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Of Needles and Haystacks: Building an Accurate Statewide Dropout Early Warning System in Wisconsin

Item

Title
Of Needles and Haystacks: Building an Accurate Statewide Dropout Early Warning System in Wisconsin
Abstract/Description
The state of Wisconsin has one of the highest four-year graduation rates in the nation, but deep disparities among student subgroups remain. To address this the state has created the Wisconsin Dropout Early Warning System (DEWS), a predictive model of student dropout risk for students in grades six through nine. The Wisconsin DEWS is in use statewide and currently provides predictions on the likelihood of graduation for over 225,000 students. DEWS represents a novel statistical learning based approach to the challenge of assessing the risk of non-graduation for students and provides highly accurate predictions for students in the middle grades without expanding beyond mandated administrative data collections. Similar dropout early warning systems are in place in many jurisdictions across the country. Prior research has shown that in many cases the indicators used by such systems do a poor job of balancing the tradeoff between correct classification of likely dropouts and false alarm (Bowers et al., 2013). Building on this work, DEWS uses the receiver-operating characteristic (ROC) metric to identify the best possible set of statistical models for making predictions about individual students. This paper describes the DEWS approach and the software behind it, which leverages the open-source statistical language R (R Core Team, 2013). As a result, DEWS is a flexible series of software modules that can adapt to new data, new algorithms, and new outcome variables to not only predict dropout, but also impute key predictors as well. The design and implementation of each of these modules is described in detail as well as the open-source R package, EWS tools, that serves as the core of DEWS (Knowles, 2014).
Author/creator
Knowles, Jared E.
Date
2015
In publication
Journal of Educational Data Mining
Volume
7
Issue
3
Pages
18-67
Resource type
en Research/Scholarly Media
Resource status/form
en Published Text
Scholarship genre
en Empirical
Language
en
Open access/full-text available
en Yes
Peer reviewed
en Yes
ISSN
2157-2100
DOI
10.5281/zenodo.3554725
URL
Open Access Publisher's Webpage (Zenodo)
Citation
Knowles, J. E. (2015). Of Needles and Haystacks: Building an Accurate Statewide Dropout Early Warning System in Wisconsin. Journal of Educational Data Mining, 7(3), Article 3. https://doi.org/10.5281/zenodo.3554725
Abbreviation
JEDM
Rights
Copyright (c) 2015 JEDM - Journal of Educational Data Mining
Item sets
Handbook Chapter 20 Citations

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