Workshop: Integrating Data-Driven Learning into the Technical Writing Classroom

Researcher’s note (June 2026). With Laurence Anthony and Stefanie Wulff, I co-led this hands-on IPCC workshop to show writing instructors how data-driven learning (DDL) and corpus tools could replace one-size-fits-all instruction with teaching tailored to each discipline. The core idea was simple: let students explore real language data—using a corpus of student technical writing alongside Laurence’s freeware tool AntConc—and discover the patterns that actually characterize writing in their field rather than memorizing generic rules. It’s the same instinct behind my current work on AI in writing: ground writing decisions in evidence, and teach people to interrogate what the tools and data put in front of them.

Abstract

Technical writing service courses have become a mainstay across institutions of higher education. However, the heterogeneous student population that these courses attract leads to generic instruction that often contradicts how students are expected to communicate within their respective fields. This workshop aims to provide participants with a basic introduction to data-driven learning as well as how to use corpora and text processing tools to facilitate more tailored technical writing instruction.

Index Terms — AntConc, corpus linguistics, data-driven learning, technical writing

Introduction

Technical writing service courses have become an integral part of higher education institutions, providing valuable instruction for local and international students who may be unfamiliar with the traditions of specialized discipline writing. However, the heterogeneous student population that these courses attract leads to generic instruction that often contradicts how students are expected to communicate within their respective fields.

Empirical research reveals distinctive linguistic and rhetorical patterns across academic disciplines [1-3]. Unfortunately, the instructional materials available to teach technical writing often fail to introduce or explain these patterns limiting students’ ability to correctly adapt their writing to particular academic disciplines and professional settings [4, 5].

Workshop Description

In this workshop, we introduce data-driven learning (DDL) as a way to facilitate more tailored technical writing instruction.

I. Data-driven learning

DDL contrasts with traditional deductive, lecture-based methods by promoting students’ active engagement with the subject through technology. 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 [6, 7].

This workshop aims to provide participants with a basic introduction to DDL and show them how to cater instruction for a heterogeneous, interdisciplinary student demographic. We believe that data-driven learning can help students learn the writing patterns common in their discipline. Our proposed approaches also provide instructors with materials and tools to customize learning to their students’ needs.

II. Corpora

One way to implement DDL is by using a corpus, i.e., a large, principled collection of spoken and or written language in digital format, together with a corresponding text processing software tool.

We will show participants how they can access and effectively use a portion of TechCorp, a corpus of student technical writing developed by two of the presenters. TechCorp comprises over 5,000 writing assignments such as lab reports, theses, and procedures.

Moreover, each text in TechCorp is annotated for various features, including the students’ academic major, year of study (freshman, sophomore), whether the text is a draft or final version, and others, so users can select only those texts from the corpus that are of interest to them at any given time.

Workshop participants will engage with critical reviews and white papers written by undergraduate and graduates in biology, engineering, technical communication, and English.

III. Text processing tools

A variety of text processing software tools have been developed to aid researchers, practitioners, and students in exploring and analyzing corpus data.

In this workshop, we introduce one such tool called AntConc [8]. AntConc is a freeware, multi-platform, portable software tool developed by one of the presenters. It is specifically designed to facilitate the teaching of technical writing from a data-driven perspective and features a user-friendly interface.

IV. Workshop coverage

We will show participants how to load TechCorp files into AntConc and explore the linguistic and rhetorical patterns of these texts using various functions, including word and keyword frequency profiles, Key-Word-In-Context (KWIC) concordance lines, dispersion plots, phraseological clusters, and collocation patterns. We will also provide several examples of how these functions can be used in a DDL approach to help students discover and understand the main patterns of use that characterize writing in their specific discipline by discussing both (i) how to introduce DDL to students for self-guided learning and (ii) how to develop instruction materials informed by corpus explorations.

We recommend participants bring their laptop computer to the workshop as well as download the latest copy of AntConc (http://tinyurl.com/ltmzm8u). The presenters will provide the corpus files and metadata inventory that participants can use during the workshop as well as in their future classrooms.

References

[1] S. M. Conrad, “Investigating academic texts with corpus-based techniques: An example from biology,” Linguistics and Education, vol. 8, no. 3, pp. 299-326, 1996.

[2] F. L. Stoller, J. K. Jones, M. Costanza-Robinson, and M. Robinson, “Demystifying disciplinary writing: A case study in the writing of chemistry,” Across the Disciplines, vol. 2, 2005.

[3] D. Ding, “Object-centered—How engineering writing embodies objects: A study of four engineering documents,” Technical Communication, vol. 48, no. 3, pp. 297-308, 2001.

[4] R. K. Boettger and S. Wulff, “The naked truth about the technical communication,” Technical Communication Quarterly, vol. 23, no. 2, pp. 115-140, 2014.

[5] J. Wolfe, “How technical communication textbooks fail engineering students,” Technical Communication Quarterly, vol. 18, no. 4, pp. 351-375, 2009.

[6] D. Willis and J. Willis, Doing task-based teaching. Oxford: Oxford University Press, 2007.

[7] D. Hanson and T. Wolfskill, “Process workshops - a new model for instruction,” Journal of Chemical Education vol. 77, no. 1, pp. 120-130, 2000.

[8] L. Anthony, “Concordancing with AntConc: An introduction to tools and techniques in corpus linguistics,” JACET Newsletter, vol. 155, p. 2085, 2006.