Education system is highly complex. Small data can fix gaps. But is that enough?
Last year I got a fantastic
opportunity to visit the United Nations Headquarters in NYC. As I visited the different chambers where many
resolutions have been passed over the years, the budding policy wonk in me was
hungry to learn more. UNICEF’s “School-in-a-box” kit which is used as a
back-to-school operation around the world got me thinking about the education
system and about the different variables used to quantify quality education in
developing countries.
Big Data in educational reform
and its impact
In an article in The Washington
Post, “’Big Data’ was supposed to fix
education. It didn’t. It’s time for ‘small data’”, the author talks about
how Big Data has only helped reveal correlations between the various factors
and not causality. Thus systemic improvements in the education system by using
this information is not as useful as we may want it to be. Since education
system is complex by nature, small data that captures the essence of learning
and teaching in the classroom is what would eventually assist in making more
effective policies.
Sustainable development goals
(SDG) and Quality education
The 2030 Agenda created by UN has
17 goals. The 4th goal being Quality Education has set most
developing countries on the right path to incrementally achieve this goal.
Various programs undertaken by Global Partnership for Education has resulted in
an increase of youth literacy from 71% in 2000-05 to 75% in 2008-13. World
Bank’s SABER ( Systems
Approach to Better Education Results) program helps identify where
individual countries need most interventions by collecting data on educational
institutions and policies. Knowledge integration of data collected by these
programs will be challenging, but will prove to be powerful in gauging the
extent of improvement of these systems over the years. It would also help in
identifying the crucial indicators for building a robust educational system.
SDG Target: “Equitable and quality primary and secondary education
leading to effective learning outcomes”
In order to reach one of the
targets set by the UN, it is vital to have quality data in order to make
effective decisions to enforce the best practices thus improving the system.
But how does one decide the metrics for this data? Various studies carried out
by US Department of Education suggest that Teacher Assignment, Teacher
Experience, Teacher Academic Skills, Class size, Technology, Academic
Development, Goals, School Leadership among many others are used as quality
measures. Another paper published by National Center for Education Statistics
explains about using measures like Graduation rates, Proportion of students
graduated without delay, drop-out rates, % of unemployed and so on. While using
all these parameters may be one way to measure the quality, are we missing out
on data points that may be unique to communities? Is there a danger of way too
much generalization through the above mentioned metrics that cloud our
abilities to understand the true underlying ground realities? We all do agree
that we cannot compare the education systems of developing countries with those
of the developed countries. Then is there more we can do in terms of quality
data collection and additional metrics we can use specifically for developing
countries?
Pulse of Education system in
Developing countries
“Learning Unleashed” an Economist article mentions that the number
of private schools have increased substantially over the years in developing
nations. A large section of the society has preferred private schools over
public schools due to poor infrastructure, quality of education in public
schools. Lack of competition have left these public schools high and dry with
no incentive for improvement. So it may be meaningful to identify metrics that
measure competition in our data collection that contributes in an indirect yet
significant way to quality education.
Along with variables that intend
to capture the qualitative information about the internal school environment,
we must also pay attention to the external environment. One can measure
competition by geo-spatially mapping different schools in the surroundings,
identifying if they’re private or public, funding for the schools and cost
incurred per student.
Studies have shown that there is
a strong correlation between quality education and diversity in schools. In
most developing countries, public schools are mostly attended by children that
belong to low-income groups and private schools by the higher income groups.
Although, most schools now try to include children from all strata of the
society, the students do not feel socially included leading to below average
performance. Thus “socially-inclusive” variable is an important one in better
understanding the quality of education.
Data about the surrounding physical
condition of the school is a crucial factor too. Cleanliness and safety must be
captured with the rest of the data points.
Changing reforms with changing
times
While quality data collection has
an undeniable role in making a positive impact in this field, suitable policies
and reforms must be enforced making use of the inferences obtained from the
collected data. Since private schools are preferred, it may be a good move to
incentivize the private sector to invest in public schools. Developing countries
like India have created a barrier to market entry by making land ownership a
pre-requisite for building a school. Thus entrepreneurs with low capital with a
drive to build schools lose opportunities.
It may also be a good move to
allocate precise and limited number of responsibilities to primary school
teachers with specific targets. For example, by ensuring a primary school
teacher concentrates on improving only the reading skills of the students, it
will be easy to keep track of the progress.
There’s a long way to go before
the education system gets fixed for good. Good quality data, data analysis
married to timely policy reforms can accelerate this process. Data alone is no
silver bullet!
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