Visualizing SSLC results over seven years.

I spent most of the last one month building a dashboard for the SSLC results at the Karnataka Learning Partnership. We released it in beta yesterday and here’s the blog post I wrote detailing how we went about it.

Patterns in examination results are something which we are always interested at the Karnataka Learning Partnership. After the design jam in June 2012, where we tried to understand the SSLC data – it’s content and structure, and visualized performance of Government and Private schools in contrast to each other, we decided to take a step deep and find patterns from the past seven years. Results of this effort is what you find here, in beta.

The Karnataka Secondary Education Examination Board shared the data over the last seven years in a combination of several Microsoft Access Database. It came with very little meta data and Megha did all the hard work of making sense of this and pulled it into a PostgreSQL database. Inconsistencies are everywhere, and the quality always depends on how you handle each exception in isolation. Among other things, we decided to look at three aspects of the data to begin with – performance of Government and Private schools, performance in Mathematics, Kannada and English, and performance of each gender. All three across seven years (from 2004-2005 to 2010-2011) for each district in Karnataka.

One of the important data wrangling that we did this time was to aggregate this data at the district level. The raw data came at the educational district level and unfortunately, we did not have geographic boundary shapes for this classification. What we have instead is the geographic boundary at the political level. We massaged the shapefiles, geocoded the data and converted it to GeoJSON in QGIS. We wrote a bunch of Python scripts to perform the aggregation and generate JSON (JavaScript Object Notation) required for the visualization. Every bit of code that we wrote for this project is on Github.

A dashboard of this sort is something which we have never attempted, and honestly it took a while for us to get around it. I had tried D3.js sometime last year and found it to be amazing. D3 is Data Driven Documents, a brilliant JavaScript library to make infographics on the web driven completely by the data. What makes D3.js awesome for me is that everything is an SVG (Scalable Vector Graphic), and there are barely any limits to the representation and interaction that you can bring into the browser with it. I’ve had good experiences with Twitter’s Bootstrap to quickly design and be consistent on the page layout and aesthetics. There are some issues while you work with D3.js and Bootstrap together, especially the way bootstrap manages events. The best way is to trust D3.js and use Bootstrap features of scaffolding and layout.

We found few interesting facts from this exercise. As you may guess, private schools perform better than government schools consistently. Western districts like Udupi, Uttar Kannada and Belgaum performs better than rest of the state. North Karnataka, especially Bidar performs terribly across the last seven years. Something which we are very curious to know why. Bangalore Rural performs better than Bangalore Urban. Government schools does much better and comparable to private schools in Bangalore Rural than Bangalore Urban. Private schools grab the cap in all the three subjects across the last seven years. Girls performs way better than boys in private schools consistently across seven years in every district. Boys does a better job in Bangalore Urban while girls dominate in Bangalore Rural.

This research will continue while we churn few more aspects from the data as the dashboard gets out of beta.

 

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