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An Introduction to Number-crunching

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Now is the best time to be analysing education data. Government educational data sets are more plentiful and contain richer information than ever, and they can be more easily accessed to see just what trends are beginning to emerge.

However, understanding the great range of information which these data sets hold and going on to extract it can be time-consuming. This is why I want to open up the work I’ve done with the data.

Whether or not you or your colleagues are already analysing these data sets, I hope my analyses that I share on this blog will promote some thought-provoking informal discussion. As you read this blog, I would really appreciate any contributions from you regarding the work I have shared, or analyses I could perform in the future. Feel free to contribute whatever you like – criticism, creative suggestions or just your interpretation of what the data is telling us.

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Understanding data can help us learn what we can do to encourage more students to study science, especially physics

Along with their full examination records, students’ personal characteristics recorded in the data include gender, ethnicity and different socio-economic measures. This information exists for every learner who studies in the school, further and higher education systems in England. What’s more, all of this information can be linked, which allows the tracking of learners from their GCSE study on to college or sixth form, and even as far as university.

The great potential to look for correlations between these school- and student-level characteristics and uptake and attainment in STEM subjects motivated the Institute of Physics, Royal Academy of Engineering and Gatsby Foundation to collaborate on a joint data project that would do just that.

Daniel Sandford Smith, Director of Programmes in the Education team at the Gatsby Foundation, thinks it’s important data is used to make informed decisions in education. “I have always been surprised at how small a role evidence plays in shaping science education.

“Data doesn’t necessarily tell us how to make science education better, but if we don’t understand the data it’s unlikely we will ever know if our efforts to improve science education are having any effect.”

Charles Tracy, Head of Education here at the Institute of Physics, explains why a data analyst is key for moving forward in education. “For some time we’ve been able to look at outputs at a school level – both in terms of performance data and behaviours of students.

“For example, we can find the number of schools from which no students chose to go on to study A-level physics. However, these outputs often raise more questions: “What was their route through the sciences to 16?”; “what were the qualifications of their teachers?”; “what subjects did they choose instead?

“In order to get quick and continuous answers to these supplementary questions and also to find out what was feasible with the data, we decided to recruit an in-house data analyst.”

My research as part of the joint data project using educational data is not the only data-driven research that will appear on the data part of the IOP blogging site.

Please look out for future blogs on all manner of physics-related policy issues that have a data-slant from the IOP’s policy team too.

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One thought on “An Introduction to Number-crunching

  1. Will the data allow you to analyse the uptake, and/or performance of pupil premium students taking physics in a future blog? I feel academic resilience is a key factor for boys and girls, and I’m sure parental support, or lack of, is a key factor in developing resilience

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