A growing emphasis on the use of evidence as a basis for school improvement processes such as school self-evaluation (SSE), had led to an increasing demand for teachers to become data literate. This study explores how teachers might learn how to use data and in particular, models of continuing professional development (CPD) that support data-use. Based on a review of literature, a data-use CPD intervention was developed and implemented in five Irish post-primary schools. The intervention tested, involved a university based expert in SSE (critical facilitator) working directly with SSE teams in each of the five schools to complete an SSE cycle within an academic year. The findings outline aspects of the intervention which supported teachers’ professional development in data-use as well as its limitations. 

The findings presented here focus on the data-use CPD aspects of the intervention. The main research questions investigated and reported on in this paper are as follows: 1. What aspects of the model supported teachers’ professional development in data-use? 2. What were the limitations of this model of CPD?

According to Schildkamp, Poortman & Handelzalts (2016, p. 228) professional development in data-use is “urgently needed for improving the quality of schools” and this is reflected in education policy across many countries (Mandinach & Gummer, 2016). An ability to use data can, it is suggested by some scholars, empower teachers, helping them to analyse and find solutions to classroom-based problems (Ikemoto & March, 2007; Mandinach & Jimerson, 2016; March, 2012) and in doing so enhance a teacher’s sense of professionalism and self-determination. 

Wayman, Jimerson, & Cho (2012) explored professional learning for data-use as one of three approaches to more effective data-use for educational improvement, claiming that it “serves as a catalyst for lasting changes in practice” (p. 165). They further claim that teacher capacity to use data will increase through participation in frequent learning opportunities, coherently linked to practice, which allow them to usefully try out new skills and knowledge. Accordingly, they propose professional learning activities that are: 1) job-embedded, 2) collaborative and 3) small. Wayman et al describe job-embedded approaches as those which are “compatible with daily activity” and which can be tried out in situ, (workday embedded) shortening the time between learning a new skill, trying it out and making judgements regarding its effectiveness. It also includes learning which is embedded in tasks for which an educator is responsible (content embedded) and provides opportunities for educators to learn with and from each other (relationship embedded). In terms of collaborative approaches to professional learning Wayman et al suggest systemic provision of time and direction to educators in order “to engage in quality collaboration around data” (p.168). Wayman et al emphasise the importance of “small” structures which involved a limited number of people for a brief amount of time. capitalising on the social nature of learning but also warning that such approaches need to be “focused and cumulative” with “a clear direction for learning about data-use” (p. 169).  

Mandinach & Gummer (2016) organise data-use for teaching into five components made up of 53 specific skills. The components include: identify problems and frame questions, use data, transform data into information, transform information into a decision, and evaluate outcomes. Their conceptual framework for Data Learning for Teachers provides a useful structure for designing a CPD intervention for teachers with the 53 skills acting as learning outcomes. 

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