Data Literacy

"Data, whether quantitative ( measurements, counts, and so on) or qualitative (color, movement, description of sound or smell, and so on), lies at the very heart of how scientists make sense of the world and construct and report patterns in it. Thus an important part of science literacy is data literacy. Being able to collect and use your own data to examine the world for patterns is an important skill for anyone to have, even if they're not a scientist."  (Bowen, M., & Bartley, A.)

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" (Ridsdale, C.)

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." (Ridsdale, C.) 

 DATA LITERACY COMPETENCIES

1. "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). We identified several best practices for teaching data literacy in the literature, some of which differ from "traditional" strategies but would be consistent with teaching practices already in use." (Ridsdale, C.)

2. "Hands-on learning in workshops and labs provides students with the necessary practical experience needed to fully understand a technical skill; students need the chance to figure out processes and methods on their own and make mistakes to readjust their own understanding." (Ridsdale, C.) 

3. "Module-based learning allows students to achieve learning outcomes in stages, in a systematic way. Successive, or iterative, learning allows students to build upon previously learned skills, encouraging process over memorization or following rigid instructions, and ultimately making learning an unfamiliar concept more manageable. (Ridsdale, C.)

4. "Project-based learning is a helpful way to implement the successive learning approach. Projects that include a wide range of investigation and have real-world applicability will solidify the connection between process/theory and practice.  Projects should include real-world data, relevant to the students' interests and in an engaging context, not just data for the sake of data. Increased engagement in working with data can foster innovation, improve learning, and increase the likelihood of lifelong learning. Projects should offer students the opportunity to go further than you expect."(Ridsdale, C.) 

Video Links

The age of data literacy | Uldis Leiterts | TEDxRiga

What is Data Literacy? Data-Pop Alliance

Quantitative vs. Qualitative Research/ UniversityNow

Lesson Plan Example

Data and Graphs/ Math Goodies

Interpret Data into a Graph/Internet4Classrooms

Web Site Source

Bowen, M., & Bartley, A. (2014). The basics of data literacy: helping your students (and you!) make sense of data. Arlington, VA: NSTA Press, National Science Teachers Association.

Ridsdale, C., Rothwell, J., Smit, M., Ali-Hassan, H., Blieme, M., Irvine, D., . . . Wuetherick, B. (n.d.). Strategies and Best Practices for Data Literacy Education Knowledge Synthesis Report. Dalhousie University. doi:http://www.mikesmit.com/wp-content/papercite-data/pdf/data_literacy.pdf