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Direct linkSorensen, 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
Puustinen, Minna; Bernicot, Josie; Bert-Erboul, Alain – Learning and Instruction, 2011
The present study regarded the self-regulated vs. not-self-regulated function and the indirect vs. direct (i.e., polite vs. impolite) linguistic form of middle school students' requests for help. Natural data (149 requests were sent via an online homework-help forum by French-speaking seventh to ninth graders) was used. Nearly 60% of the requests…
Descriptors: Homework, Speech Communication, Grade 9, French
Download full textLynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis
