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Sorensen, Lucy C. – Educational Administration Quarterly, 2019
Purpose: In an era of unprecedented student measurement and emphasis on data-driven educational decision making, the full potential for using data to target resources to students has yet to be realized. This study explores the utility of machine-learning techniques with large-scale administrative data to identify student dropout risk. Research…
Descriptors: At Risk Students, Dropouts, Data Collection, Data Analysis
Carmo, Mafalda, Ed. – Online Submission, 2017
This book contains a compilation of papers presented at the International Conference on Education and New Developments (END 2017), organized by the World Institute for Advanced Research and Science (W.I.A.R.S.). Education, in our contemporary world, is a right since we are born. Every experience has a formative effect on the constitution of the…
Descriptors: Educational Quality, Models, Vocational Education, Outcomes of Education