Jenzabar Retention
Through its custom-built, patent-pending predictive modeling techniques, Jenzabar Retention, (formerly known as FinishLine) helps to pinpoint at-risk students quickly and offer them the necessary resources to attain success.
As with Course Signals, the product includes early alerts based on a predictive model. However, this model is customizable for each institution, based on its own experiences.
With Jenzabar Retention, when a risk factor appears on a student’s profile, relevant administrators are notified immediately. Alerts are prompted not only through poor attendance or failing grades, but also by numerous other academic, social, and economic factors from the past five years.
“It’s not an absolute indicator, but it’s a predisposition,” said Burt Rubenstein, vice president of student success solutions at Jenzabar. If the data suggest that students are at high risk, professors can “proactively give them a better advisor or put them into a learning community.”
Every institution has a customized array of indicators that suggest low-, medium-, and high-risk students. Jenzabar works with institutions to understand how administrators define risk. Called “candidate factors,” these behaviors could include anything from poor attendance to skipping payments on bills. Jenzabar takes those candidate factors and runs them against the institution’s historical data to see if the two correlate; if they do, those factors become reputable indicators of low, medium, or high risk.
“We intentionally don’t have a generic model,” said Rubenstein, because every school has its own unique challenges.
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Currently, Jenzabar Retention does not alert students of their personalized risk factor, though the company is researching whether such a feature would benefit users.
When asked if campus leaders are feeling pressure to invest in learning analytics in order to improve student outcomes, Rubenstein didn’t hesitate.
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