Improving the Data Information Literacies of Technical Communication Undergraduates

Researcher’s note (June 2026). Chris Lam, Laura Palmer, and I wrote this in 2017 because we kept noticing that our technical communication undergraduates were heading into industry jobs that demanded real data fluency, yet our curricula barely touched research training. The core idea was simple: teach students to manage data before they try to analyze it, and weave that literacy across a program rather than quarantining it in one course. That argument feels even more urgent in my current work on AI in writing and assessment—students now need to judge, curate, and interrogate machine-generated data and text, and the same “manage before you analyze” instinct still holds.

Abstract

The research training that technical communication undergraduates receive remains an under examined but never more timely topic of discussion. The skills a practicing technical communicator must possess is quickly expanding. In particular, technical communicators require data collection, curation, and analysis competences. In our paper, we offer three case histories that illustrate how to increase the data information literacies of technical communication undergraduates. Our observations were recorded in three classroom settings: a content analysis course, a SEO and website analytics course, and a UX app development and design course. We conclude with suggestions for improving data information literacy in technical communication undergraduates well as a call to action for further research on this topic.

Index Terms – Data information literacy, research, technical communication, undergraduate curriculum

Introduction

The research training that technical communication undergraduates receive remains an under examined but never more timely topic of discussion. Typically, our field’s discussion of research is directed toward current and future academics. In fact, only a handful of studies have explored research training at the undergraduate level.

As Spilka points out, most undergraduates will conduct some type of research on their first job and then throughout their career [1]. She defines practitioner research as—

any type of research conducted by technical communicators as part of either their routine or their specialized job responsibilities. The scope and purposes of practitioner research can be local and limited, confined to the solution of internal problems, or much broader, with its potential relevance extending beyond company borders and across diverse types of contexts, situations, and work in the field [p. 217].

Two trends have emerged from the few investigations into the research training of technical communication undergraduates: (i) most undergraduate programs contain little to no research training, and (ii) the training our undergraduates do receive does not generally reflect what industry requires [1-3].

The typical career goal of technical communication undergraduates is to work as writers and editors, usability specialists, or content strategists in any number of professional fields. These careers require awareness to data-driven methods like survey, usability testing, content analysis, and experiments. Conversely, undergraduates often have contradictory perceptions of how the research they conduct in an academic setting, which is often secondary research, differs from the research they will conduct in the workplace, which is often primary research.

Simultaneously, the evolving duties of twenty-first century technical communicators all require solid data information literacies. For example, Pflugfelder states that technical communicators’ relationships with computerized data management professionals have intensified alongside the pressures of communicating with industry experts who work with data warehousing methods and object-oriented and post-relational database systems [4]. The complexities related to analyzing and interpreting large data sets also utilizes one of a technical communicator’s greatest strengths—the ability to produce persuasive narratives from data [p. 19].

Previous research in fields such as library and information science have explored the data information literacy needs of students. Data information literacy encompass the ability to think critically about concepts and arguments as well as read, interpret, and evaluate data [5]. For example, Carlson et al. identified 12 core competencies that students working with data should develop [6]. These competences related to issues such as data management and organization; data sharing, preservation, and curation of data; and the analysis and visualization of data. However, we could not identify any literature that addressed the data information literacy needs of technical communication students who have future career aspirations in industry rather than academe.

Our paper addresses some of the data information literacies needed to prepare technical communicators to work in industry. We offer three case histories to illustrate how some of the recommended core competences can be integrated into our classrooms. We argue that data information training—or research training in general—cannot be contained to a single course and must be integrated throughout a program’s curricula.

Case History #1: Content Analysis

The first case history focuses on some of the data information literacies that students developed in a content analysis course. Content analysis (or CA) is the systematic, objective, quantitative analysis of message characteristics. It includes both human-based coding and computer-aided text analysis. Further, the term content encompasses any written messages as well as oral and visual messages [7].

CA is a popular method for academic research; however, there is a growing interest from commercial researchers and practitioners. Within technical communication, CA can be used to evaluate transcripts from usability studies to identify the common mental models of users, assess bias in publications produced by government agencies, or codify the design images used to market antidepressants to millennials.

To produce these results; however, students must learn how to format and manage data. Competences related to these tasks include data management and organization and the collection of metadata [6]. The following addresses how students developed these literacies.

