In a recent post, in response to a study that found New Zealand has one of the most unequal education systems in the world, NZ Initiative Research Fellow Jenesa Jeram says this focus on inequality in relation to educational achievement can obscure the real gains and improvements some schools and students are making.

To more clearly measure any gains in learning in schools, she has in conjunction with the New Zealand Initiative developed a model that controls for a student’s socioeconomic background (and other factors that might contribute to education outcomes) to estimate the value a school adds to its students.

This is an age-old problem in education. If a chemistry class all gets excellences, is that down to the school, the individual Chemistry teacher, the English teacher who taught them how to construct written arguments or the pupils themselves? This does not even consider the influence of parents and the general community.

The standard way to assess if a school is ‘good’ at least in terms of academic achievement is to look at school pass rates and end of year grades of the cohort.

However, another approach involves value-added models of assessment, which measure a student’s progress over the course of a year by comparing achievement at the start of the year with that at the end and seeing how much development has taken place.

This is where the research at The New Zealand Initiative comes in. Through gathering enough information on individual students’ circumstances, they can start to make a prediction on academic success and future outcomes.

The idea behind the model is the comparison of present students to those with similar backgrounds from previous years. This will in theory at least, predict academic success, the chances of further study, or on the negative side, welfare dependency or crime.

However, this use of big data cannot say why certain students or teachers show more improvement, it can only provide correlations, but not the causations. This needs to be up to individual schools and teachers to investigate and determine for themselves – the use of small data, those relationships so critical to teaching.

Take as an example, the data in a school is showing that fewer girls are going on to take maths courses at senior level.

This is where listening comes in. Senior leadership discussing the problem with its teachers and those teachers then discuss it with their students. This communication leads to discovering the problem was how mathematics is perceived or even taught in the school. That the focus on doing of lots of mathematical problems was too theoretical. After discussing with students they wanted a course that was more practical, applying the principles to real-world problems. With this change of emphasis, students took more interest and the number of girls in courses increased.

As a teacher, my decisions should not be based on the students’ backgrounds; that should not determine if the student is going to succeed.

This post originally published here and was republished with the permission of the author.

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