Predicting High School Graduation and College Enrollment: Comparing Early Warning Indicator Data and Teacher Intuition

Abstract

Each year, more districts implement early warning systems (EWS). These EWS predict negative student outcomes, such as dropping out, before they occur. Predictions are then used to match at-risk students to appropriate supports and interventions. Research suggests that these systems are useful in ensuring educators respond to student needs early, generating conversation around specific students at risk of dropping out. However, no research considers what new information teachers gain from having a specific prediction for a student. This article bridges this gap by comparing teacher and EWS predictions of whether students will complete high school and enroll in college. Further, it assesses whether accuracy in teacher judgment stems from additional information not in modelsespecially related to academic tenacity-and biases like self-fulfilling prophecies. Generally, EWS can provide benefits both as organizational tools and by increasing the precision with which students are identified for supports and interventions.Each year, more school districts implement early warning systems (EWS). These systems use data-early warning indicators (EWI)-to predict student outcomes (such as dropping out or failing to attend college) before they occur, and then to develop coherent supports and interventions around those forecasts. The most common use of these predictions is to help districts identify and support students at risk of dropping out or experiencing some other roadblock (Allensworth, 2013;Davis, Herzog, & Legters, 2013;Kemple, Segeritz, & Stephenson, 2013). In particular, research suggests that EWS help generate meaningful conversations around these atrisk students and, more broadly, organize coherent responses to their needs at an earlier stage in their schooling (Allensworth, 2013;Davis et al., 2013).Although research clearly demonstrates that EWS add value by generating conversations about students earlier than they might otherwise occur, few studies consider how, exactly, the data from EWS are useful once these conversations begin. What new information do teachers gain by having an EWS prediction, given what they already know about students from classroom observation and assessment? One could imagine EWS forecasts help teachers because the predictions add precision, perhaps helping identify students as at risk who, otherwise, appear Correspondence should be addressed to

An exploratory study into teachers’ beliefs and experiences about allocating students

“…Teachers' perceptions furthermore indicate great differences in how decisions are made within and between schools. These findings support prior research on how teachers' perceptions of student characteristics influence decisions about placement of students in specific groups (e.g., Soland, 2013;Van Houtte & Demanet, 2016), add to what is known about influencing factors and limitations in group decision-making by teachers, and offer a more detailed picture of how allocation decisions are made in practice (Bol et al, 2014;Elffers, 2012).…”

Seeing Potential

“…They find no student–teacher interaction effect on academic expectations of students. Similarly, using baseline data on eighth-grade students from NELS:88 and a student fixed-effect approach, Soland (2013) does not find evidence that race or sex congruence between students and teachers matters for teachers’ postsecondary expectations for students. Although observational, several studies find that Black teachers are more likely than White teachers to (a) think Black students will enroll in or complete college, (b) be optimistic about Black students’ future academic careers, and (c) see future educational potential in Black students (Beady & Hansell, 1981; DeMeis & Turner, 1978; Ehrenberg et al, 1995).…”

Is Moneyball the next big thing in education?

“…Studies show that teachers are quite good at predicting which students will drop out. In my work (Soland, 2013), 10th-grade teachers accurately predicted which students would drop out 89% of the time compared to 88% for EWI using statistical models. As one might suspect, teachers factored information on student attitudes and behavior into these predictions, which helped account for their accuracy.…”

“…First, teachers may base their predictions on less than objective information. For example, I found that teachers were likelier to predict that Latino and African-American students would drop out (Soland, 2013). Even more troubling, these same teachers were likelier to mistakenly predict that minority students would fail to graduate.…”