G7_JAPAN

ADVOCACY
EDUCATION INTERNATIONAL
Race to the bottom:
big data and teacher
evaluation frameworks
Education as a public good currently faces
serious challenges on a global scale.
National tax regimes are struggling with
– or exploiting – elements of economic
globalisation such as aggressive tax
planning by large corporations and rampant tax
avoidance by the rich and powerful. It is unfortunate
and seems all too predictable that with the resulting
shift from a tax state towards a debt state, public
goods such as quality education are increasingly
viewed as unaffordable across the globe.
This state of affairs raises the stakes for decisionmakers
in terms of how tax revenues are spent on
education. What areas are to be prioritised and for
what reasons?
All too often, money from the public purse is
spent on activities that are ineffective. Take the
example of accommodating big data analysis within
the education sector. Today, there is a burgeoning
global industry profiting from the technical allure
that comes with offering products and services
concerned with big data analysis. It is clear that the
use of big data could help improve education. At
the same time, it is possible to get carried away by the
number-crunching capacities of giant databases.
Missing the bigger picture
The case of Value-Added Modelling (VAM) in the
United States shows how tax revenues are easily
misdirected when new technology-based tools
are put on the market. VAM is used to measure a
teacher’s performance, in order to calculate pay, in
a particular school year against a bank of student
data on aggregated averages of movement that
may or may not be based on the actual students
that are being taught. Its use in teacher evaluation
frameworks has exploded across US states recently
in ways that tend to be misguided from a research as
well as economic and societal perspective. Although
the US is the hotspot for VAM excess, debate on the
tool is going on elsewhere, such as in England, the
Netherlands, Spain, and Chile.
In the US, VAM tends to be used in ways that are
deeply flawed from a research point of view. VAM
has been incorporated in evaluation frameworks that
Dogs chasing a lure at a race – just as chasing after ineffective approaches leads
to money being wasted that could be invested in students, teachers and schools
arbitrarily punish or reward teachers and schools
without pointing out areas of potential improvement.
As a result, numerous court cases have been taken
by teachers and their unions across the US. Both the
American Educational Research Association and
the American Statistical Association have issued
authoritative statements warning that VAM has
been used far too widely.
Moreover, the current uses of VAM for teacher
evaluation in the US do not promote system
improvement. They skew performance monitoring
towards identifying the “good” and “bad” teachers
and school leaders, rather than assessing the impact
of policies on system performance. VAM’s narrow
focus distracts from the fact that the most serious
challenges to ensuring educational opportunities
are related to poverty and disadvantage, issues
beyond the control of schools and teachers.
Rather than putting too much trust in numbers,
policy design needs to include school leaders and
teachers in the debate and concrete measures to
improve education as a public good. Otherwise,
resources are simply wasted. The uses of VAM in the
US are likely to further corrode public trust in education
and become part of a broader trend that makes
education defensive rather than bold and innovative.
© PA/EMPICS/MARK LEES
TORE BERNT
SORENSEN
Centre for
Globalisation,
Education &
Social Futures,
University
of Bristol
www.ei-ie.org