How Does A School Leader Determine What Data To Use When Making A Decision?

Introduction

Many issues that schools face do not have obvious solutions. Nevertheless, while acting rapidly on an issue or a trouble may feel efficient, interim without information is frequently non constructive. For example, a school may invest in expensive new curriculum materials to try to improve pupil achievement in a sure discipline expanse. Even so, if the crusade or causes of depression student accomplishment lie elsewhere (e.g. lack of targeted support for particular students), the trouble remains unresolved or may even be exacerbated. These types of actions toll time and coin without resulting in improved student performance. Therefore, information technology is of import to apply information to determine the causes of a problem, before taking improvement deportment.

Research has suggested that data-based decision-making (data employ) can contribute to increased student learning and accomplishment (east.g. Lai et al. 2014; McNaughton, Lai, and Hsaio 2012; Poortman and Schildkamp 2016; Van Geel et al. 2016). However, in order to realise the full potential of data in education, more insight is needed urgently in terms of the best ways to utilise data to improve the quality of schools. This theoretical paper volition focus on the findings of inquiry conducted in this area and suggests possible hereafter directions for further research. The discussion is informed by cartoon upon a range of relevant literature, including five recent review studies in this expanse (Datnow and Hubbard 2016, 2015; Heitink et al. 2016; Hoogland et al. 2016; Schenke and Meijer 2018).

School improvement may be conceptualised every bit an iterative process in which the use of data plays an important function (see Effigy one). As school improvement should start with clearly defined, specific and measurable goals, several models of information use emphasise the importance of goal setting (due east.g.Mandinach et al. 2008; Marsh 2012; Marsh, Pane, and Hamilton 2006; Schildkamp and Poortman 2015). With goal setting playing a crucial function, all the other steps in the school improvement process need to accept these goals into account. Goal setting, therefore, is placed at the top of the model in Figure 1. Further, data collection needs to be related to the goals; sense-making should revolve effectually the goals; actions should exist directed towards these goals; and the evaluation should focus on whether or non the goals were achieved.

Figure one. The iterative process of improvement: improving the quality of educational organisations through the apply of data.

Source: original figure created by the author for this publication.

The data that are collected to attain these goals tin can be of different types. It may be systematically collected data (sometimes called formal data), such as assessment results, surveys and systematic classroom observations. In about existing models of data use, the focus is on data nerveless in a formal and systematic manner (e.m. Mandinach et al. 2008; Marsh 2012; Marsh, Pane, and Hamilton 2006; Schildkamp and Poortman 2015). However, data can also be collected in a less formalised way – for instance, through informal classroom observations and discussions (sometimes called informal data). Such information may be nerveless as part of formative assessment within an cess-for-learning arroyo (e.1000. Heitink et al. 2016). A tertiary source of data that schools can use includes educational research prove (e.one thousand. Brown 2015). In addition, contempo developments in the field of 'big data' suggest that this may too be a fruitful data source that could be used to help inform decision-making in instruction (due east.g. Veldkamp et al. 2017).

Once dissimilar types of information take been nerveless, a procedure of sense-making has to start. This process is described in several models (e.k. Mandinach et al. 2008; Marsh 2012; Marsh, Pane, and Hamilton 2006; Schildkamp and Poortman 2015). Some key questions are: how can the nerveless information exist analysed and interpreted, and what do the data mean in relation to the goals? As described in such models, this process of sense-making can pb to the implementation of concrete improvement actions, the outcomes of which later need to be evaluated, based on data, to determine whether the previously fix goals were achieved. This whole iterative process is displayed in Figure 1. This figure broadly and generally integrates elements of different models from the field of data utilize and combines these models with cognition from the fields of determinative assessment, inquiry use and 'big data'. Of importance here is the mode that the different components of the model are put into exercise. All the components of this figure will be farther discussed below. In this paper, research insights pertaining to each of these components will be discussed, and gaps in the literature volition be identified.

