2.5 Some key district leadership practices that enhance teaching and learning
2.5.5 Data-informed decision-making as a strategy for learning improvement
& Kennedy-Lewis, 2013; Marsh & Farrell, 2015). Lai and Schildkamp (2013, p. 10) define data as “information that is systematically collected and organised to represent some aspect of schools” (Lai & Schildkamp, 2013, p. 10). Levin et al. (2012) conceptualise DDDM as a key strategy for supporting teaching and learning improvement. According to Marsh, Kerr, Ikemoto et al. (2005, p. 1), DDDM refers to “teachers, principals, and administrators systematically collecting and analysing various types of data ... to guide a range of decisions to help improve the success of students and schools”. This includes qualitative as well as quantitative data that teachers and school leaders need for decision-making (Lai & Schildkamp, 2013; Wayman, Cho, Jimerson, & Spikes, 2012).
Evidence from research on improving school districts shows that data-informed decision- making emphasising data concerning student progress and outcomes is a crucial and effective district-level leadership strategy (Cawelti & Protheroe, 2007; Togneri & Anderson, 2003).
Datnow, Park, and Wohlstetter’s (2007) study of districts confirms the positive relationship between student achievement and the engagement of DOs in Data-Driven Decision-Making (DDDM). It also highlights the district leadership key role in establishing a culture and support system for performance-driven inquiry and decision-making at the school and local system levels. Louis et al. (2010) suggest that student assessment data should be available as a requirement for district accountability. This enhances DOs’ planning for learning for schools and learners to meet performance targets. Wohlstetter, Datnow, and Park (2008) found that district leadership practices that develop DDDM include:
1. Establishment of meaningful goals for improvement in learner performance aligned with system-wide curriculum and accountability requirements.
2. Create explicit norms and expectations for data use for decision-making.
3. Develop structures to enable the interchange of information between the district office and schools about performance and plans for improvement.
4. Invest in developing the capacity of schools and district personnel to use data.
Honig and Coburn (2008) analysed a district’s instructional decisions over three years and found that DOs were inclined to interpret problems in ways that were consistent with their
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beliefs about data use. Similarly, Honig et al. (2010) found that DOs sometimes used data to garner political support from different stakeholders. Their study revealed that the critical dimension of data-driven decision-making is the “use of evidence throughout the central office to support continual improvement of work practices and relationships with schools.” Data- based decision-making is not only about the need to use data, but leaders should also construct meanings from the data and act upon these. Data on its own does not change anything if it is not analysed and interpreted to inform learning improvements (Farley-Ripple, 2012). Daly (2012, p. 2) argues, “the ultimate success of data use for educational improvement may depend on how states and local education agencies build capacity.” As a result, districts need to provide the capacity and support to assist schools in using data to inform decision-making (Marsh et al., 2005). Also, it could also be undertaken by districts investment in management information systems and professional development to develop proficiency and capacity at the schools (Datnow et al., 2007).
Anderson, Leithwood, and Louis (2012) believe that when districts prioritise data and if they inform their leadership practices through data use, they have a positive impact on principals and teachers. This is also because district leaders set expectations and model data to use within their districts. Data use also influences decision-making that provides direct and relevant support to schools. Consequently, there is a positive effect on student achievement in schools (Anderson et al., 2012). Moreover, Chinsamy (2013) asserts that the use of learner performance data has proven to be relevant to school districts in supporting and monitoring learner performance improvement. However, he emphasises the importance of correct and up-to-date data to provide relevant support as well as planning under limited resources.
From the above discussion, DDDM at all education system levels is important, and literature suggests that data use should be a norm. As a result, the DOs need to promote a culture of use through structures and processes, which widely promote dialogue and learning through practices within the district and educational system (Datnow et al., 2007; Fullan, 2007). Hence, Hargreaves and Braun (2013) recommend that data-driven or evidence-informed improvement should enable educational leaders to monitor the progress of all learners and schools in real- time. Consequently, make timely interventions so that no child will indeed be left behind”
(Hargreaves & Braun, 2013, p. 4). It is also important for DOs to gather and analyse student
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engagement data to use as a tool for improving student involvement in their learning (Levin et al., 2012).
The literature agrees that leadership is a central component in DDDM that occurs within a school (Hamilton et al., 2009; Van der Berg et al., 2011). According to (Hamilton et al., 2009;
see also Wayman et al., 2012), the key leadership functions are providing a vision for data use and defining the purpose and expectations for its use. While principals set the tone for their schools, district heads set it for their districts and the more explicit the vision, the more precise the expectations are for the staff at the school or district levels. Consequently, the culture of accepting and expecting the use of data to inform practice and improve learner performance develops (Means, Padilla, & Gallagher, 2010). Furthermore, principals and district leaders should make time for collaboration and resources to foster data culture and model data use (Means et al., 2010).
Hamilton et al. (2009, p. 46) define data culture as:
A learning environment within a school or district that includes attitudes, values, goals, norms of behaviour, and practices, accompanied by an explicit vision for data use by leadership, that characterise a group's appreciation for the importance and power that data can bring to the decision-making process. It also includes the recognition that data collection is a necessary part of an educator's responsibilities and that the use of data to influence and inform practice is an essential tool that will be used frequently.
Developing a data culture should involve strengthening collaboration, developing a data team and providing timely access to data. Furthermore, continuous improvement must be emphasised (Datnow et al., 2013). What also emerges in this discussion is the significant element of the DOs’ effective use of data to inform practices that would support schools.
However, Levin et al. (2012) contend that while data is available in the districts and schools, the focus of district offices usually remains on ranking and grading schools while ignoring areas that might improve learning outcomes. This scholar suggests that district leaders should use data to assess how well they are progressing and to compare the performance of their schools with the set goals and targets. Levin et al. (2012) highlight the importance of using results-orientated strategies as an ongoing pursuit for improvement and accountability across
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