Early alert warning systems (abbreviated commonly in the literature as EAWS) are typically described as systems to improve student retention, as across the United States, nearly 30 percent of first year students fail to return for a second year. However, more pertinent to MIT is that EAWS are intended to provide a ‘formal, proactive, feedback system through which students and student-support agents are alerted to early manifestations of poor academic performance’, typically at or before the midpoint of the first semester (Cuseo, 2003). EAWS programs vary across a variety of factors including whether all first-year students are screened vs. subgroup of students who are considered ‘at-risk’ are the major focus, and whether performance across all classes is tracked vs. a specific set of ‘gateway’ classes (i.e. core/introductory math, science courses, etc.). Most (60%) surveyed EAWS systems in the U.S. use a centralized reporting model in which the alert and response is coordinate by a specific unit/team (Simon, 2011).
Students with early struggles may benefit from proactive coaching and mentoring to improve study skills, address potential deficiencies in academic preparedness and/or personal/medical/social concerns which may be adversely impacting academic performance. There is data that EAWS may be particular effective at reducing/eliminating ‘achievement gaps’ for students who are at-risk for leaving STEM disciplines (US Department of Education, 2017).
Redundancy is considered a critical component of effective EAWS, and most experts favor the collection of multiple data points and processes to identify students experiencing difficulties (Kuh et al., 2005).