Data utilize and the iterative process of school improvement

Goal setting

Goal setting is placed at the summit of Figure 1. This draws attention to the notion that information use does not start with data. Rather, data are just one of the tools that schools tin employ in their school comeback processes. This means that data use needs to start with certain goals, often continued to improving the quality of teaching and learning. These goals need to exist concrete and measurable. At the student and classroom level, they may pertain, for instance, to student learning goals. At the school level, they may chronicle to certain aggregated achievement goals that involve the whole schoolhouse. At the system level, for instance, the goals may be benchmarks set by local or national schoolhouse evaluation bodies, and/or educational standards fix by regional or national government policy. It is important to recognise here that goals can never exist value-neutral, every bit they are based on assumptions and beliefs near what is valued. At national level, for example, the goals may be educational standards that reflect the particular educational policies of a authorities at a given time. At the school and classroom level, goals may exist based on what experts in a domain hold is important to learn. These goals are often a result of deliberation, negotiation and contend between dissimilar stakeholders (Penuel and Shepard 2016); dissimilar stakeholders may accept unlike goals, which may not ever align. Moreover, goals may also evolve and change over time.

Important stakeholders in the goal-setting process are schoolhouse leaders. School leaders demand to residual the diverse goals of different stakeholders with the culture, the vision, mission and values of the schoolhouse. Information technology is necessary for schoolhouse leaders to translate policy into the specific goals they recollect the schoolhouse should work on: they tin can prioritise sure goals, and they can influence what data needs to be collected. A primal job for school leaders is to make certain that school comeback goals are collectively developed, and that there is dialogue nigh these goals. Additionally, school leaders tin can shape where and how sense-making happens, and whether the suggested improvement actions are really implemented and evaluated. The attitude and behaviour of schoolhouse leaders volition also influence the attitude and behaviour of teachers, including the degree to which teachers are involved in the goal-setting procedure, their willingness to commit to the set goals, and the degree to which teachers are willing to appoint in information use, and see data use as a meaningful strategy for school improvement (Coburn 2006; Park, Daly, and Guerra 2013).

Previous research apropos specific goals in schools has shown that they tin be divided into three blocks of goals: accountability goals, school evolution goals and instructional goals (e.g. Schildkamp, Karbautzki, and Vanhoof 2014; Schildkamp and Kuiper 2010; Schildkamp, Lai, and Earl 2013; Schildkamp et al. 2017). With regard to accountability goals, for case, school staff tin use data, such as assessment results and internal evaluation results, in club to demonstrate the extent of progress to school evaluation bodies and parents. Schoolhouse development goals concern monitoring and improving the functioning of a school, which tin can involve policy development, curriculum development and planning for the professional development of schoolhouse staff (e.g. Schildkamp and Kuiper 2010; Schildkamp, Lai, and Earl 2013). Thirdly, instructional goals relate to improving the quality of educational activity in the classroom. Data tin be used, for case, for setting learning goals, determining students' progress and giving students feedback on their learning process (east.g. Schildkamp and Kuiper 2010; Schildkamp, Lai, and Earl 2013).

The employ of data should lead to school improvement, which is often framed equally increased student accomplishment. Nonetheless, a renewed emphasis on determinative assessment means that information technology is increasingly recognised that attending should also be given to learning (Penuel and Shepard 2016; Van der Kleij et al. 2015). Equally observed by Penuel and Shepard (2016), the focus should non concentrate solely on achievement on a certain kind of (standardised) assessment only too consider learning in a broader sense and students' ability to engage in problem-solving and reasoning. Yet, regardless of the focus on learning and/or accomplishment, there is always a tension evident between using data for school development and instructional goals, and using information for accountability goals.

