20 min listen
Predicting Risk in Social Service and Education Programs
FromEvidence First
ratings:
Length:
9 minutes
Released:
May 15, 2018
Format:
Podcast episode
Description
Social service and education programs aim to help the people they serve achieve positive outcomes (for example, completing a degree or getting a job). But some participants still don’t succeed. Could predicting who is more at risk of not meeting important milestones allow programs to intervene with supports for those who most need them? Predictive analytics is a tool that can help programs use existing data to make predictions of risk for their clients. Program staff can identify milestones, which, if not met, can prompt action. For example, if a child is not reading at grade level by grade 3, school staff can provide additional supports to help avoid unwanted future outcomes, such as failing or dropping out. Join Katie Beal as she talks to Rekha Balu, Director of MDRC’s Center for Applied Behavioral Science, who describes how predictive analytics is informing MDRC’s work, and to Brad Dudding, Chief Operating Officer at the Center for Employment Opportunities (CEO), who explains how CEO is using predictive analytics to help formerly incarcerated individuals gain employment and reduce recidivism.
Released:
May 15, 2018
Format:
Podcast episode
Titles in the series (57)
Gordon Berlin Interviewed About the Role of Research Evidence in Shaping Social Policy: In April, MDRC President Gordon Berlin was interviewed by Denver Frederick, host of the “Business of Giving” radio show in New York City, about the role of rigorous research evidence in informing how government and philanthropy invest in education... by Evidence First