{"id":201428,"date":"2018-02-02T11:56:08","date_gmt":"2018-02-02T16:56:08","guid":{"rendered":"https:\/\/today.uconn.edu\/?p=201428"},"modified":"2023-07-18T12:03:07","modified_gmt":"2023-07-18T16:03:07","slug":"using-student-data-to-predict-and-prevent-high-school-dropouts","status":"publish","type":"post","link":"https:\/\/today.uconn.edu\/2018\/02\/using-student-data-to-predict-and-prevent-high-school-dropouts\/","title":{"rendered":"Using Student Data to Predict and Prevent High School Dropouts"},"content":{"rendered":"

Each year, more than half a million students drop out of high school in the United States.[1]<\/a>\u00a0<\/sup>But what if schools could predict which individuals were most at risk for dropping out \u2014\u00a0and perhaps even take action to prevent such an outcome? As it turns out, such a scenario is closer than ever to becoming a reality.<\/p>\n

In more than 30 states across the nation[2]<\/a><\/sup>\u00a0today, school districts are using what is known as an Early Warning System (EWS) to predict students\u2019 academic milestones and specific student outcomes, including identifying those students who may be most likely to drop out. Connecticut is now on the cusp of joining them, thanks in part to the ongoing efforts of David Alexandro, a doctoral student in the Neag School\u2019s\u00a0measurement, evaluation, and assessment program<\/a>, and his colleagues at the Connecticut State Department of Education (CSDE).<\/p>\n

\u201cState education departments, districts, and schools create early warning systems to improve student learning by addressing a range of outcomes,\u201d says Alexandro, who is working with the CSDE to develop the state\u2019s first EWS to help support Connecticut students in grades 1 through 12. \u201cWithin this framework, one of the most commonly studied outcomes is high school dropout. If a model can do a good job identifying potential dropouts early enough, then schools and districts can provide timelier, targeted supports and interventions to help more students graduate.\u201d<\/p>\n

\u201cIf a model can do a good job identifying potential dropouts early enough, then schools and districts can provide timelier, targeted supports and interventions to help more students graduate.\u201d<\/p>\n

\u2014 David Alexandro,
\nPh.D. candidate, Neag School of Education<\/em><\/p><\/blockquote>\n

Connecticut\u2019s Early Warning System
\n<\/strong>In Connecticut, the EWS under development \u2014\u00a0called the Early Indication Tool (EIT) \u2014\u00a0relies on data that public schools and districts across the state are already required to provide to the Connecticut State Department of Education. Using this data, Connecticut\u2019s EIT, according to Alexandro, will be able to model the probability that, for instance, a student will drop out of school based on a combination of factors, including attendance, behavior, and course performance.
\n<\/strong><\/p>\n

Alexandro, an intern in the CSDE\u2019s Performance Office, joined the project this past May and is building on work started by fellow Neag School doctoral student\u00a0William Est\u00e9par-Garcia<\/a>. Est\u00e9par-Garcia spent about a year extracting data from several CSDE databases and developing a series of models that would predict student achievement for K-12 students.<\/p>\n

\u201cThe EIT plays an essential role in supporting local education agencies in Connecticut\u2019s Every Student Succeeds Act (ESSA) plan,\u201d Alexandro says. \u201cWorking at the CSDE and building the EIT models has helped me to appreciate the potential of early warning systems in education.\u201d<\/p>\n

In fact, developing the EIT has since become the basis of his dissertation, in which he will go beyond traditional EWS approaches by evaluating machine-learning methods to predict student dropout risk and improve early warning systems.<\/p>\n

While traditional EWSs typically designate each student as \u201con-track\u201d or \u201cat-risk,\u201d the EIT will go a step further by identifying a targeted support level for every student. In addition, the EIT is unique in that it will provide a longitudinal view of student data for every student in a school or district \u2014 not only those students who are at risk or in need of targeted support.<\/p>\n

\u201cThe launch of the Early Indication Tool is a huge milestone for education in Connecticut.\u201d<\/p>\n

\u2014\u00a0Ajit Gopalakrishnan,
\n<\/em>Chief performance officer,
\nConnecticut State Department of Education<\/em>
\n<\/em><\/p><\/blockquote>\n

Now that the EIT\u2019s models have been developed and tested at the state level, the tool, for students in grades 1 through 6, is being shared with districts statewide. Over recent months, Alexandro and his colleague Charles Martie, the CSDE education consultant who has been leading the state\u2019s EWS development efforts, have been delivering presentations on the EIT to colleagues across Connecticut. In September, they presented the tool at the Performance Matters Forum, an interactive professional learning experience attended by more than 600 district and school leaders, data managers, and IT staff in Connecticut. In addition, through October and November, Alexandro and Martie presented the tool to education consultants, to CSDE leaders, at regional education research conferences, and during professional development sessions for principals, teachers, literacy coaches, and others.<\/p>\n