Further enquiry could helpfully explore these tensions more fully and investigate the conflicts that are probable to arise between these dissimilar types of goals (Hargreaves and Braun 2013). It is widely held that an overemphasis on accountability can sometimes have unintended and undesirable consequences, such every bit: undue focus on a specific blazon of student who can help improve the schoolhouse's status on accountability indicators; and then-called 'gaming the system' practices to improve the school'southward condition on accountability indicators; 'teaching to the test'; excluding certain (weaker) students from a examination; and encouraging low performing students to drop out (Ehren and Swanborn 2012; Hamilton, Stecher, and Yuan 2009). On the other hand, it has besides been recognised that some forms of accountability tin also make a organisation more transparent, and data used in such a system can reveal aspects that need improvement (Tulowitzki 2016). Nosotros argue that is of import that data are used meaningfully beyond all iii purposes: accountability, schoolhouse development and instructional improvement. Later all, as Earl and Katz (2006) aptly country: 'Accountability without comeback is empty rhetoric, and improvement without accountability is whimsical activity without direction' (p. 12).

Still, information technology can be the example that school leaders and teachers start the process of schoolhouse improvement with data itself, rather than with articulate and measurable goals. Considerable amounts of data are nerveless in schools because people have been collecting them for many years, rather than for purposeful reasons. These data may once have served their purpose, merely as society and schools are constantly changing, for every data source available in the school, it is of import to ask what the goal of the data collected actually is and why certain aspects are being measured (Tulowitzki 2016). Moreover, in that location is a need to think almost collecting unlike kinds of data, as the school may develop new goals over time for which data has not been collected – for instance, in areas that may exist less oftentimes assessed, such likewise-beingness, citizenship and information literacy. Equally, students' development of cocky-regulated learning skills – i.east. the procedure of students taking responsibility for their ain learning (Black and Wiliam 2009) – is another such surface area where information may not have been collected. Clearly, setting goals for new areas will require investigation and perhaps re-evaluation about what types of data to collect, and how all-time to collect it.

With the increasing availability of information in today's society, data may exist used for educational purposes that have not yet been considered. A characteristic of 'large data', for case, is that different types of data can be linked to each other and that it is possible to await for patterns in these data sets without having pre-defined hypotheses. In this way, patterns may be discovered that have never been thought of before, which can lead to new possible applications, purposes and goals of data use. This surface area needs further investigation in relation to the utilise and application of information in instruction (Veldkamp et al. 2017).

Collecting data

Subsequently the goals have been fix, information must be nerveless to determine if these goals are being reached (see Figure one). In today'due south globe, it is given that more and more information are available. We argue that it is important to triangulate and utilize multiple data sources to improve didactics, rather than having an over-reliance on assessment data in its narrowest sense. Based on previous studies (see, e.g. Brownish, Schildkamp, and Hubers 2017; Kippers, Schildkamp, and Poortman 2016; Schildkamp, Lai, and Earl 2013; Van der Kleij et al. 2015), an intentionally broad and inclusive definition of data is proposed, including the following aspects:

Formal data: This includes any systematically nerveless relevant information virtually students, parents, schools, schoolhouse leaders and teachers, and the community in which the schoolhouse is located. These data may be derived from both qualitative (due east.g. structured classroom observations) and quantitative (e.g. assessment results) methods of assay (Lai and Schildkamp 2016). This is often referred to every bit data-based controlling, data-driven decision-making or data-informed conclusion-making, which tin can be divers equally the process of 'systematically analyzing existing data sources within the school, applying the outcomes of analyses in order to introduce teaching, curricula, and school operation, and, implementing (e.g. 18-carat comeback actions) and evaluating these innovations' (Schildkamp and Kuiper 2010, 482).
Informal data: Teachers collect data on the needs of their students in everyday practise. This may be, for example, by observing their students and by engaging in conversations with their students within an assessment-for-learning arroyo: 'Role of everyday exercise by students, teachers and peers that seeks, reflects upon and responds to information from dialogue, demonstration and ascertainment in ways that enhance ongoing learning' (Klenowski 2009, 264). These data are frequently collected apace; 'on-the-wing', so to speak. This is also function of what is sometimes referred to as professional judgement or intuitive data collection (Vanlommel and Schildkamp 2018).
Enquiry results: Teachers can also employ existing research with the aim of improving educational activity. This is ofttimes referred to as research-informed teaching practice, which, as Flood and Brown (2018) note, has been defined as 'the process of teachers accessing, evaluating and applying the findings of bookish research in social club to better teaching and learning in their schools' (Overflowing and Brown 2018, pp.347–348). Within the category of enquiry results, a distinction can exist fabricated between practitioner or activeness research results (derived from practitioners conducting enquiry in their own schools), scientific research results from a study in which a schoolhouse participated, and scientific research results from a written report in which a school did non participate (Brownish 2015).
'Big data': Big data are characterised by the and so-called 'three Vs': Volume, Diverseness and Velocity. Big data concern huge amounts of data (Volume), in varied forms (Variety), existence continuously added to and updated (Velocity) (Laney 2001). These data can be used to monitor every bit well as to predict the performance of an organisation (Veldkamp et al. 2017).

In some studies, the use of a broad spectrum of data types is summarised nether the term prove-informed practise. As observed in Brown (2015) and elsewhere in the literature, in bear witness-informed practice, instruction is consciously informed by formal data, breezy data and research. Based on Veldkamp et al. (2017), 'big data' are added as i of the possible sources of data in show-informed practice. There is also some overlap between the different information sources: for example, formal data may be used in scientific research as well equally in practitioner research; informal data may be used in practitioner research. Big information are derived from formal data, informal information and research results (see Figure 2). Information technology is also important to acknowledge here that all data are always socially constructed: information drove is never a value-costless process (Eynon 2013). For case, even when researchers develop measurement instruments to collect data, sure choices are made with regard to what to measure and what not to measure.

Effigy 2. Visualisation of different types of evidence.

Source: original figure created by the writer for this publication.

The goals determine the types of data that stakeholders collect. However, there is a hazard that more data are nerveless apropos concepts that are easier to mensurate. This tin can lead to goal displacement, in which an arrangement focuses solely on the goals for which it has data (Lavertu 2014). The danger here is that organisations may focus on the measurable at the cost of other important goals. Hereafter research could, for case, focus on the challenge of how best to measure concepts subsumed within what are sometimes referred to as twenty-first-century goals (eastward.1000. motivation, critical thinking, etc.), and then that data can be used for a wide range of goals relating to improving the performance of schools.

Furthermore, new tools are being developed that can help schools to collect and shop data, and to visualise and analyse these data (east.g. data warehouses, dashboards, data lockers, data analytics, data mining tools, auto learning). This leads to new opportunities to unlock the potential of data use for improving the quality of schools, also as significant challenges (due east.thousand. how to develop loftier-quality tools, how to support people in making use of these tools, how to foreclose possible corruption and misuse). All of these questions need farther research. Explicit attention needs to exist paid here to 'high tech' (e.g. the development of high-quality tools) and 'human touch' (e.chiliad. the use of these tools) aspects. An instance of a pertinent research question hither may be: how can we develop and use online learning environments and digital adaptive assessments to make students more responsible for their ain learning in such a way that information technology leads to increased educatee achievement? Moreover, we are merely beginning to unlock the potential of the use of large data. Every bit described by Veldkamp et al. (2017), one advantage of big data appears to be that it may lead to new insights with regard to how to amend operation. Big data, for case, tin can be used to predict the future functioning as well as the problems of an arrangement, so that people tin act in a timely mode and, perhaps, even forestall some of the bug from actually occurring. However, overarching questions that need farther research investigation in this regard include how to combine different sources of data in a reliable and valid manner, how to develop models that can predict future performance; who gets access to what information from an ethical perspective; what questions tin can be answered based on big data, as well as questions about the many other social, technical and ethical implications of big data.