I. Data management and organization

Before students begin any analysis, they must learn how to manage and organize data. My students worked with MS-Excel to gain experience in these tasks. Previous literature has questioned students’ proficiencies with technologies. For example, Rude observed that technical editing students are often “surprisingly unaware” of available tools in word processors [8, p. 62]. I discovered my students had less familiarity with MS-Excel than MS-Word.

MS-Excel includes several features relevant to content analysis. For example, filters allow users to narrow down data in a worksheet, allowing them to view only the information they need. This feature also makes it easier to identify and fix variable inconsistencies (female v. femail v. F), which some text analyses programs (and some Excel features) would count as three different variable levels. Additionally, PivotTables make organization more manageable by summarizing data and allowing users to manipulate it in different ways.

II. Metadata

A task related to data management is collecting metadata (or data about data). Advanced content analysts annotate their content with metadata to document their own research process. However, metadata can also make the results of content analysis richer and more generalizable. Consider the potential value of the metadata depicted in Figure 1. As shown in Column A, each text is assigned a unique file number (e.g., 420, 421, 422…). Ideally, users would have a separate file with a corresponding file name that includes the actual text; shorter text like Tweets or Facebook posts could be pasted directly into the worksheet. The remaining columns identify the text type (white papers) as well as each writer’s, academic major, level, gender, age, native language, and ethnicity. With a large enough sample, content analysts could separate and then analyze their content on any combination of metadata. For example, research supports that females use more formal language registers than males. The collection and management of this particular metadata then would allow users to quickly filter and then explore language variation among males and females.

[Figure 1: Example metadata worksheet in MS-Excel — image to be added.]

Case History #2: SEO and Website Analytics

The second case history focuses on a course on SEO (search engine optimization) and website analytics. In this course, students took their own website—built in an introductory pre-requisite course—and leveraged the site for SEO through a variety of strategies including metadata; they also added analytics (Google Analytics) and learned to perform data analysis with respect to their website traffic and user behaviors on the site. The course introduced students to iterative nature of website design and how SEO implementations could be measured and improved through analytics.

Students came to the class with a basic knowledge of site architecture, HTML 5, and CSS. Additionally, they considered themselves to be good Googlers—that is, they saw themselves as able to locate information well through search engines. However, they also arrived in the class with little exposure to the concepts of how search engines work (algorithms) and how best practices in HTML 5 functioned as metadata for search engines. As well, while some may have deployed analytics on their own sites, few to none had any idea what the data means, especially in relation to SEO.

I. Metadata

In HTML, there are basic meta tags for pages. These tags include “Description”, “Keywords”, “Author” and others. While not public facing elements, these meta tags are machine readable; in other words, the search engine’s algorithms will use this information to index pages. However, other practices within HTML also act as metadata. Page title tags, Alt-text descriptions, file names, and URLs also function to describe the contents of pages. As a result, the course included learning more detailed HTML work and how to re-work existing sites to properly leverage them to appeal to search engine algorithms.

II. Data analysis

Once their sites were being indexed by Google and data was appearing in their analytics, students needed to interpret what the analytics data meant for improving search engine acquisition. With Google Search Console activated, students were required to develop an in-depth understanding of how the data from intersection of site performance and user behaviors would inform changes to their site for the purposes of SEO. Measuring time on site/pages and bounce rates were some of the metrics used to determine if visitors to the site were engaged with its content.

Case History #3: UX App Design and Development

The third case history focuses on a project-based course in app development. Students in this are technical communication majors with no prior knowledge in programming, coding, or app development. They were introduced to the user experience research and design process and asked to design, develop, and test app ideas. Students were exposed to data information literacy via knowledge of databases and data formats and data conversion and interoperability [9].

I. Knowledge of databases and data formats

Part of the UX course was building out the back-end components of their web applications using the Ruby on Rails framework. Ruby on Rails uses an SQL database, which students have no prior background in. Therefore, students are challenged to learn first about data types (string, text, boolean, binary, integer, etc.). More importantly, however, students critically consider user needs while they design back-end databases. For instance, students consider how datatypes like “DATE” and “DATETIME” differ and how they impact the overall user experience when displaying or querying data. In addition, students learn—often the hard way—that mistakenly assigning a datatype in an SQL database greatly limited the user experience as they sorted or filtered data. Therefore, even in the backend work of database design, students are challenged to consider user needs, interactions, and experiences.