\"David
David Alexandro discusses his research on the Early Indication Tool (EIT) at a 2017 Northeastern Educational Research Association poster session. (Photo courtesy of David Alexandro)<\/figcaption><\/figure>\n

\u201cThe Connecticut State Department of Education is thrilled to provide an \u2018early warning system\u2019 tool to all school districts,\u201d says Ajit Gopalakrishnan, CSDE\u2019s chief performance officer. \u201cUConn interns William Estepar-Garcia and David Alexandro worked with Dr. Charles Martie at the CSDE to conduct the requisite research and modeling that have now enabled us to launch our Early Indication Tool. Will and David are highly skilled, competent, and thoughtful professionals who are passionate about the work and\u00a0eager to make a contribution. The launch of the EIT is a huge milestone for education in Connecticut.\u201d<\/p>\n

Meaningful Research
\n<\/strong>Prior to joining the Neag School\u2019s Ph.D. program, Alexandro had studied applied mathematics and engineering and, over the years, served as a management consultant; a programmer; a high school teacher; a volleyball, lacrosse, and basketball coach; and a high school administrator. It was his experiences in the realm of education that ultimately motivated Alexandro, a father of three, \u201cto fully immerse [him]self in applied statistics and research, and work toward earning a Ph.D.\u201d
\n<\/strong><\/p>\n

The opportunity to work on the development and implementation of a successful EWS in Connecticut is one for which he is particularly grateful. \u201cI am thrilled to have returned to my roots as a collaborative learner \u2026 while engaging in productive, interdisciplinary dialogue and meaningful research,\u201d says Alexandro, who is working to complete his Ph.D. in May 2018.<\/p>\n

\u201cI would not be in a position to serve as an expert on this project without the training I received at the Neag School,\u201d he adds, crediting Neag School faculty members\u00a0Suzanne Wilson<\/a>,\u00a0Christopher Rhoads<\/a>, Jane Rogers, Hariharan Swaminathan, and\u00a0Eric Loken<\/a>\u00a0among his numerous mentors. \u201cI bring a piece from each course I have taken in the measurement, evaluation, and assessment program to the EIT.\u201d<\/p>\n

EIT, which is currently being piloted in Connecticut, is scheduled to be fully implemented during the 2018-19 school year.<\/p>\n

 <\/p>\n

Access research reports, implementation guides, and more on Early Warning Systems compiled by the American Institutes for Research at\u00a0<\/em>earlywarningsystems.org<\/em><\/a>.<\/em><\/p>\n

[1]<\/a><\/sup>\u00a0\u00a0https:\/\/www2.ed.gov\/rschstat\/eval\/high-school\/early-warning-systems-brief.pdf<\/em><\/h5>\n
[2]<\/sup><\/a>\u00a0http:\/\/dataqualitycampaign.org\/wp-content\/uploads\/2016\/03\/Supporting-Early-Warning-Systems.pdf<\/em><\/h5>\n","protected":false},"excerpt":{"rendered":"

Each year, more than half a million students drop out of high school in the United States. But what if schools could predict which individuals were most at risk for dropping out \u2014 and perhaps even take action to prevent such an outcome? As it turns out, such a scenario is closer than ever to becoming a reality.<\/p>\n","protected":false},"author":33,"featured_media":201429,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_crdt_document":"","wds_primary_category":0,"wds_primary_series":0,"wds_primary_attribution":0,"footnotes":""},"categories":[2428,2424,1855],"tags":[],"magazine-issues":[],"coauthors":[1878],"class_list":["post-201428","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-educational-psychology","category-neag-community-engagement","category-neag"],"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"publishpress_future_action":{"enabled":false,"date":"2026-05-11 03:58:53","action":"change-status","newStatus":"draft","terms":[],"taxonomy":"category","extraData":[]},"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/posts\/201428","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/users\/33"}],"replies":[{"embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/comments?post=201428"}],"version-history":[{"count":1,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/posts\/201428\/revisions"}],"predecessor-version":[{"id":201430,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/posts\/201428\/revisions\/201430"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/media\/201429"}],"wp:attachment":[{"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/media?parent=201428"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/categories?post=201428"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/tags?post=201428"},{"taxonomy":"magazine-issue","embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/magazine-issues?post=201428"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/today.uconn.edu\/wp-rest\/wp\/v2\/coauthors?post=201428"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}