Jan 11th 2016

Being proactive with Big Data/Data Analytics Solutions – Learning Analytics, Behavioral Analytics, Academic Analytics

Blog by Chandrashekhar Vyas, Ed-Tech Solutions, Diaspark

“What information to be collected and how”

Let us get educated in what is the misunderstanding in collecting the right data and the solution. This is quite descriptive problem statement within the Ed-tech space and the only answer is Big Data and Advance Analytics (Behavioral, Descriptive, Predictive, Qualitative, Quantitative) solution. There is a desperate need to improve student outcome but also make every student’s career –a success. We cannot ignore the Impact versus ROI – so you can maximize or control resource allocation as needed. I would not even talk about Organization internal acceptance – Resistance in order to adapt new technologies or collecting external data or a very fundamental challenge – sharing and connecting school data with universities. All this  sums up when we want to achieve more and more with decreasing budgets.

“Graduation Rate to be increased 10% in 5 years” How?

Like every business problem this is quite straight forward too – Be more Responsive and Agile than Reactive ..Be Proactive!!. Well, what will it take to do so is to first collect the data – lots of data. We will talk about Veracity of the data later. Thanks to the economical cloud computing – so provision storage for lots of data that we are planning to collect. One important medium to provide us data is the “student” – so let us not shy away with facilitating students with latest and greatest software applications, environment, access and technology. Yup we will need Data Analytics or Big Data solutions–  because we would then need to make sense out of Personalized Learning Analytics.

Actionable Insights do not come with just collecting lot of data but also measuring a student’s behavior ( Behavioral Analytics). Before we move forward, let us also touch on a very sensitive aspect – Social Media information may be difficult to collect but critical.  But don’t forget to inform the “student” what is being collected and why – this is governance and also one of the fundamentals about “Clean Data” that we are collecting. So let us categorize how this lot of data can be useful:

- Analyze system-wide student information
- Gain insights from grades, attendance, and demographics
- Measure combined and individual efficacy rates of faculty and programs
- Evaluate testing results in aggregate and individual levels

“Let us also make the counsellors job a little easy!!”

The veracity in the small or big data collection is directly correlated to the business impact of the Big Data or Data Analytics Strategy for a business in the Ed-tech space. With the advance Regression models or use of sophisticated statistical frameworks like “R”  we should be able to answer and deliver.

At Diaspark we can help you achieve below from the data you collecting and using Academic Analytics that we offer:

- Visualize complex datasets to make them easily accessible to everyone
- Improve tailored teaching and help support for student and curriculum needs
- Transform classes, programs, and staffing based on insights
- Decrease student dropouts by predicting student issues

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