Literacy Literacies

Back in the early 1990s, Jon Durant very usefully outlined out the three main types of scientific literacy. This is probably as good a place to start as any: Knowing some science – For example, having A-level biology, or simply knowing the laws of thermodynamics, the boiling point of water, what surface tension is, that the Earth goes around the Sun, etc. Knowing how science works – This is more a matter of knowing a little of the philosophy of science (e.g. ‘The Scientific Method’, a matter of studying the work of Popper, Lakatos or Bacon). Knowing how science really works – In many respects this agrees with the previous point – that the public need tools to be able to judge science, but does not agree that science works to a singular method. This approach is often inspired by the social studies of science and stresses that scientists are human. It covers the political and institutional arrangement of science, including topics like peer review (including all the problems with this), a recent history of policy and ethical debates and the way funding is structured.

Although several financial literacy definitions have been proposed, there is no universally accepted meaning. Following the proposed financial literacy conceptual framework depicted in Figure 1, financial literacy could be defined as measuring how well an individual can understand and use personal finance-related information. This definition is direct, does not contradict existing definitions within the literature and is consistent with other standardized literacy constructs.

Data literacy requires that students investigate authentic problems; use data as part of evidence-based thinking; use appropriate data, tools, and representations to support this thinking; develop and evaluate data-based inferences and explanations; and communicate solutions (e.g., Briggs, 2002; Cobb & Moore, 1997; Madison, 2002; Rubin, 2005; Scheaffer, 2001; Steen, 2001). As such, true data literacy is neither a single discipline nor a subdiscipline of mathematics. Whereas the phrase data literacy often connotes images of students analyzing large datasets (e.g., Ben-Zvi & Arcavi, 2001; McClain & Cobb, 2001), we have found another perspective on data literacy that can help students make sense of data encountered in both school and “real life.” ... Students investigate fairness by considering and creating arguments, as true data literacy requires that students be able to recognize faulty arguments based on data and create their own, valid, data-based arguments (Moore, 1985; Steen, 2001).

The field is now working on a common definition of data literacy (Mandinach & Gummer, 2012a). In this article, we broadly define data literacy as the ability to understand and use data effectively to inform decisions. It is composed of a specific skill set and knowledge base that enables educators to transform data into information and ultimately into actionable knowledge (Mandinach, Honey, Light, & Brunner, 2008). These skills include knowing how to identify, collect, organize, analyze, summarize, and prioritize data. They also include how to develop hypotheses, identify problems, interpret the data, and determine, plan, implement, and monitor courses of action.

Health literacy—the degree to which individuals can obtain, process, understand, and communicate about health-related information needed to make informed health decisions (Berkman, Davis, & McCormack, 2010)

The concept of mental health literacy was introduced by myself and some colleagues in 1997 and was defined as “knowledge and beliefs about mental disorders which aid their recognition, management or prevention” (Jorm, Korten, Jacomb, Christensen, et al., 1997). More recently, I have proposed that mental health literacy involves many components, including knowing how to prevent mental disorders, being able to recognize when a mental disorder is developing, knowing about help-seeking options and treatments available, knowing about self-help strategies, and mental health first aid skills to support others affected by mental health problems (Jorm, 2012).

terminology should be unified around the concept and term of data literacy. We can adopt the definition of Calzada Prado and Marzal (2013), according to whom data literacy enables individuals to access, interpret, critically assess, manage, handle and ethically use data. Managing, figuring in this definition comprises preservation and curation. We can supplement the above with the viewpoint of Mandinach and Gummer (2013, p. 30), who define data literacy as “the ability to understand and use data effectively to inform decisions”. They add that it is a specific skill set and knowledge base that enables us to transform data into information and ultimately into actionable knowledge.

Briefly, Quantitative Literacy is about reasoning in context and about making judgments based on real-world data and having real-world consequences.

Health literacy, the ability to access, process, and understand health information