Predictive Analytics Has a Future in Education
In our world of rising costs and shrinking funds, a rising number of colleges are in real financial trouble. Institutes are deep in debt as they’ve borrowed heavily to fix aging infrastructure, keep up with competitors and lure students with state of the art amenities. In an increasingly competitive sector, education  institutes need to develop programs that meet the market need. But what is more critical is finding ways to improve student retention, grades and services to students. To do this efficiently and effectively, educators and administrators need deeper insights into what impacts student completion rates, learning outcomes, and yet fulfills the goals of the institution bringing increased returns. Analytics once again could provide these answers. Yes, as with most organizations everywhere, educational institutes are also collecting huge amounts of data every day. Generated by students, faculty and administration, this data can be analyzed to discover meaningful patterns, and then used to make important operational decisions. Broadly speaking Analytics can help Education institutions: Improve academic success by: Measuring teaching and learning effectiveness Predicting Student performance and  success Personalizing the learning process for each student, playing to their strengths and encouraging improvement Identifying students at risk of leaving the course Improve Institutional efficiency and effectiveness by: Managing financial performance Allocating Resources better Reducing risk An analytics driven educational institute is sure to see results in terms of a more successful and satisfied student and teacher population. However for real results to be seen, it’s important for them to understand and give value to the human interpretation side of analytics. Data, by itself, will not mean anything without valuable human interpretation, insights and action. Contextual data must also be given it’s due for without it, a complete pictures of students’ learning and performance cannot be obtained. The advantages of predictive analytics in education are undoubtedly many. There are numerous examples of success. For example the University of California reduced the cost of risk and saved more than $493 million over four years with the help of IBM Smarter Analytics. Then why is it that we don’t see wider acceptance yet of analytics in the education sector especially in countries like India? Well one of the major barriers is cost. Management does not view analytics as an investment and they wrongly perceive that analytics tools are expensive and will not show sufficient returns. However with open source technology widely available, analytics tools are easier to access and are getting more affordable. The key lies in investing in analytics professionals that can contribute effectively to the entire process. Another concern is privacy and ownership for both students and teachers. The management must take initiative and ensure that ethics are maintained and permissions at all levels sought. And then there is the perception that analytics could override human perception, making decision making too clinical. Well all things considered educational institutions must begin to consider using analytics to improve returns. It could provide them the edge they need to remain profitable. But they must remember to ensure a careful balance. Automation and analytical tools on one side and human gut feeling on the other- let them compliment and add value to each other.
In our world of rising costs and shrinking funds, a rising number of colleges are in real financial trouble. Institutes are deep in debt as they’ve borrowed heavily to fix aging infrastructure, keep up with competitors and lure students with state of the art amenities. In an increasingly competitive sector, education  institutes need to develop […]
In our world of rising costs and shrinking funds, a rising number of colleges are in real financial trouble. Institutes are deep in debt as they’ve
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["Go", "programming_languages:R", "AI", "predictive analytics", "programming_languages:Go", "RAG", "automation", "analytics", "GAN", "R"]
["AI", "analytics", "RAG", "predictive analytics", "R", "Go", "GAN", "automation", "programming_languages:R", "programming_languages:Go"]
Sarita has over 10 years of extensive analytics and consulting experience across diverse domains including retail, health-care and financial services. She has worked in both India and the US, helping clients tackle complex business problems by applying analytical techniques. She has a Master’s degree in Quantitative Economics, from Tufts University, Boston, and a PG Diploma in Management from T.A. Pai Management Institute, Manipal.