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Kelli A. Bird; Benjamin L. Castleman; Zachary Mabel; Yifeng Song – Annenberg Institute for School Reform at Brown University, 2021
Colleges have increasingly turned to predictive analytics to target at-risk students for additional support. Most of the predictive analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack of transparency by systematically comparing two…
Descriptors: At Risk Students, Higher Education, Predictive Measurement, Models
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Pelanek, Radek – Journal of Educational Data Mining, 2015
Researchers use many different metrics for evaluation of performance of student models. The aim of this paper is to provide an overview of commonly used metrics, to discuss properties, advantages, and disadvantages of different metrics, to summarize current practice in educational data mining, and to provide guidance for evaluation of student…
Descriptors: Models, Data Analysis, Data Processing, Evaluation Criteria
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Bahi, Saïd; Higgins, Devin; Staley, Patrick – International Journal of Science and Mathematics Education, 2015
Individual level data for the entire cohort of undergraduate mathematics students of a relatively small US public university was used to estimate the risk that a student will switch major to another one before degree completion. The data set covers the period from 1999 to 2006. Survival tables and logistic models were estimated and used to discuss…
Descriptors: Academic Persistence, Undergraduate Students, College Mathematics, Majors (Students)
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Bozick, Robert; Gonzalez, Gabriella; Engberg, John – Journal of Student Financial Aid, 2015
The Pittsburgh Promise is a scholarship program that provides $5,000 per year toward college tuition for public high school graduates in Pittsburgh, Pennsylvania who earned a 2.5 GPA and a 90% attendance record. This study used a difference-in-difference design to assess whether the introduction of the Promise scholarship program directly…
Descriptors: Merit Scholarships, College Bound Students, Enrollment Influences, Enrollment Management
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Jenkins, Jade Marcus; Farkas, George; Duncan, Greg J.; Burchinal, Margaret; Vandell, Deborah Lowe – Educational Evaluation and Policy Analysis, 2016
As policymakers contemplate expanding preschool opportunities for low-income children, one possibility is to fund 2, rather than 1 year of Head Start for children at ages 3 and 4. Another option is to offer 1 year of Head Start followed by 1 year of pre-K. We ask which of these options is more effective. We use data from the Oklahoma pre-K study…
Descriptors: Kindergarten, Early Childhood Education, Preschool Education, Data Analysis
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Balcazar, Fabricio E.; Oberoi, Ashmeet K.; Suarez-Balcazar, Yolanda; Alvarado, Francisco – Rehabilitation Research, Policy, and Education, 2012
A review of vocational rehabilitation (VR) data from a Midwestern state was conducted to identify predictors of rehabilitation outcomes for African American consumers. The database included 37,404 African Americans who were referred or self-referred over a period of five years. Logistic regression analysis indicated that except for age and…
Descriptors: Vocational Rehabilitation, African Americans, State Agencies, Probability