Sense-making

Subsequently data accept been nerveless, the users must engage in a sense-making process (Weick 1995; Vanlommel et al. 2017) (meet Figure 1). The data demand to be analysed and interpreted to place problems (i.eastward. when users are not meeting the agreed goals) and possible causes of these issues. At this stage, the data users take to appoint in a sense-making process, because the implications regarding solutions to the problems and consequent actions based on the analysis of the data are often non self-evident (Mandinach et al. 2008; Marsh 2012; Vanlommel et al. 2017). During the sense-making process, the data needs to be combined with local expertise, understanding and experience, to turn it into cognition that can be used in the improvement process. This leads to conclusions and an action plan. Previous research (e.g. Gelderblom et al. 2016; Schildkamp, Poortman, and Handelzalts 2016) has suggested the ways in which teachers and school leaders may experience some difficulties with some aspects of this procedure. For example, this may include difficulties with analysis and/or translating the information into an action plan (e.g. Chocolate-brown, Schildkamp, and Hubers 2017; Schildkamp and Kuiper 2010; Schildkamp and Poortman 2015; Schildkamp, Poortman, and Handelzalts 2016).

Sense-making is not a straightforward or exclusively rational process (Bertrand and Marsh 2015; Kahneman and Frederick 2005). The same data might have different meanings for unlike people; decisions can never be completely based on data, because people filter data through their own lenses and experiences, in which intuition also plays an important role (Datnow, Greene, and Gannon-Slater 2017). In addition, it is axiomatic that people may too be inclined to use simpler, quick strategies that require less cognitive effort (Kahneman and Frederick 2005). This process of sense-making may lead to fake interpretations when people try to fit data into a frame that confirms their assumptions and pre-existing beliefs without searching for alternative explanations, when their conclusions are based on a express set of data (lack of data triangulation), or when their interpretation is greatly influenced by prior beliefs (Kahneman and Frederick 2005; Kaufmann, Reips, and Merki 2016). A study conducted by Vanlommel and Schildkamp (2018) identified that all of these things tin happen when teachers are trying to brand sense of the formal and informal data they have collected to guide their decision-making.

Dissimilar stakeholders at different levels are involved in this sense-making process. At the system level, policy-makers are required to make sense of different types of data to develop policy. At this level, sense-making of standardised assessment data often plays an important part (Rickinson et al. 2017). Concerns at this level that need to be addressed include the repeated use of similar sources, a focus on well-known sources, the use of sources that are familiar and comfortable, and the use of data that are easy to locate (Rickinson et al. 2017). Elsewhere, at the level of the schoolhouse, principals (preferably together with teachers) need to brand sense of the information nerveless. The focus here is also often on achievement data. However, if student learning is to be the focus, principals need to exist able to collect and make sense of data from other data sources as well, to be able to read the contextual circumstances so that they tin can act in ways that are responsive to the situation (Clarke and Dempster 2016). Equally stated past Earl (2015) and as advocated in this paper, it must be recognised that information come in many forms. At the level of the classroom and the individual student, teachers make use of a variety of formal and breezy data. However, some evidence suggests that teachers often rely more on breezy data than on formal data, and it is noteworthy that confirmation bias still plays a major role hither (Bolhuis, Schildkamp, and Voogt 2016; Farrell and Marsh 2016; Katz and Dack 2014; Vanlommel and Schildkamp 2018).

