SBIR Phase I: Mapping a Pathway to College Using Predictive Analytic Modeling and Decision Support

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The SBIR Phase I project supports six months of R/R & D to develop a goal-setting and planning software tool for students from 5th to 12th grade and their families, not unlike current tools for financial planning or trip planning. Students and families will benefit from the power of prediction-based goal setting as well as an early warning system that indicates when students are off track of their education goal, and evidence-based program suggestions to get students back on track of their goal. Navigating the education system in the U.S. is complex, even more so for first-generation college seekers and new immigrant students and families. Between 9th grade and the first year of college, 50% of America's students fall off the path toward higher education even as research shows that students cannot make a living wage without a post-secondary diploma. Today, nearly every U.S. school offers an electronic grade book and a parent portal that shares student performance data with students and their families. This SBIR-sponsored tool would integrate seamlessly with these existing portals to offer added-value features to a system that has not changed in over 15 years. With notoriously poor counselor-to-students ratio of 471:1, and with the uneven distribution of access to supports (such as private counseling services for those who can afford it), technology such as this may very well serve as a leveling instrument in U.S. schools. SBIR support provides the opportunity for technology transfer of what we know about financial planning and business practices to inform education planning and goal-setting based purely on data analytics and best practices.This SBIR-sponsored innovation harnesses the advances of business intelligence and decision support intelligence and applies it to education. This is a new-to-the-market tool with graphic displays of data, progress mapping, predictive analytics, research-based and customized interventions associated with improved student outcomes, all within parent portal systems that are widely used across the U.S. This web and mobile-enabled tool monitors performance and progress, not unlike what the mobile wrist band devices that offer health enthusiasts today. After initial 6 months of funding, the project will have determined proper alignment of data sets for analytic purposes, will have developed predictive analytic models using de-identified student data, will have developed a data visualization dashboard that showcases the power of this forward-looking tool on school web-based platforms, and will have conducted feedback sessions with customers on their interest level, ease of use, and accuracy of predictive outcomes. The methods used will include multiple regression, logistic regression and descriptive analysis. Prediction accuracy will be evaluated by logistic regression and ROC curve analysis.