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2.2.1 Evidence of the impact of data use on improvement of learner achievement

The use of data was employed as an accountability mandate for the No Child Left Behind law (2001) in the US to ensure that schools were meeting yearly targets for improvement referred to as “Adequate Yearly Progress or AYP”. This policy implied that the availability of data would inform and initiate changes in teaching practice. There are also case studies that serve as proof that data has supported educational decisions (Feldman & Tung 2001; Lachat 2002; Pardini 2000; Protheroe, 2001; Symonds, 2003). However, the requirements of NCLB

created pressure to carefully monitor learner performance on the high-stakes assessments that determined success or failure (Mandinach, Rivas, Light, Honey, & Heinze, 2006).

Assessment data have been used for accountability and high standards in order to measure school effectiveness through learner performance and is the heart of reform efforts in

education (Earl, 2005). Other research studies show that equity and accountability have made it imperative for educators to base decisions on accurate and meaningful data about learner achievement (Johnson, 2002; Lachat, 2002). However, Ingram et al., (2004) question the assumption that accountability data will lead to positive change in the daily interaction between teachers and learners.

19 A research report from the Pathways to College Networks (n.d) study on using data to

improve educational outcomes emphasises the use of data as a powerful tool to strengthen academic outcomes for all learners, in particular the underserved. The argument presented is that data informs whether certain groups of learners have been disproportionately tracked into lower level classes of special education, and whether or not they are proportionately

represented. To improve educational outcomes, data provide schools, districts and national departments with opportunities to share best practices based on hard evidence. According to the Annual National Assessment report (2013) efforts by the Minister of Basic Education in South Africa is to use data to identify and support schools that are underperforming in learner achievement, based on the annual national assessment data in schools. These efforts are diminished by a lack of knowledge and skills necessary to use data to improve school

effectiveness and/or learner achievement in some of the schools. These schools depend on the districts to aggregate collected data, and learn of their performance in relation to others when a list of all schools in each district is compiled according to levels of performance. This is demoralising and discourages teachers’ efforts. It also makes some schools give up on attempts to improve school effectiveness. On the other hand, it becomes a challenge to schools that take responsibility for the use of data inquiry to understand learner needs. In this way they find ways to improve teaching, in order to engage learners in different learning experiences.

Researchers argue that the thoughtful use of learner data can be positively correlated with learner achievement (Edmonds, 1979; Springfield, 1994; Teddlie & Reynolds, 2000; Weber, 1971). This notion is supported by consensus among researchers who claim that data use is central to school improvement and school effectiveness (Chrispeels, 1992; Earl & Katz, 2002; Protheroe, 2001; Wayman & Stringfield, 2003). Data use is effective when teacher

20 decisions about instructional effectiveness are based on assessments of learners’ actual

proficiencies in various skill areas (Pardini, 2000). According to the Pathways to College Networks study, greater reliance on data has led some teachers to be more accountable to one another through collaborative school improvement work, and reflective practice. Data is used to challenge untested assumptions and beliefs about some learner abilities.

As teachers struggle with the challenges of changing current school structures into effective learning environments, an increasing number of researchers argue that data is considered to be a powerful ally in stimulating positive change in teacher practice and improved learner performance (Lachat & Smith, 2005). The use of data to support learner success and school improvement is considered one of the foundations of using data to improve learner

performance (Bernhardt, 2000a; Codding & Rothman, 1999). Mason (2002) notes that the types of data collected for school improvement determine the types of decisions that are made for learner improvement. As a result, teachers are able to modify their instructional strategies when they have current information about the skill levels and proficiencies of their learners. However, Ingram et al., (2004) argue that the implementation of central assessments

influenced changes in the topics taught, but that there is little change in teachers’ instructional approaches. Instead, teachers are found to teach new topics using conventional strategies (Firestone, Fitz, & Broadfoot, 2000).

Rallis and McMullen (2000) argue that inquiry-based schools promote a culture of high standards, and the use of appropriate assessments to improve learning. Therefore, schools using data-driven decision making are expected to monitor effective progress, to use data to understand where learners are academically, and to establish improvement plans that are

21 targeted, responsive and flexible (Mitchell, Lee & Herman, 2000). The data-driven inquiry process acts as a tool to enable school leaders and teachers to implement change in schools, but it is often considered furthest from current standards of excellence (Holcomb, 1999, 2001). The development and implementation of data-driven decision-making tools is also considered a necessary step towards the effective use of data (Mandinach et al., 2006).

