One thing that distinguishes schools in the United States from other schools around the world is how data walls (which typically reflect standardised test results) decorate hallways and teacher lounges. Green, yellow and red colours indicate the levels of performance of students and classes.
For serious reformers, this is the type of transparency that reveals more data about schools, and is seen as part of the solution to conducting effective school improvement.
These data sets, however, don’t often spark insight about teaching and learning in classrooms: they are based on analytics and statistics, not on the emotions and relationships that drive learning in schools. They also report outputs and outcomes, not the impact of learning on the lives and minds of the learners.
If you are a leader of any modern education system you probably care a lot about collecting, analysing, storing, and communicating massive amounts of information about your schools, teachers, and students based on these data sets. This information is “Big Data”, a term that first appeared around 2000, which refers to data sets that are so large and complex that processing them by conventional data processing applications isn’t possible.
Two decades ago, the type of data that education management systems processed were input factors of education systems such as student enrolments, teacher characteristics, or education expenditures handled by an education department’s statistical officer. Today, however, Big Data covers a range of indicators about teaching and learning processes and, increasingly, reports on student achievement trends over time.
Among the best known datasets today is the OECD’s Program for International Student Assessment (PISA) which measures reading, mathematical and scientific literacy of 15-year-olds around the world. The OECD now also administers an Education GPS, or a global positioning system, that aims to tell policymakers where their education systems are placed in a global grid and how to move to desired destinations. The OECD has clearly become a world leader in the Big Data movement in education.
Despite all this new information and the benefits that come with it, there are clear handicaps to how Big Data has been used in educational reforms. In fact, pundits and policymakers often forget that Big Data, at best, only reveals correlations between variables in education, not causality.
We believe that it is becoming evident that Big Data alone won’t be able to fix education systems. Decision-makers need to gain a better understanding of what good teaching is and how it leads to better learning in schools. This is where information about details, relationships and narratives in schools becomes important.
These are what Martin Lindstrom calls Small Data: small clues that uncover huge trends. In education, these small clues are often hidden in the invisible fabric of schools. Understanding this fabric must become a priority for improving education.
To be sure, there is not one right way to gather Small Data in education. Perhaps the most important next step is to realise the limitations of current Big Data-driven policies and practices. This is an example of what Small Data looks like in practice:
1. Reduced census-based national student assessments to the necessary minimum and transferred saved resources – to enhance the quality of formative assessments in schools and teacher education on other alternative assessment methods. Evidence shows that formative and other school-based assessments are much more likely to improve quality of education than conventional standardised tests.
2. Strengthened collective autonomy of schools – by giving teachers more independence from bureaucracy and investing in teamwork in schools. This would enhance social capital that is proved to be critical to building trust within education and enhancing student learning.
3. Empowered students – by involving them in assessing and reflecting their own learning and then incorporating that information into collective human judgment about teaching and learning (supported by national Big Data). Because there are different ways students can 'be smart' in schools, no one way of measuring student achievement will reveal success. Students’ voices about their own growth may be those tiny clues that can uncover important trends of improving learning.
Big Data has certainly proved useful for global education reform by informing us about correlations that occurred in the past. But to improve teaching and learning, it behoves reformers to pay more attention to Small Data – to the diversity and beauty that exists in every classroom – and the causation they reveal in the present. If we don’t start leading through Small Data we might find out soon enough that we are being led by Big Data and spurious correlations.
Finnish educator, author and scholar Pasi Sahlberg is one of the world’s leading experts on school reform.
Jonathan Hasak, based in Boston, is working to change public policies to better support youth who are disconnected from the labour market and disengaged from school.
This article was first published in the Washington Post on May 9, 2016. Read this article in full in JPL, the Journal of Professional Learning, here. Reading JPL is counted as Teacher Identified professional learning hours for Maintenance of Accreditation.
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