Teaching students how to work with and understand the limits of data

We live in a world of data. Data informs decision-making and professional practices. Individuals and organizations must grapple with data and information of varying quality and relevance. And regardless of their degree’s disciplinary focus, our students will graduate to work in a world increasingly reliant on the use of data.

Data takes many forms. While it may be tempting to think of data skills as being the domain of the STEM disciplines, programs across multiple disciplines have long taught students to work with data. Social science disciplines regularly teach students to analyze quantitative and qualitative data to answer societal questions. Humanities disciplines often teach students how to bring technology and big data into humanities scholarship and to reflect on the use of data and its implications. In the words of digital humanities scholar Ryan Cordell, “We need students and colleagues who are adept and thoughtful about the tools, platforms, and media of our day.” This need crosses all disciplinary boundaries.

It is imperative that students learn how to use data responsibly and thoughtfully. Data scientist Andrea Jones-Rooy writes, “After millennia of relying on anecdotes, instincts, and old wives’ tales as evidence of our opinions, most of us today demand that people use data to support their arguments and ideas. …. But in the frenzy, we’ve conflated data with truth. And this has dangerous implications for our ability to understand, explain, and improve the things we care about.” Universities have an important role to play in educating students to assess the relationship between data and truth, and in so doing, to use data appropriately.

As I discussed in my February column, instructors are increasingly pressed to evolve their teaching to meet contemporary technological challenges and opportunities. In my March column I discussed the importance of human skills in the face of technological advances and focused on opportunities for instructors to build intercultural skill development into their teaching. In this month’s column, I move to consider the instruction of data skills in higher education.

What is data literacy and where does it fit in university programs?

Data literacy has been described as an individual’s ability to “use data productively and to think about it in a critically reflective way” (Sternkopf and Mueller 2018), “derive meaningful information from data” (Statistics Canada 2019), “explore, understand, and communicate with data” (Tableau 2021), and “understand, share common knowledge of and have meaningful conversations about data” (Gartner 2021), among other definitions.

Programs will differ in how they incorporate data literacy. Some will have elements of data literacy woven throughout the program, with students developing data literacy skills across multiple courses from the first year of their program until graduation. Other programs may be less engaged with data literacy, limiting their explicit instruction to research methods classes.

My suggestion is that all programs have the opportunity to advance students’ data literacy skills. Doing so benefits graduates across a range of degrees. As a Royal Bank of Canada report described in its assessment of the Canadian labour market: “Being able to draw inferences, make unexpected connections and identify overarching trends is a competitive edge. In the 21st century … you can’t get anywhere without analytics.” For some programs, data literacy training may take the form of analysis skills. In others, it may take the form of being critical consumers of data. As Joseph Aoun writes, “[t]he purpose of data literacy … is to give us the tools to read the digital record and also to understand when we ought to look elsewhere” (emphasis added).

How can instructors help students advance data literacy?

As instructors, we can use explicit instruction to help our students develop their abilities to use data in all courses and not just courses focused on research methods and/or data analytics. Here are some ideas to prompt your thinking:

• Select a data literacy skill that makes sense for your course. A useful resource for thinking this through is Sternkopf and Mueller’s “data literacy maturity grid” (see Table 3 in their paper). This model has numerous elements of data literacy that can translate easily into university teachings, such as data ethics and security, asking questions, finding data sources, verifying data, cleaning data, analysis, visualization, communication, and assessment and interpretation.
• Add a data literacy skill to your course-level learning outcomes. Some examples: identify possible sources of bias in data; construct a community profile using Statistics Canada data; collect and assess digital data; critically assess biases embedded in algorithms and explain their implications; use [software name] to conduct basic analysis.
• Include instruction on a data literacy skill in your class time. This can be an opportunity to draw upon other campus resources for guest speakers, such as the university library, the ethics office, the career centre, or colleagues in your unit or other units.
• Build a data literacy skill into your course assignments and explicitly make the connection between the assignment and the learning outcome for students in the syllabus. Here is one example: “The purpose of this assignment is to provide you with the opportunity to strengthen your data literacy skills by identifying appropriate data sources (course learning outcome 4) while demonstrating your understanding of the factors contributing to climate change (course learning outcome 2).”
• Provide a clear articulation of data literacy skill levels in assignment rubrics. Sternkopf and Mueller’s data literacy maturity grid presents data literacy skills on a four-point continuum that can be helpful for your thinking. For example, in their grid, “ask question/define” skill levels range from “lacking ability to formulate questions to find meaningful answers in data,” to “high awareness of what questions can be answered by data.”
• Discuss with your colleagues opportunities to scaffold data literacy skill development over the full program. Your university teaching and learning centre can be a vital resource in helping your program identify how it can advance skill development from the first year to the final year of the program.

Continuing the Skills Agenda conversation

Do you teach data literacy in your own courses? If so, I would love to hear how you do so in the comments below. I also welcome the opportunity to speak with your university about skills training. Please connect with me at lo************@us***.ca, subject line “The Skills Agenda.” And for additional teaching, writing, and time management discussion, please check out my Substack blog, Academia Made Easier.

I look forward to hearing from you. Until next time, stay well, my colleagues.