New Student Intake Survey

Predicting student success, and applying appropriate interventions is a difficult problem that requires a large amount of data, a diverse set of data, and timely predictions. Typical sources of data for student success predictions include Student Information Systems (SIS), Learning Management Systems (LMS), and data collected during the application process. Whereas LMS data has features that could be indicative of student engagement, SIS and application data elements are mostly demographic. Neither of these datasets include information directly related to psychosocial and skills factors relevant to student success. This presentation will demonstrate that utilizing a New Student Intake Survey not only increases the data context around a student, but it also allows predicting student success and retention very early in the semester, even for new students. The better data context is achieved through understanding the underlying factors that may be affecting student success at that point in time; think COVID and all the disruptions it caused for students this year. We demonstrate that with the use of a tailored survey, we can make better predictions of student success, and intervene for situations that are not easily found using demographic data.