Using pupil data to support teaching

A report from the Institute of Education Sciences has found that an intensive approach to providing support for using pupil data to inform teaching did not improve pupil achievement, perhaps because the approach did not change teachers’ use of data or their reported classroom practices.

For the study, researchers recruited 102 elementary (primary) schools from 12 US districts. Schools were randomly assigned to either a treatment or control group. Treatment schools received funding for a half-time data coach of their choosing, as well as intensive professional development for coaches and school leaders on helping teachers use pupil data to inform their teaching. The control schools received no additional funding for a data coach or professional development. Impacts on teacher and pupil outcomes were measured after an  18-month implementation period.

The results suggest that despite the additional resources, teachers in the treatment schools did not increase how often they used data or change their teaching practices in response to that data. Similar percentages of teachers in treatment and control schools reported data-related activities, such as analysing data to understand pupil needs. The intervention also had no effect on pupil achievement. On average, pupils in treatment and control schools had similar achievement in maths and English.          

Source: Evaluation of support for using student data to inform teachers’ instruction (September 2019), Institute of Education Sciences, US Department of Education. NCEE 2019-4008 

Suggestions for more effectively using student data

The Data Quality Campaign (DQC), a US organisation advocating that families and educators receive timely educational data to increase student achievement, has released a new report, Time to Act: Making Data Work for Students, which offers suggestions to policymakers on how data systems can be used more effectively. Primarily, they encourage states to shift their focus from building data systems to using data to determine and meet each student’s individual academic needs.
The DQC worked with educational leaders to determine four policy priorities that they deemed most critical:

  • Measure what matters: Have clear achievement goals and use data to ensure that each student is on track to achieve them.
  • Make data use possible: Provide teachers with data-use training and support.
  • Be transparent and earn trust: Make sure all districts know how their students are doing, why data is important, and how it’s being used.
  • Guarantee access and protect privacy: Give parents and teachers timely data that is kept private.

Each policy priority is followed by specific recommendations, successful state examples, and DQC resources for further information.

Source: Time to Act: Making Data Work for Students (2016), Data Quality Campaign.

The effects of data-based decision making

The use of data to inform educational decisions is becoming increasingly popular worldwide. An article in the most recent American Educational Research Journal describes the effect of a two-year schoolwide data-based decision-making intervention, called Focus, on student achievement.

Focus trains schoolwide teams of teachers and administrators to use data to guide their teaching using a protocol developed at the University of Twente in the Netherlands. Staff receive seven training meetings in year 1 and four training meetings in year 2, and are provided with documents and planning aids to help them track student data and progress.

Fifty-three primary schools (1,193 staff members) in the Netherlands used Focus to apply student achievement data to guide instruction during a two-year study. All schools (n=53) were trained to use data-based decision-making in mathematics during years 1 and 2, and had the option to also use it in spelling lessons in year 2 (n=38). Student achievement data from standardised maths tests given twice a year were collected for children aged 6-12 for two years before implementing Focus and then for two years during the intervention. Results showed benefits of the intervention equal to an extra month of schooling and were most statistically significant for students from low socio-economic backgrounds.

Source: Assessing the Effects of a School-Wide Data-Based Decision-Making Intervention on Student Achievement Growth in Primary Schools (2016), American Educational Research Journal.

Do you live in a “geek city”?

Geek Cities: How Smarter Use of Data and Evidence Can Improve Lives”, a paper from Results for America and The Bridgespan Group, highlights some of the top cities using evidence and data to get better results for citizens. The paper includes information on how cities like London, Baltimore, and Denver are measuring what works, building the evidence base, investing in what works, and budgeting for what works. Based on lessons observed in these cities, the authors make recommendations for city leaders, as well as for federal, state, and philanthropic partners, who want to increase the use of data and evidence to “spur urban innovation”.

Source: Geek Cities: How Smarter Use of Data and Evidence Can Improve Lives (2013), The Bridgespan Group.

Translating effect sizes for practitioners and policy makers

This report from the Institute of Education Sciences in the US is targeted at researchers who conduct and report on education intervention studies. It aims to help researchers present statistics in ways that allow their size and practical significance to be more readily understood by practitioners, policy makers, and other researchers. Three key issues are addressed:

  • Inappropriate and misleading presentation of the size of intervention effects;
  • Representing effects descriptively; and
  • Assessing the practical significance of intervention effects;

Source: Translating the statistical representation of the effects of education interventions into more readily interpretable forms (2012), Institute of Education Sciences