While considerable attention has been paid to the utilise of data by school leaders and teachers, less attention has been paid to data utilise by policy-makers and by students. Some studies have been conducted in this area for policy-makers (see, e.one thousand. Campbell, Pollock, Briscoe, Carr-Harris, & Tuters, 2017; Rickinson et al. 2017), and for students (come across, e.g. Jimerson and Reames 2015; Kennedy and Datnow 2011). However, there is a demand for further work to be conducted. For example, students are crucial stakeholders in the process of data employ, and they tin use data to actively steer and amend their own learning, by themselves, with their peers, and with their teachers. Moreover, the different stakeholders may also interact with each other, and these interactions take place across settings such every bit schools and classrooms. We propose that further research is necessary to explore how stakeholders' interactions with each other and with information tin can back up learning. Furthermore, dissimilar types of data lead to different types of sense-making processes. Analysing and interpreting formal information, for example, is an entirely different process from analysing and interpreting breezy data. The latter tend to be acquired at a much faster pace, and therefore too require a much faster sense-making and conclusion-making process: this may present challenges for teachers (Kippers, Schildkamp, and Poortman 2016), who may non have been supported with professional development in this expanse. We offset need to investigate exactly what competences are needed to utilise different types of information for comeback purposes. One way of investigating this would be by conducting a task analysis of the competences needed to use these types of data, by using the 4-Components/Instructional Design model (Van Merrienboer 1997). Based on the results of a task analysis, professional development interventions to support teachers in the apply of dissimilar types of data could exist developed, implemented and evaluated.

Finally, information technology is important not merely to develop, implement and evaluate professional evolution interventions for data use (e.g. programmes such equally those investigated in Schildkamp and Poortman 2015; Van Geel et al. 2016) but as well to invest in instructor education colleges. Historically, bereft attention has been paid to data use in near instructor education programmes (Mandinach and Gummer 2013). We argue that this needs to alter if we are to realise the total potential of using data to increase student learning.

Action and evaluation

The outcomes of the sense-making process described above can pb to dissimilar types of improvement actions (see Figure ane). For instance, it may lead to curriculum changes and to changes in instruction. It may also lead to changes to the cess practices in a schoolhouse, such as the implementation of more determinative assessments instead of over-reliance on summative cess (due east.thou. Gelderblom et al. 2016; Poortman and Schildkamp 2016; Schildkamp, Poortman, and Handelzalts 2016). In a information team study, Poortman and Schildkamp (2016) discovered that using data to improve the quality of instruction in about cases includes the use of data for three important pillars of school improvement: (1) curriculum (e.grand. improving curriculum coherence), (2) assessment (eastward.g. developing and implementing (formative) assessments across the years to identify at-run a risk students) and (3) instruction (eastward.g. providing additional instructional support to at-risk students).

It is evident that the use of data for curriculum, assessment and instructional actions requires instrumental data apply: that is, actually making changes in schoolhouse and in classrooms. Ofttimes, data are used in a conceptual manner, which means that data employ leads to changes into teachers' and school leaders' thinking (Farley-Ripple et al. 2018; Lai & Schildkamp, 2013; Schildkamp and Visscher 2010; Weiss 1998), although this does not necessarily interpret into concrete improvement deportment. Sometimes, information are used strategically; that is, information are manipulated to accomplish specific power or personal goals (Farley-Ripple et al. 2018). Data can also exist used symbolically, which implies that educators are using information but not in any meaningful mode (Farley-Ripple et al. 2018), often to (seemingly) comply with external pressure and demands. Finally, data can likewise be misused or abused – for example, when so-chosen 'teaching to the test' occurs or when attention is placed solely on the students on the verge of achieving some kind of threshold or criterion (Booher-Jennings 2005). Symbolic data use, misuse and abuse are often consequences of an overly strong focus on accountability instead of on improvement (Datnow and Park 2018).

