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ERIC Number: ED518121
Record Type: Non-Journal
Publication Date: 2011-Apr-9
Pages: 18
Abstractor: As Provided
Data Driven Identification and Selection Algorithms for At-Risk Students Likely to Benefit from High School Academic Support Services
Lacefield, Warren E.; Applegate, E. Brooks; Zeller, Pamela J.; Van Kannel-Ray, Nancy; Carpenter, Shelly
Online Submission, Paper presented at the Annual Meeting of the American Educational Research Association (New Orleans, LA, Apr 8-12, 2011)
This study describes a well-defined data-driven diagnostic identification and selection procedure for choosing students at-risk of academic failure for appropriate academic support services. This algorithmic procedure has been validated both by historical quantitative studies of student precedents and outcomes as well as by current qualitative comparisons with existing school procedures and efforts to accomplish the same goal through committee work and recommendations. Results indicate it is both possible and feasible using readily available school student information system data to identify who appears to be at substantial academic risk, what some of those risks are, and who appears likely to benefit from specific academic support service interventions. (Contains 6 figures and 7 footnotes.)
Publication Type: Reports - Descriptive; Speeches/Meeting Papers
Education Level: Elementary Secondary Education; High Schools; Secondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Michigan