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Download full textLuan, Jing; Zhao, Chun-Mei; Hayek, John C. – Association for Institutional Research (NJ1), 2009
Data mining provides both systematic and systemic ways to detect patterns of student engagement among students at hundreds of institutions. Using traditional statistical techniques alone, the task would be significantly difficult--if not impossible--considering the size and complexity in both data and analytical approaches necessary for this…
Descriptors: Higher Education, Institutional Research, Data Collection, Institutional Mission
Peer reviewed
Direct linkZhao, Chun-Mei; Luan, Jing – New Directions for Institutional Research, 2006
The authors provide an overview of data mining, giving special attention to the relationship between data mining and statistics to unravel some misunderstandings about the two techniques. (Contains 1 figure.)
Descriptors: Statistics, Enrollment, Data Collection, Data Analysis
Luan, Jing; Zhao, Chun-Mei – New Directions for Institutional Research, 2006
As a tour de force, data mining is likely to gain wider use in the next few years. To facilitate this transition, we make several recommendations addressed to both institutional research professionals and the Association of Institutional Research.
Descriptors: Institutional Research, Enrollment Management, Educational Research, Data Analysis
Voorhees, Richard A.; Smith, Gregory P.; Luan, Jing – Journal of Applied Research in the Community College, 2006
This article sketches a research agenda for the further study of community college student transitions. Specific techniques are depicted as are potential data sources that can be used to pursue that agenda. The role of student tracking systems in transition research is discussed as well as the applicability of national surveys to the study of…
Descriptors: National Surveys, Researchers, Institutional Research, Community Colleges