Implementing an activity plan based on data is non an easy task for teachers and school leaders (Schildkamp and Visscher 2009; Van Petegem and Vanhoof 2004); in order for this to happen, we argue that there is a demand for them to connect the data to their own functioning (Schildkamp, Poortman, and Handelzalts 2016). Black and Wiliam (1998) referred in this context to profound changes in the way in which teachers view their own role and considerable changes in their daily practices in the classroom. Studies (east.g. Gelderblom et al. 2016; Wayman, Jimerson, and Cho 2012) have shown that the availability of data does non ensure the actual utilise of data to brand changes in the instructional practices that happen in the classroom. Further studies, we propose, should focus on how to translate information into improved practices in the classroom, and thus should concentrate on studying in depth what happens in the classroom (e.g. by conducting classroom observations). Finally, to come full circumvolve, information technology is important to evaluate the process of data use in a schoolhouse. Pertinent questions include: were the deportment implemented? – did they pb to the desired effects among the different stakeholders? – and was the goal, as stated in the offset of the process, reached? To be able to decide this, new data volition need to be collected.

It has to be noted here that researchers could also play a role in this evaluation phase, and in the co-product and synthesis of bear witness. Research results can form a source of testify that educators tin can utilise in the school improvement process, and researchers can assist educators in the evaluation of their schoolhouse improvement processes. Moreover, researchers can assist schools in synthesising evidence that is available. Some studies have been conducted in this area with promising results (e.g. Brown 2015; Brown and Greany 2018; Campbell, Pollock, Briscoe, Carr-Harris, & Tuters, 2017; Stoll et al. 2015; Zala-Mezö, Strauss, Müller, Häbig, Kuster, Herzig and Unterweger, 2018). For example, Zala-Mezö et al. (2018) conducted a report into student participation for school improvement. They also fed back their research results to the participating schools, which led to increased sensation of the importance of student participation in the school. This example is encouraging, just more than evidence is needed on the role of researchers in supporting schools in the use of data.

Discussion

Limitations

In this paper, although directions for further enquiry are offered, it is important to note that at that place are many more possibilities (also every bit challenges) in this field – too many to hash out in one paper. A systematic literature review in all the research fields described in this paper (i.e. information use, formative assessment, research apply and large information) was beyond the scope and intention of this theoretical discussion paper. Further research is necessary to investigate the approaches and processes presented here, and besides to be able to provide unlike stakeholders with practical examples.

Recommendations for further enquiry

Multidisciplinary enquiry is condign increasingly important in the field of data apply. To benefit fully from of all the rich potential of data employ, expertise must be combined in the fields of, for case, technology, data mining, and auto learning and psychology, every bit data use is even so fundamentally a human endeavour with human goals. The field of data utilise in education is strongly related to the field of data use in medicine. Sackett et al. (1996) described evidence-based medicine as the employ of the current best prove in making decisions about the care of patients. Evidence-based instruction can be similarly described every bit the employ of the current best evidence in making decisions nearly the quality of pedagogy and the learning of students. Information technology would be interesting to report this relationship farther and compare data utilise in didactics organisations and other systems, including health.

Although not described here in depth, it is important to stress that the different directions for future research volition crave a range of dissimilar inquiry methodologies, ranging from small-scale micro-process studies, to investigate how and why something is happening as information technology is (east.thou. studying sense-making) to large-scale experimental studies to study, for instance, the furnishings of a specific intervention. The field should explore the latest methodologies, for example, with regard to how to measure concepts that are challenging to measure out.

It is too important to note here that the process of data utilize does not happen in isolation. Research can focus on unmarried components, but must also be contextualised. Information apply is influenced past system, organisation and squad/individual level factors (Datnow, Park, and Kennedy-Lewis 2013; Schildkamp and Kuiper 2010). For case, at the level of the organisation, the school evaluation torso influences this process for schools. At the school level, for example, school leaders accept a directly influence on this process. At the team and teacher level, it is axiomatic that school leaders and teachers need knowledge and skills to be able to utilize data effectively (Schildkamp et al. 2017). Different stakeholders at different levels of the system will have different goals, which will non necessarily marshal and sometimes may even exist contradictory.

Based on previous research conducted in different countries (e.g. Schildkamp and Kuiper 2010; Schildkamp, Karbautzki, and Vanhoof 2014; Schildkamp and Poortman 2015; Schildkamp et al. 2017; Schildkamp & Poortman, 2019), we have gained noesis about the effective features of professional development in the use of data, and we are more enlightened of the most important enablers and barriers when it comes to using information to better the quality of teaching and learning. However, what we do not yet understand is how we can create those enablers and remove those barriers to unlock, fully, the potential of data utilize. Essentially, we need to focus on how to make data use an organisational routine that leads to sustainable improvement for all of the stakeholders involved.

Recommendations for practice

A crucial stakeholder in the whole data use procedure is the schoolhouse leader. Information technology is necessary for school leaders to use data literacy skills, so that they tin can monitor, model, scaffold, guide and encourage the use of information (Hoogland et al. 2016; Datnow and Hubbard 2015, 2016; Schenke and Meijer 2018). To build a information use culture, school leaders demand to recognise that a compliance orientation towards data use will not pb to accurate or sustained data apply (Park, et al., 2013). Data use should be framed as a continuous school improvement process, and not as an activity to meet accountability demands (Hoogland et al. 2016; Datnow and Hubbard 2015, 2016; Murray 2014; Park et al., 2013; Schenke and Meijer 2018). Critical dialogue between different stakeholders is crucial here, and it is important that data use does non focus solely on accomplishment and the deficits of educatee capabilities, but that it also focuses on students' strengths (Park et al., 2013). Furthermore, schoolhouse leaders need to distribute leadership then that teachers are empowered in the data use process, and feel that they tin can accept action based on information (Hoogland et al. 2016; Datnow and Hubbard 2015, 2016; Schenke and Meijer 2018). Finally, school leaders need to facilitate the apply of data, by providing access to data and providing fourth dimension for information use, including professional development (Hoogland et al. 2016; Datnow and Hubbard 2015, 2016; Schenke and Meijer 2018).

Although a lot of data use professional development interventions are bachelor worldwide, not many of these interventions accept been studied systematically. Schildkamp and Poortman (2019) identified and analysed 11 data use interventions which were studied scientifically. Based on these interventions, Schildkamp and Poortman (2019) identified several features of effective in-service professional person development in the apply of data (e.g. creating structures and protocols to develop data use; providing professional development over a longer time period; and making the link betwixt data and instruction explicit). We believe that these features should be taken into consideration when coaching and training schools in using data for school comeback. In previous inquiry, Schildkamp and Poortman (2015) developed ane of the effective interventions that are described in Schildkamp and Poortman (2019): namely, the information squad intervention. This is one of the few interventions worldwide that has been systematically researched to evaluate and theorise the furnishings of professional evolution over an extended period of time in different countries (The Netherlands, Sweden, Kingdom of belgium, England and the The states). The data team intervention focused on professional development in the use of data for schoolhouse comeback (east.k. Schildkamp and Poortman 2015). The program consists of teams who use data collaboratively to solve a selected educational problem within the school. Encouragingly, research results indicated that participation in the intervention led to increased data literacy (Ebbeler et al. 2017; Kippers et al. 2018). It was reported that several schools were able to solve their trouble and better student achievement (Poortman and Schildkamp 2016). The intervention has been used in primary, secondary, vocational and higher education. The side by side recommended footstep would exist to written report the sustainability of this and other data use interventions.

Conclusion

In countries around the world, schools are increasingly expected to use data to monitor their performance, diagnose areas for improvement and use information to make informed decisions to improve the quality of education efficiently and finer. However, as described in this paper, data use for school comeback is a complex procedure. In this paper, an overview has been provided of how data tin can be used for schoolhouse improvement at different levels – at student, classroom, school and organisation level. Enquiry findings concerning information use for improving the quality of schools have been discussed, and different concepts of information employ have been explained, including enquiry results in this area. Directions for further research take also been suggested. It is our hope that this theoretical give-and-take paper can be used as a starting signal for further research into the use of information for schoolhouse improvement, so that data use tin can fulfil its potential of improving the quality of education for all students.