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ERIC Number: EJ1094176
Record Type: Journal
Publication Date: 2016
Pages: 7
Abstractor: ERIC
ISSN: ISSN-0009-1383
Scaling Student Success with Predictive Analytics: Reflections after Four Years in the Data Trenches
Wagner, Ellen; Longanecker, David
Change: The Magazine of Higher Learning, v48 n1 p52-58 2016
The metrics used in the US to track students do not include adults and part-time students. This has led to the development of a massive data initiative--the Predictive Analytics Reporting (PAR) framework--that uses predictive analytics to trace the progress of all types of students in the system. This development has allowed actionable, institution-specific insight about what affects undergraduate student success and failure to member institutions from two- and four-year public, proprietary, traditional, and progressive institutions. The institutions then exchange information about the tools and practices that they have found measurably improve student outcomes. Viewing normalized data through a multi-institutional lens and using a common set of measures with common data definitions has led to insights that are not available when looking at records from a single institution. The development of PAR has helped members eliminate redundant programs, understand the scale of their programs, match interventions with the causes of student academic risk, measure the impact of student-success programs, and respond to budget cuts with informed decisions. PAR has also helped member institutions remedy their local data problems; organize data across silos; uncover gaps, errors, and overlaps in student-data elements; and compare themselves to other campuses.
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Publication Type: Journal Articles; Reports - Descriptive
Education Level: Higher Education; Postsecondary Education; Adult Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A