In the New Digital Workforce, Data Literacy is Essential to Achieving Success
The pandemic put the concept of data literacy on full display. Critics questioned the validity of a measure that relied solely upon those who chose to be tested and then projected estimations of positivity rates for a specific region. City and state leaders established thresholds to determine where and when masking would be necessary and if schools could be open for in-person learning. With limited supply, provisions of personal protective equipment and distribution of respirators flexed in response to spikes in positivity rates to meet the greatest need.
But the effects of the pandemic were not just about illness. In response to its economically crippling effects, the federal government developed formulas to determine which small businesses were eligible for the Paycheck Protection Program. Families weighed the value of reduced employment hours (and thus take-home pay) against the cost of childcare. Issues of who did and who did not have access to vaccines and other healthcare by particularly marginalized populations were exposed.
One could argue that low levels of data literacy proficiency contributed to the COVID-19 becoming a pandemic, determined the global response, and drove targeted efforts at recovery. What became irrefutable is that the skill of data literacy is one of critical value.
The Evolution of Workforce Readiness
In 2000, the Secretary’s Commission on Achieving Necessary Skills (SCANS) released its report, “Skills and Tasks for Jobs.” In the report, definitions of foundational skills and competencies were established and demonstrated in hypothetical contexts to show their critical need among the workforce, including concepts like data literacy. No longer would “the three Rs” be enough for an educated and productive citizenry. Therefore, in addition to traditional academic fluency, the SCANS report argued for the development of “soft skills,” “employability skills,” “post-secondary skills,” or “durability skills” among learners. In this article, I will refer to these skills as “post-secondary skills.”
For more than twenty years since the SCANS report, education institutions have conducted research into skills as learning science, developed methods of instruction, and built rudimentary tools of assessment. But unlike the ability to identify the protagonist in a passage or the square root of a complex number, many of the post-secondary skills can be context-specific or even learner-specific, thus making a single measure of proficiency practically impossible to design. The examples of data literacy mentioned above show that no multiple-choice test could assess a learner’s full data literacy, and no single multiple-choice test can measure all the ways in which data literacy can be applied.
Gathering Evidence of Learner Competency
Today, the instruction, assessment, and research into post-secondary skill acquisition has taken a major leap forward through advancements in technology, specifically the comprehensive learner record (CLR; sometimes also referred to as a learning and employment record, or LER). Moving beyond the multiple-choice test or course grade, the CLR empowers institutions to accept learner evidence of competency that may occur within or beyond the classroom. Instead of an instructor only assessing competency based upon an instrument s/he designs and administers in the classroom, the CLR allows the instructor to shift the onus of demonstration onto the learner.
“Using data relevant to your work, your home, or another arena, show me your ability to review, interpret, critique, and advance data in order to posit solutions to a challenge you face in the selected context.”
With such an assignment, learners are free to use data relevant to their context, apply tools of analysis with which they are familiar, and display vehicles of presentation best representative of conclusions derived and aligned to the learner’s modality of communication. Little of this information fits into a grade book. All of it is part of the learner’s CLR.
Of course, an instructor can be more specific, asking learners to use a specific tool of data crunching, model of analysis, or theory of change; but the point is that by moving away from a single measure and empowering a learner in context, the resulting work provides much greater insight into the learner’s strengths, competencies, and capacities. No single number score or letter grade could begin to be this comprehensive.
With a more robust demonstration of competency portrayed in the CLR, employers gain deeper and faster insight into a candidate’s potential and can scrutinize the candidate’s approach to a problem as well as their ability to identify a solution. Such information can be difficult for a human resources department to mine if looking only at a transcript or resume – or via the questioning of a nervous interviewee. Depending upon the assignment’s construction, the evidence portrayed within the CLR may show the candidate’s ability to work in a team, perform under pressure, or enact problem-solving strategies – all post-secondary skills increasingly in demand by employers today.
Often paired with the CLR movement is the issuance of a learner’s digital wallet – a portfolio of competencies (and sometimes the evidence) acquired across institutions, training, internships, and apprenticeships. With Cloud storage, interoperability, and even blockchain security, a learner is now able to own his/her data forever, independent of the issuing institution. Akin to a verified resume or portfolio, the learner’s digital wallet becomes a fungible exchange with institutions of learning or employment. With a few clicks, a learner can determine what data to share and how, creating a candidate’s curated experience for review, consideration, or qualification.
Intellectual Currency for a New Era
Post-secondary skills continue to evolve the further we move as a society from learning as “information regurgitation” to “data processing.” Reciting states and capitals, filling in a blank periodic table, and memorizing the quadratic formula represented intellectual currency in a bygone era. With that kind of information now available practically anywhere and to anyone, the measure of intellectual currency shifts to what someone (or someones) can do with it to advance health, production, or safety in a wide array of contexts of conditions, positions of influence, and responsibilities of roles. CLRs and digital wallets bridge the gap between the bygone era and today’s more explicit demand for thinking, problem-solving, collaboration, and other post-secondary skills.
One could posit that the entirety of this new post-secondary skill era can be surmised as data literacy, making it the new answer to the existential question of “what is learning for?” And if that is the answer, then the social contract is to create systems that allow all learners – regardless of demographics, geography, or innate abilities – to have access to development and the ability to communicate their fluency, potential, and promise.