1. Expenditures, inputs and outcomes in elementary education: combine data from Accountability Initiative, DISE and ASER on a single platform
ASER data focuses on learning outcomes. The other large data set in the field of elementary education, the District Information System for Education (DISE), contains detailed information on every government and recognized private school in the country. DISE data is also updated annually and available from 2003 onwards (see www.dise.in).
Even though not directly comparable, in many ways DISE and ASER complement each other. DISE data focuses on enrolment and on inputs (numbers of schools, teachers, classrooms, and many more), whereas ASER provides data on outcomes. A third piece concerns government expenditures on elementary education. State level numbers have been analyzed and summarized by Accountability Initiative (http://accountabilityindia.in/) and will be available for this task.
The objective is to design a platform from which state level information on expenditures, inputs and outcomes can be accessed, searched and compared. The suggestion is to begin with a single year (2011-12, which is the most recent year for which DISE data is available and for which a final ASER data set is available). If this is accomplished, the same can be done for previous years as well.
-Choose specific indicators from each data set that together can provide a summary picture of expenditures, inputs, outcomes
-Design a platform that allows the user to:
oSelect state(s) to get the summary picture
oAccess more details on any given block of indicators
oView changes on specific indicators over time.
2. Data visualization: ASER divisional estimates
Some first steps towards visualizing ASER estimates of enrolment and learning at the state level have been taken and can be seen here: http://www.asercentre.org/education/data/india/statistics/level/p/66.html
The next step in this process is to find ways to visualize division level estimates and changes in these over time. A division comprises a group of districts within a state; these may be based on existing administrative divisions or commonly used geographic regions. A set of divisional estimates have been calculated for 19 major states in India for each round of ASER since 2007.
Visualizing divisional estimates is more complex than state level estimates, for two reasons:
-Given the size of the unit, how to display/select these on a map is slightly more complex
-At this level of aggregation, because the sample sizes are much smaller, the precision of the estimate itself becomes important. Therefore, the task is to display not only the estimate itself but also its standard error (i.e. the 95% confidence interval for the estimate).
-Design a system that will provide divisional estimates and the associated standard errors, preferably both visually and in a data table
-User should be able to select divisions and see all estimates
-Comparison over time: both of a given estimate and its standard error.