Data-driven Practices for School Improvement

When the notion of accountability proliferates in schools, principals are made responsible for student academic achievement. This has made it necessary for principals to implement and manage data systems that accurately report on the instructional practices that can inform on the ways to improve student achievement.  To address this, teachers need to create formative data generation that informs teaching and learning (Mandinach & Schildkamp, 2020).  This has given rise to an avalanche of data and this data overload causes principals and teachers to either swim or drown in the sea of data.

It is, therefore, prudent to look into questions such as what data to collect, how to collect the data, and how to use the data to integrate them into decision making and subsequently school practices.  More data does not lead to school improvement but instead, data should be used as evidence to support instructional decisions and leadership practices (Schildkamp, 2019).  However, there is a problem because principals and teachers must be trained to find the story in the data. Another problem in data-based information requires trust and transparency so that teachers feel safe and not consider it as evidence of their lack of effort in their work.

Data should be collected, interpreted and used as formative data to create feedback loops around the evidence to improve teachers’ instructional and principals’ leadership practices.  This way, there is concrete evidence to guide practices rather than the use of intuition, which could lead to prescribing flu medication to a cancer patient.  Another benefit is, the focus will be on the students by examining the detailed information around each student and each teacher’s instruction.

To provide some examples of how data can be used to achieve student achievement: the class teacher create an ‘academic audit’ for each student to track their performance on every subject.  It starts with, at the beginning of the year, stating the takeoff value (TOV) and setting the expected target rate (ETR) to be reached at the end of the year.  In between, the student and their respective subject teachers track the improvement or deterioration of grades for each assessment.  This way, intervention plans could be provided in a timely manner, rather than wait for the final grades at the end of the year when nothing can be done to improve on the student’s scores. Another advantage is, the student is provided with an expectation to achieve a target set and a belief that they can achieve the potential.

To reinforce the benefit of the academic audit, teachers should also analyse the items or questions in the test.  Each question is traced to the chapter and when (which grade the student was in) it was taught and this analysis will provide the information of which part of the syllabus the students are weak in and which sections that should be reinforced.  This way, extra classes or reteaching and intervention plans only need to cover those areas that they are weak in and this saves resources and time for both the students and teachers.

Data will only be useful if principals and teachers are trained on data collection methods and data analysis, which inevitably involves some statistical knowledge.  Unfortunately, neither the principal nor the teachers’ preparation programmes include this in their training programmes, as a result it becomes the responsibility of the principals to provide the training so that teachers are data literate.  It becomes the principal’s responsibility, but it could be a good thing, as the principal himself/herself would know exactly what issues need to be addressed in the school. 

There is an increasing awareness of the importance and benefits of using data as accountability policies require that teachers and students’ performance are measured by test scores.  It is the principal’s duty to initiate, train and enforce the use of data to inform on both instructional and pedagogical practices.  This way, intervention plans can be implemented with confidence knowing what is prescribed is correct for the problem.  It is important to note that successful data use is less about technology and more about integrating it into the everyday practices of schools.

Reference

Mandinack, E. B. & Schildkamp, K. (2020). Misconceptions about data-based decision making in education: An exploration of the literature. Studies in Educational Evaluation. Available at https://doi.org/10.1016/j.stueduc.2020.100842

Schildkamp, K. (2019). Data-based decision-making for school improvement: Research insights and gaps. Educational Research, 61(3), 257-273. Doi: 10.1080/00131881.2019.1625716 Available at : https://doi.org/10.1080/00131881.2019.1625716

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