System and Method for Selecting Predictors for a Student Risk Model
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United States of America Patent
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Issued Date -
Jul 3, 2014
app pub date -
Dec 27, 2012
filing date -
Dec 27, 2012
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Abstract
Systems and methods may automatically generate institution-specific, program-specific or course-specific student risk assessment models from an arbitrary set of potential risk predictors. Student data from previously completed courses are collected and used to create a design matrix of predictor values and an outcome vector. The system determines the coefficients for the model using an automated predictor selection method, such as lasso logistic regression. The system uses the model with current student data to assess an outcome probability, such as the risk of a current student from failing or dropping a course. In addition to an overall risk assessment model, component models focused on particular components of risk, such as performance, participation, attendance, timeliness, or student profile, can be generated. The component models may be used along with the overall risk assessment model to help explain the reasons behind the risk assessment.
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