wulff, S. (2019, June), Board 17: Teaching STEM Undergraduates Discipline-specific Writing Skills: A Data-driven Learning Approach Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32284

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