Just last week I hosted a #UkEdResChat on how and when to go about supporting teachers develop research literacy.  However, the more I thought about the issue the more it seemed to me that maybe we were focussing on the wrong type of ‘literacy’ and that instead we should be discussing and focussing on developing teacher ‘data-literacy’.  For it seems to me that If we want teachers to improve their day to day practice by engaging in some form of professional inquiry then having the skills to effectively interpret data is essential. and far more relevant to classroom teachers than research literacy. So to help develop my thinking on what data-literacy could and should look like, I did a quick search on Google Scholar using the term teacher data literacy and came across Mandinach and Gummer (2016) and their conceptual framework on teacher data literacy (DLTF), and which seems to be worth sharing.  Accordingly, this post will discuss Mandinach and Gummer’s definition of data literacy and associated conceptual framework; the dispositions and habits of mind that influence data use; and a discussion of the implications for school leaders

Data literacy for teaching (DLFT)

Using data is a key component of the teachers’ role.  In England, the 2011 Teacher Standards – updated in 2013 – state that teacher should use ‘relevant data to monitor progress, set targets, and plan subsequent lessons.’  However, as Mandinach and Gummer point out – this type of statement is extremely general and does not provide any guidance about the knowledge and skills necessary to be data literate.  In order to address this issue Madinach and Gummer offer up this definition of data literacy for teaching.

Data literacy for teaching is the ability to transform information into actionable instructional (teaching) knowledge and practices by collecting, analysing, and interpreting all types of data (assessment, school climate, behavioural, snapshot, longitudinal, moment to moments etc) to help determine instructional steps.  It combines an understanding of data with standards, disciplinary knowledge and practices, curricular knowledge, pedagogical content knowledge and an understanding of how children learn (p2)

 Mandinach and Gummer then go onto state three propositions on which their work is grounded.  

1.    There is no question that educators must be armed with data?

2.    Assessment literacy is not the same as data literacy.

3.    Simply relying on professional development to develop teacher data literacy is not enough and a conceptual framework is required.

The Data Literacy For Teachers Framework

The DLTF framework was a product of number activities – including the convening of experts, a review of published materials and resources dealing with data-literacy; a review of relevant teaching standards – which were then synthesised by Mandinach and Gummer with what is viewed as good teaching – and resulted in five domains for data literacy - identify problems and frame questions; use data; transform data into information; transform information into a decision; and, evaluate outcomes.  The associated knowledge and skills associated in with each domain are summarised in Table 1 – more detail can be found in Mandinach and Gummer’s article

Dispositions, habits of mind, or factors that influence data

Mandinach and Gummer the go onto identify another category of components which they viewed as necessary for data uses, which they called dispositions or habits of mind and which are linked to general teaching rather than be specific towards data use.  Nevertheless, it is argued that the six dispositions identified influence how teachers approach the use of data.  The six dispositions are:

·     Belief that all students can learn

·     Belief in data/think critically

·     Belief that improvement in education requires a continuous inquiry cycle

·     Ethical uses of data, including the protection of privacy and confidentiality of data

·     Collaborations

·     Communication skills with multiple audience

Implications for school leaders, CPD co-ordinators and school research leads

First, it’s important to remember that this conceptual framework needs to be ‘tested’ in the field and should not be seen as the definite statement of what data literacy looks like. As Mandinach and Gummer acknowledge more research is required into: how data literacy may change over time (just think social media and GDPR); how best to meet the data literacy needs of teachers at different stages of their career; the possible continuum of data literacy ranging from novice to expert what data literacy looks like for teachers at different stages of their careers and what implications this has for support made available to teacher; how data literacy may differ for a newly qualified teacher, compared to say a senior leader in a multi-academy trust?  In other words, this conceptual framework is not the finished article and is a work in progress, and should not be ‘oversold’ to colleagues

Second, what are the implications of the DLTF for schools wising to  incorporate ‘disciplined inquiry’ either as part performance management or continuous professional development?(see https://www.garyrjones.com/blog/2019/3/23/performance-management-and-disciplined-inquiry).  Initial reflection would suggest that if you accept that teachers in a school will have varied levels of data literacy – then it’s unlikely that a single model of disciplined inquiry or type of inquiry question is likely to appropriately meet the development needs of all teachers.  Indeed, before embarking on ‘disciplined inquiry’ it may be wise to undertake some form skills audit – so that you start off where colleagues are rather than where you would them to be.  This can also help you put in the place different packages of support relevant for teachers at different stages of the development of their research literacy.

Third, although there appears to tide appears to be turning against the importance of internal school data , especially in the inspection of schools (https://www.tes.com/news/ofsted-inspections-wont-examine-internal-school-data) that does not mean this framework is any less relevant.  Indeed, what this framework does is emphasise that data literacy is not just about – assessment or progress data– but is far more comprehensive. 

Finally, should attention switch from developing teacher research literacy to the development of data literacy?  Well given the lack of support research evidence which compares and contrast data or research literacy in action in schools, the answer to that question has to be no.  Instead, the two skill sets should be seen as overlapping and complimentary.  For example, for teachers seeking to identify problems and frame questions are more likely to be able to do this if they are research literate and can use research to better understand an issue.  On the other hand, a research literate teacher who subsequently makes a ‘research informed’ change in practice will need to have the capability to evaluate the outcomes of that change in practice

And finally

If you are interested in becoming more data literate, then I recommend that you have a look at Richard Selfridge’s book - Databusting for Schools: How to use and interpret education data. 

Reference

DfE (2013) Teachers’ Standards: Guidance for school leaders, school staff and governing bodies, July 2011 (introduction updated June 2013). London: DfE

Mandinach, E. B., & Gummer, E. S., What does it mean for teachers to be data literate: Laying out the skills, knowledge, and dispositions, Teaching and Teacher Education (2016), http://dx.doi.org/10.1016/j.tate.2016.07.011

Selfridge, R (2018) Databusting for Schools. How to use and interpret education data. London, Sage Publishing