STUDENT ENGAGEMENT AND ANALYTICS SYSTEMS AND METHODS WITH MACHINE LEARNING STUDENT BEHAVIORS BASED ON OBJECTIVE MEASURES OF STUDENT ENGAGEMENT

Embodiments leverage wireless data passively collected by access points of a school to correlate a student's behavior with respect to time and place. This data-driven approach can quantify student behaviors and objectively measure and track how students move and interact on campus. For example, if a student accesses a wireless router that is in the same physical location as a class on the student's schedule at the time the class takes place, the correlated time and place quantifies how the student behaves with respect to class attendance. The invention takes the sum of such interactions (e.g., attending classes, studying in the library, etc.) and produces a student engagement score (SES) for each student. The SES is evaluated to determine how student behaviors change over time (e.g., throughout a semester) and whether the student is trending to a low engagement score and thus “at-risk” of dropping out.