The importance of using data and evidence effectively in educational contexts has risen noticeably over the last decade. However, a lack of clarity regarding their definitions still exists. Research shows that data and evidence use can improve student learning and achievement (Carlson, Fosmire, Miller, & Nelson, 2011), and it is essential to define what ‘data’ are in educational settings. Developing a common language and understanding of what it means to be data literate is the first step in creating a culture of data and evidence-use within schools (Jimerson, 2014).

Clarifying the meaning of data and evidence in education

The terms data and evidence are often used synonymously within educational contexts. This may in part be due to ambiguity around the meaning of data, which has been mistakenly adopted by some educators as interchangeable with evidence (Schildkamp & Kuiper, 2010; Lai & Schildkamp, 2013).

A helpful distinction is offered by the Australian Educational Research Organisation (2022) who define data as “…information that is collected and analysed in order to produce findings and/or to inform decision-making.” This differs with evidence which can be defined as “…any type of information that supports an assertion, hypothesis or claim.” Through this comparison, data is transformed into information which can then be used as purposeful evidence.

In education, data have primarily been equated with statistical and numerical representations (Lai & Schildkamp, 2013) and are rarely used in the format in which they are originally presented. To be useful these data must be transformed into information and evidence requiring a degree of data literacy.

Data literacy is not a new concept. Educators have always observed, questioned, and assessed their students’ content knowledge and skills. Data literate educators know how to identify, collect, organise, analyse, summarise and prioritise both quantitative and qualitative data. They also know how to develop hypotheses, identify problems, interpret data, and determine, plan, implement, and monitor courses of action. Data literate educators draw on a range of data sources extending beyond the classroom and standardised assessments (Gummer and Mandinach, 2015). They make use of behavioural, perception, demographic, attendance and other data which they triangulate with achievement data to interpret student performance and enable appropriate adjustments to their teaching.

To be data literate, it is also important to be assessment literate. This means being able to write and select high-quality assessments; knowing how to integrate results obtained from varying forms of assessments to generate improvements in learning and, knowing how to use data – transformed into evidence – to communicate accurately and meaningfully about student learning progress.

There is an opportunity for schools to engage in conversations which articulate data literacy, and data and evidence use, to enhance teaching and learning at all levels within a school. Understanding how educators perceive and use data and evidence to inform teaching and learning can ultimately lead to the unveiling of strategies that enhance evidence informed practice.

This article relates to research being conducted at the University of Sydney in relation to teachers using data effectively. Visit the Research Opportunities for Independent Schools webpage for more information.


References

  • Australian Educational Research Organisation. (2022). Key Concepts Explained. Accessed on 24 February 2022 https://www.edresearch.edu.au/using-evidence/key-concepts-explained 
  • Carlson, J., Fosmire, M., Miller, C., & Nelson, M. S. (2011). Determining data information literacy needs: A study of students and research faculty. portal: Libraries and the Academy, 11(2), 629-657.
  • Gummer, E., & Mandinach, E. (2015). Building a Conceptual Framework for Data Literacy. Teachers College Record, 117(4), n4.
  • Jimerson, J. B. (2014). Thinking about data: Exploring the development of mental models for “data use” among teachers and school leaders. Studies in Educational Evaluation, 42, 5-14.
  • Lai, M. K., & Schildkamp, K. (2013). Data-based decision making: An overview. In Data-based decision making in education (pp. 9-21): Springer.
  • Schildkamp, K., & Kuiper, W. (2010). Data-informed curriculum reform: Which data, what purposes, and promoting and hindering factors. Teaching and Teacher Education, 26(3), 482-496.

New to AISNSW and want to receive AISNSW Education News updates? Click here to register, and select ‘AISNSW Education News’ on the Areas of Interest/Subscriptions page.