Big Data in Education

Sometimes you need to start small before going big.  First off, an apology for the picture of the doughnuts, but they express a clear and simple point and helped to inform a recent decision that I made when in an airport (I chose the Chocolate Glaze over the French Cruller and saved 100+ calories).

People make better decisions when presented with data.  That statement is valid for everyone, not just data scientists or mathematicians.  In my opinion, data is best used under the following circumstances when it is:

1. Consistant – allows for an end user to know what they should expect out of the data.  Also allows for easy comparisons across similar data sets. Props to the FDA for doing a great job regulating food labels – I constantly see people stopping in the supermarket and making decisions based upon those labels.  In education (the field that I work in), the companies that hold assessment data (which includes everything from homework assignments to summative assessment) all have unique formats and unique scoring systems making the data difficult to compare across providers and geographic regions.
2. Intuitive – Data needs to be interpreted by all end users.  In education, those end users or stakeholders include everyone from a student or parent all the way up to legislators.  To keep folks concentrated on the task at hand of interpreting data, step one has to be to present it back to them in simple ways that do not require advanced degrees in mathematics to interpret.
3. Readily available / up to date – different systems output data at different times.  Unlike food, whose output is measured once, Educational data is constantly changing (think about how often students get homework!), so keeping up to date with systems that talk to each other.

There is a tremendous amount of buzz around Big Data in every industry, which is slowly making its way over to the Education space.  In my opinion there is a tremendous amount of small data that can be used that will end up empowering stakeholders to improve outcomes.  A great example of the power of using data in the ways that I described above is the Nike Fuel Band, which tracks daily activity and presents the end user with a number or fuel points (a normalization method that accounts for all types of activity from basketball to ballet) that can be compared to others and the users historical fuel points.

Ping me if you are interested in learning more, but as you probably imagine these are the exact types of things that we are working on over at Engrade.

Side note: since I brought up the Fuel band, check out this video, which I got a kick out of:

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