Share Paper: Detecting Students at Risk: Adventures in AI

  1. Owen Hall, Jr., Pepperdine University, United States

Abstract: Identifying students at academic risk early in the matriculation process, especially online students, is on ongoing challenge. The evidence to date suggests that early invention can have a positive impact on student learning performance. One very telling variable in this regard is freshman performance. Current date suggests that nearly one-half of United States universities are experiences a first-year student attrition rate of 25 percent. Time is often the essence since waiting until midterm exam results to intervene can often prove problematic. In this regard, web-based learning management systems (LMS) provide an effective vehicle for not only modifying content and pace, ...