BACKGROUND: We begin with a definition, synthesized from existing literature and refined based on expert input: Data literacy is the ability to collect, manage, evaluate, and apply data, in a critical manner. It is an essential ability required in the global knowledge‑based economy; the manipulation of data occurs in daily processes across all sectors and disciplines. An understanding of how decisions are informed by data, and how to collect, manage, evaluate, and apply this data in support of evidence‑based decision-­making, will benefit Canadian citizens, and will increasingly be required in knowledge economy jobs. Data literacy education is currently inconsistent across the public, private, and academic sectors, and data literacy training has not been approached systematically or formally at Canada’s post‑secondary institutions. There are also per-sector capability gaps, which makes it difficult to set realistic expectations of data-based skills.

CONSIDERATIONS: Developing the solid foundational knowledge of data literacy is integral to building discipline-­/domain-­specific knowledge and ensuring that citizens are able to use and apply these skills appropriately and diversely throughout their personal and professional lives. The best place to begin this initiative is the undergraduate curriculum in post-­secondary institutions, due in part to their overarching goal of producing globally competitive, critically thinking, well-­equipped graduates. Post-­secondary curricula already introduce students to new theories and practices, and to new forms of literacy such as information literacy and computational thinking. Twenty-­first century problems require twenty-­first century skills (Pentland, 2013); adding data literacy explicitly to undergraduate curricula will help ensure graduates will be better equipped to meet the data skills gap in Canada’s (and the global) workforce.

FINDINGS AND BEST PRACTICES: Data literacy education requires methods that engage and motivate students, as well as encourage task commitment. Best practices for teaching data literacy education include collaboration between educators, organizations, and institutions to ensure goals are being met by all stakeholders; diverse and creative teaching approaches and environment including the effective use of technology; successive/iterative learning with complementary skills integrated (e.g. project‑based learning); emphasizing mechanics in addition to concepts (i.e. practical, hands on learning); and increasing engagement with the content by using real world data. Courses built on this model will connect learning with contributing to society or personal interests, and encourages both in-­school and lifelong learning. We have also identified gaps in our collective understanding of data literacy education, which will require further research.

DATA LITERACY COMPETENCIES: We have synthesized a set of skills and abilities that together comprise various levels of data literacy, which we present in a data literacy competencies matrix, organized by the five core aspects of our data literacy definition (data, collection, management, evaluation, application). This matrix is intended to form the basis of ongoing conversations about standards for assessing and evaluating levels of data literacy, and to inform the creation of learning outcomes in data literacy education.

CONCLUSION: For the benefit of students, employers, and society, data literacy must be recognized as a necessary civic skill (Swan et al., 2009). This recognition should come from all levels of government, and from post-­secondary institutions. There needs to be agreement on what elements of data literacy are necessary in an undergraduate core curriculum, in order to provide a consistent foundational education for those entering an increasingly data‑dependent workforce.

Find the complete PDF report here: Strategies and Best Practices for Data Literacy Education.

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