Publications

[5] Gender effects in student technical writing—a corpus-based study
RK Boettger, S Wulff—IEEE Transactions on Professional Communication, 2019

[4] Teaching STEM undergraduates discipline-specific writing skills: A data-driven learning approach  | ASEE Poster (pdf)
RK Boettger, S Wulff—American Society for Engineering Education Annual Conference and Exposition, Tampa, FL, 2019

[3] Using authentic language data to teach discipline-specific writing patterns to STEM students
RK Boettger, S Wulff—Proceedings from the IEEE ProComm Conference, Austin, TX, 2016

[2] Using corpus-based instruction to explore writing variations across the disciplines: A case history in a graduate-level technical editing course
RK Boettger—Across the Disciplines, 2016

[1] The naked truth about the naked this: Investigating grammatical prescriptivism in technical communication
RK Boettger, S Wulff—Technical Communication Quarterly, 2014

Presentations and Workshops

[6] Passive voice in student technical writing: gender or genre?
S Wulff, RK Boettger—The 5th Annual Conference on Writing Analytics, St. Petersburg, FL, 2018

[5] The role of data-driven learning in STEM education courses
S Wulff, RK Boettger—STEMPowered Symposium, Gainesville, FL, 2018

[4] Quantitative content analysis [Workshop]
RK Boettger—Association of Teachers of Technical Writing Research Methods Workshop, Portland, OR, 2017

[3] The Technical Writing Project: Bringing corpus data into the technical writing classroom
S Wulff, RK Boettger—The 4th International Conference on Writing Analytics: Writing Analytics, Data Mining, and Student Success, St. Petersburg, FL, 2017

[2] Integrating data-driven learning into the technical writing classroom [Workshop]
RK Boettger, S Wulff, L Anthony—IEEE ProComm Conference, Austin, TX, 2016

[1] Introducing the Technical Writing Project, a corpus of student technical writing
S Wulff, RK Boettger—American Association of Corpus Linguistics Conference, Ames IA, 2016

In-Progress Reserach (updated March 2022)

Data collected from 374 students are currently being analyzed along various dimensions.

One analysis will focus on students’ uptake and change of use of the illustrative language examples presented in the instructional units and accompanying materials. This will be done by creating concordances and frequency counts of all attestations of the language examples in the student data (using the statistical and programming environment R). Uptake and use will be tracked as a function of time in the semester, instructor, the students’ major, and other available meta-data. In a second analysis, the data will be annotated for parts of speech and syntactic information (using the corresponding spaCy natural language processing tools). This will facilitate a comprehensive analysis of students’ uptake and change of use of the grammar topics covered in the instructional units above and beyond the specific language examples provided in the instructional units.

In addition, students’ responses to the satisfaction survey will be analyzed, and correlated with the uptake and use results obtained in the above analyses.