How Big Data Analytics Can Benefit the Education Sector

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Over the past few months at Neal Analytics, we’ve been working closely with both Microsoft and government officials to utilize Big Data and advanced analytics in order to contribute toward one of the most important and impactful areas of our society: education.

Education is fundamental to a student’s long term success. It is self-evident that strong educational programs are intrinsic to the well-being of any community, and government institutions at every level acknowledge that reality. This is why it is so disheartening that as of today, high school dropout rates remain at 7% with some demographics climbing as high as 12%. The statistics for higher education are even more disturbing: 41% of students who set out to achieve a degree in 2007 failed to achieve that degree by 2013, six years later. This is doubly harmful; not only does the student in question lose out on the future income that a degree could generate, but they must also accept the sunk cost of that education, burdening themselves with debt.

For the larger community, lack of education is just as much a tragedy. In terms of wasted income and lost economic production, the cost to the individual is considerable. The income gap between community members aged 25-32 with and without college degrees averages out to be $17,500 annually as of 2012. This translates to the difference between a middle class income and borderline poverty levels. The connection between income and health, security, and crime rates is well established, so it becomes obvious that society has a massive incentive to promote and improve all levels of education.

The varied policies and ideas that attempted to grapple with this issue in the past have been met with inconsistent success. The problem is that while the issue itself is glaring, the solutions are not easy; every student’s challenges and circumstances are broad and identifying the solutions is difficult. Sweeping changes may not be necessary and more funding is not a complete solution. It is more effective to instead target, identify, and provide the correct resources: after school programs, guidance counselor training, lunch meal subsidies, or any other number of potential solutions for the specific problems in each unique community and individual within it. This is not only cheaper in the long run, but also much more effective. The problem is how.

Advances in Big Data and Machine Learning programming allow companies like Neal Analytics to parse the accumulated data in school records and therefore identify trends. As the machine’s learning algorithm improves over time, it will be able to more and more accurately identify at-risk students based on statistical indicators, and provide suggested solutions based on circumstances and resource constraints. This provides the ability to make a massive and abstract problem into a tangible, addressable solution.

The program is already in place in several K-12 educational programs, where the graduation rates of their students increased by a statistically significant amount. More and more institutions are taking notice of the potential aid that Big Data analysis can provide their students and are adopting this as a component in their efforts to address dropout rates. Neal Analytics recently released a Solution on Microsoft’s Azure Marketplace to address this issue, and we expect to see a large increase in data analysis and business intelligence techniques adopted in the educational field.

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