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

Ryan K. Boettger; Stefanie Wulff

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Conference: 2019 ASEE Annual Conference & Exposition
Location: 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
Page Count: 6
DOI: 10.18260/1-2--32284
Permanent URL: https://peer.asee.org/32284
Download Count: 686

Paper Authors

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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 ryan.boettger@unt.edu.

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Stefanie Wulff

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Abstract

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.

Citation
Format

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

TY  - CPAPER
AB  - 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&#39; active engagement through computer-assisted tools and a databank of authentic writing. Instruction is typically designed to (1) foster students&#39; 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 &amp; 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.
AU  - Ryan K. Boettger
AU  - Stefanie Wulff
CY  - Tampa, Florida
DA  - 2019/06/15
PB  - ASEE Conferences
TI  - Board 17: Teaching STEM Undergraduates Discipline-specific Writing Skills: A Data-driven Learning Approach
UR  - https://peer.asee.org/32284
DO  - 10.18260/1-2--32284
ER  -