II. Data conversion and interoperability

Second, this course introduced students to the literacy of data conversion and interoperability, which is defined as the proficiency in “migrating data from one format to another” and understanding the “potential loss or corruption of the information caused by changing data formats.” [9, p. 24]. Students encountered this literacy often throughout the course but most notably in their manipulation of image file formats. For instance, students learn the differences between png and jpeg file formats and how they are rendered in the browser. They have to consider factors like file size and load time as they convert image files from one format to another.

Discussion

These three case histories offer a brief overview of the ways that data information literacies can be integrated into undergraduate technical communication classrooms.

While the literature supports that our students will complete practitioner research in their place of work, technical communication programs have made few strides in updating their curricula. Reinsch suggested that our researchers often “produce flawed research because [they] don’t know how to use the most appropriate research methods…and have not continued to master additional and new research techniques” [3, 201].

We also argue that the research approaches that technical communication academics apply do not reflect the training that our students need, especially those students transitioning into industry. Previous studies support that technical communication academics apply a variety of methodological approaches, including case study, ethnographic, and survey [9, 10]. However, these findings provide an incomplete view of our research landscape. Carliner et al. found that almost 50% of our published research is non empirical or anecdotal [11]. The empirical research we academics do publish is primarily qualitative and used for discourse analysis and historical research [9]. In other words, academics are collecting a lot of data, but we are not showing evidence of strong data analysis and interpretation.

In the final section, we offer suggestions for improving training of technical communication undergraduates to prepare them for the evolving needs of industry.

Conclusions

Based on our personal experiences and scholarship, we maintain that data information training cannot be contained to a single course and must be integrated throughout a program’s curricula. Below we offer suggestions for improving data information literacy in technical communication undergraduates:

I. Identify your students’ data information literacy needs.

Earlier in the paper, we referenced 12 core competencies related to working with data [6]. Similarly, the case histories illustrated a handful of these literacies.

Faculty and program administrators must determine what their technical communication students need to know about data management and analysis. During this process, stakeholders should determine the job placement of their former undergraduates as well as identify companies these stakeholders would like to ideally place future graduates. Interviewing former students as well as an analyzing data information requirements in current job postings would suggest where data information literacy could be integrated into an existing curriculum.

II. Teach students how to manage data before teaching them how to analyze data.

Many current technical communication job postings require students be familiar with software such as MadCap Flare, RoboHelp, or Drupal. This naturally primes upcoming graduates to panic because they don’t know enough “tools.” Based on our experiences, students cannot competently learn these tools until they understand how they are used to collect, manage, and repurpose data. Job-oriented students are naturally focused on the final product rather than the process used to construct that product. Therefore, we argue that students must first learn how to manage data.

First, students must become more aware of how data influences their role as a technical communicator. For example, microcontent (e.g., web page titles, page headings, taglines, email subject lines, tweets, and search-engine results) is essential to enhancing a user’s experience with a product. The second case history demonstrated how students could improve the search engine rank of their web site based on revisions to its metadata and analytics.

Next, students must learn how to collect and manage data. This is an important, but arguably boring (from the student’s perspective), step in developing data information literacy. The first case study described a basic metadata spreadsheet that can be used to drill down large datasets into more manageable, interesting chunks. For example, correlating basic metadata of participants with their usability test transcripts might reveal interesting findings – e.g., only 3 out of 7 participants preferred the background color of a web site, but those participants also happened to be the client’s ideal customer demographic.

III. Continually assess your data information literacy learning outcomes

Just as instructors create learning outcomes for their individual courses, programs should also create outcomes of what specific data information learning is expected at the end of each course, academic year, and before graduation. Good outcomes are specific, measurable, clear, and student centered rather than instructor centered.

We recommend that these outcomes be sequenced within existing degree plans and gradually increase in complexity as students begin junior- and senior-level coursework. For example, applications like MS-Excel can introduce students to basic project management and data visualization skills. In writing this paper, we could not identify any research on data information skills or tools students currently use in their technical communication classrooms; however, we did identify research that showed that entering business students scored a mean of 59.5% (out of 100%) on their Excel skills pretest [12].

Additionally, global data information tasks skills should be taught in the service courses while more advanced skills should be taught in the major-specific courses. For example, using Excel to visualize technical information could be taught in an introductory or intermediate technical writing courses that’s enrolled by non-majors, but SEO and analytics skills training should be integrated into advanced courses for technical communication majors.

In conclusion, we hope these discussions and our own experiences with teaching data information literacy function as a call to action for more research in this area—both within the specific data information literacies that industry is demanding from our undergraduates as well as how technical communication programs are adapting their curricula in response.

References

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