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Predictive analytics for online education

Research finds that a student’s digital footprint is a better indicator of success in online education than traditional standards such as race, income, and enrollment status. This data is helping instructors provide more personalized support and is closing the gap of success in distance education.

A number of students balance their online education alongside work and family commitments. These factors, along with the “lonely nature” of online education, often contribute to a loss of motivation in continuing with studies. Fortunately, an increasing number of institutions are using predictive analytics to change the culture and experience of online education.

Post-secondary institutions are tracking how much time students spend on assignments, watching lectures, accessing optional resources, and posting on discussion boards. These insights provide a holistic view of entire classes and individual students, allowing instructors to identify at-risk students and provide personalized support.

Strayer University significantly increased online student retention using predictive analytics. By identifying at-risk students and providing them with highly targeted and personal support, the overall number of students considered to be at risk decreased by 17%.

In a traditional class, an instructor is able to identify at-risk students through a number of factors including in-class participation and assessments. The data provided by online student activity provides similar, if not more data, allowing instructors the ability to motivate and support students the moment they show signs of becoming at-risk.

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About the Author: Dustin is a senior account manager with DesignedUX, providing communications and marketing strategy to organizations in education and technology. Dustin is also a part-time faculty member at Centennial College and serves on the board of the Canadian Public Relations Society.

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