Building On-Track Indicators for High School Graduation and College Readiness: Evidence from New York City

Kemple, James J.; Segeritz, Micha D.; Stephenson, Nickisha

doi:10.1080/10824669.2013.747945

Cited by 31 publications

(41 citation statements)

References 3 publications

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“…Generally, these inputs are student-level variables from large administrative datasets, in part because these data prove most accessible to school districts. For example, several studies document that grade-point average (GPA), course failures, and attendance strongly forecast whether a student will drop out of school (Allensworth & Easton, 2005;Balfanz & Boccanfuso, 2007;Kemple et al, 2013;Neild, Balfanz, & Herzog, 2007). A student's GPA and attendance, in particular, account for most of the variance in dropout rates, according to several studies (Adelman, 2006;Geiser & Santelices, 2007;Kemple et al, 2013;Noble & Sawyer, 2004).…”

Section: Background and Literature Reviewmentioning

confidence: 98%

“…For example, several studies document that grade-point average (GPA), course failures, and attendance strongly forecast whether a student will drop out of school (Allensworth & Easton, 2005;Balfanz & Boccanfuso, 2007;Kemple et al, 2013;Neild, Balfanz, & Herzog, 2007). A student's GPA and attendance, in particular, account for most of the variance in dropout rates, according to several studies (Adelman, 2006;Geiser & Santelices, 2007;Kemple et al, 2013;Noble & Sawyer, 2004). Although some research has begun to consider whether noncognitive inputs such as student motivation (Dweck & Leggett, 1998) and determination (Duckworth & Seligman, 2005) predict student outcomes, these inputs largely have yet to be incorporated into EWS research.…”

Section: Background and Literature Reviewmentioning

confidence: 98%

“…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).…”

mentioning

confidence: 97%

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

Soland

2013

Journal of Education for Students Placed at Risk (JESPAR)

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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

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Section: Background and Literature Reviewmentioning

confidence: 98%

Section: Background and Literature Reviewmentioning

confidence: 98%

mentioning

confidence: 97%

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

Soland

2013

Journal of Education for Students Placed at Risk (JESPAR)

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“…We show three statistics that we deemed most helpful for assessing predictive accuracy. First, following prior on-track research (e.g., Allensworth & Easton, 2007; Kemple et al, 2013), we report the percentage of students whom the predictor correctly classified as A–G completers or A–G noncompleters. We refer to this percentage as % correct .…”

Section: Resultsmentioning

confidence: 99%

“…Contingency tables have the advantage of being easy to understand and are thus appealing in the context of a school district. We follow the large literature in medicine and epidemiology (e.g., Loog, 2003; Swet, 1988; Vecchio, 1966) as well as the more recent education literature (e.g., Allensworth, 2013; Bowers, Sprott, & Taff, 2013; Kemple et al, 2013) and calculate the predictive accuracy of each of these dichotomous measures for identifying individuals in need of intervention.…”

Section: Methodsmentioning

confidence: 99%

Using Research to Improve College Readiness: A Research Partnership Between the Los Angeles Unified School District and the Los Angeles Education Research Institute

Phillips

Yamashiro

Farrukh

et al. 2015

Journal of Education for Students Placed at Risk (JESPAR)

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The Los Angeles Unified School District (LAUSD) serves a large majority of socioeconomically disadvantaged students who are struggling academically and are underprepared for high school graduation and college. This article describes the partnership between LAUSD and the Los Angeles Education Research Institute, and how this collaboration endeavors to produce accessible and high-quality research to inform pressing problems of practice. The article also presents findings from an ongoing partnership research project analyzing a district policy focused on improving college readiness by aligning high school graduation and college-eligibility requirements. In a cohort that went through high school before the policy became mandatory for all students, less than 1/5 of all students (and 30% of graduates) met the college eligibility criteria. Our findings indicate that academic and behavioral indicators from 8th and 9th grade can help identify for possible intervention students who are not on track to meet these new graduation requirements.

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Re‐envisioning the purpose of early warning systems: Shifting the mindset from student identification to meaningful prediction and intervention

McMahon

Sembiante

2019

Review of Education

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Emphasis in school dropout literature has shifted from exploring wide‐ranging causes of dropping out to soliciting a smaller number of predictive indicators to identify students at increased risk for dropping out. However, much of the past decade's Early Warning research excludes indicators that do not add to the predictive nature of the model even if they might inform intervention. Considering this shift and the relative infancy of Early Warning research, this paper presents a critical systematic review of the literature surrounding dropout and Early Warning System(s) (EWS). Through a focus and evaluation of student‐level indicators, we present an argument of how an effective EWS can bridge the gap between the prediction of dropout events and underlying causes, while providing actionable information for schools to intervene. Finally, we present preliminary data that demonstrate the potential of elementary‐level student‐level indicators as an avenue for shifting the focus of EWS from student identification to meaningful prediction and intervention. The results show how the student‐level indicators explored in this paper can effectively identify students at these grade levels who are on‐ and off‐track to graduation. We discuss the implications of evidence warranting the position that dropping out of school can be predicted as early as the elementary grade levels and its potential to drive future research in this area.

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Building On-Track Indicators for High School Graduation and College Readiness: Evidence from New York City

Cited by 31 publications

References 3 publications

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

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

Using Research to Improve College Readiness: A Research Partnership Between the Los Angeles Unified School District and the Los Angeles Education Research Institute

Re‐envisioning the purpose of early warning systems: Shifting the mindset from student identification to meaningful prediction and intervention

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