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Download full textLacefield, Warren E.; Applegate, E. Brooks; Zeller, Pamela J.; Van Kannel-Ray, Nancy; Carpenter, Shelly – Online Submission, 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…
Descriptors: Academic Failure, At Risk Students, Identification, Information Systems
Peer reviewed
Direct linkEssa, Alfred; Ayad, Hanan – Research in Learning Technology, 2012
The need to educate a competitive workforce is a global problem. In the US, for example, despite billions of dollars spent to improve the educational system, approximately 35% of students never finish high school. The drop rate among some demographic groups is as high as 50-60%. At the college level in the US only 30% of students graduate from…
Descriptors: Artificial Intelligence, Computer Graphics, Computer Interfaces, Statistical Analysis