Recent research by the panel of the Institute of Education Sciences for the National Center for Education Evaluation and Regional Assistance indicates that changes in accountability and testing policies still advocate that educators with access to an abundance of learner data, from various tasks and tests, can strengthen the role of data for guiding instruction and improving learner performance (Hamilton, Halverson, Mandinach, Supovitz & Waynman, 2009). In the United States, the Department of Education is also calling upon schools to use assessment data to respond to students’ academic strengths and needs (American Recovery and Reinvestment Act, 2009; Department of Education, 2009; Obama, 2009). The

significance of data is that it provides ways to assess what learners are learning, and to what extent they are making progress.

According to Knapp, Swinnerton, Copland and Monpas-Huber (2006), the use of data requires knowledge of concepts, theories and interpretative frames of reference. To use data effectively, educators should be able to ask questions and obtain insight about learner progress, monitor continuous improvement, and tailor teaching practice to meet the needs of learners. Timperley’s study (2009:1) identifies seven conditions required for the use of data to have the impact hoped for to improve teaching and learning. First, the data need to provide teachers with curriculum-relevant information. Second, teachers have to see the information

22 as something that informs teaching and learning, rather than as the reflection of the capability of individual learners; something to be used to sort, label and provide credentials. Third, teachers need to find sufficient meaning in assessment data in order to make appropriate adjustments to their practice. Fourth, school leaders need to have conversations with teachers to unpack meaning. Fifth, teachers have to improve their knowledge of pedagogical content, in order to make relevant adjustments to classroom practice in response to the assessment information. Sixth, that school leaders need to know how to lead the kinds of change in thinking and practice that are required for teachers to use the data. Finally, all those within the school need to be able to engage in systematic, evidence-informed cycle of inquiry to build the relevant knowledge and skills already identified.

In addition, the Institute of Education Sciences panel of researchers Hamilton, Halverson, Jackson, Mandinach, Supovitz, and Wayman (2009) offers five recommendations for the use of data in instructional decision-making aimed at improving learner performance.

 Teachers should use data from multiple sources (such as oral and written classroom tasks, tests and examinations) to set goals, make curricular and instructional choices, and allocate instructional time. Use of data should be part of a cycle of instructional inquiry aimed at ongoing instructional

improvement intended to encourage and motivate high learner performance.  The panel provides guidance about how teachers can instruct learners in the

use of their assessment data to develop personal achievement goals and guide their learning. It suggests that teachers use learners’ goals to understand factors that may motivate learner performance, and adjust teaching practices accordingly.

23  It suggests that school leaders establish a comprehensive plan for data use that

takes into account multiple perspectives. It also emphasises the need for school leadership to establish structures and practices support teachers as they implement data use and interpretation.

 The third recommendation relates to how schools and districts can encourage educators to collaborate through data use. Collaboration is seen to create or strengthen the whole school in sharing expectations and common practices around data use.

 At district level, suitable data use requires a secure and reliable data

management system for collaboration, development and maintenance of high quality achievement through data use.

For the purpose of this study, the recommendations from bullets one, three and four are relevant in so far as they shape and inform the research questions and findings of this study. Recommendation two relates to classroom practices. These were not included, since

classroom observation was not part of the process of this study. The fifth recommendation is directed at the district level and this research study is limited to findings at school level.

Additional recommendations from other researchers identify five key strategies for the use of data-driven decision making in performance driven school systems (Datnow et al., 2007). These include the building of a foundation for data-driven decision making as integral to establishing measurable goals. Leaders within the school should create explicit norms and expectations to establish a culture of data use and continuous improvement. There is a need to

24 invest in an information management system, and the school must dedicate an individual or team to be responsible for supporting data analysis. They will serve as experts with whom the teachers can work. The school will have to select data with multiple purposes to inform and guide improvement efforts. To build school capacity for data-driven decision making, schools would have to invest in professional development, provide support for teachers in modelling data use, and data discussions, and provide time for collaboration so that teachers and school leadership can learn; sharing their understanding of learner needs. Lastly, in analysing and acting on data to improve performance, the schools would be creating structures to foster data-driven decision making.

For successful implementation of the strategies listed above, advice is that schools invest time and resources in building a solid foundation for system-wide improvement efforts. The

process also requires measureable goals at the school system, school classroom, and individual learner level.