Chip and Dan Heath, authors of the best-selling books “Made to Stick” and “Switch,” once said, “Data are just summaries of thousands of stories.” Indeed, but the summaries are not the stories themselves, particularly when it comes to student retention.
The stories are more complex and nuanced. Analyzing data leads to information–a better understanding of the stories. It also can predict what’s to come in the next chapters and sequels ahead. That’s the missing puzzle piece for many higher education institutions using data to help boost retention rates and student support.
Consider this one data point: More than 40 percent of community college students neither complete college nor transfer to a different school. While that data point is a summary of countless student stories, it is just that: a summary; an ending point to more than 40 percent of higher education journeys.
But what if we looked at other data to better understand the stories that brought us to this point? What if we looked at students who dropped out and analyzed their performance over their time in college? What if the information we gleaned could tell us the moment students disconnect with their goals, their coursework, and their classes before they make the decision to leave?
What’s better is that with the knowledge found by understanding what the student data tells us, institutions and staff can intervene and provide necessary and personalized support before it’s too late.
Let’s discuss how exactly analyzing data can increase student retention and student support in higher education:
The power to predict
The role of data when it comes to increasing student retention and student support comes back to one capability: the power to predict. Predictive analytics leverages student data to not only identify students in need of assistance but also to identify the obstacles standing in the way of their success.
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