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Waters, John K. – Campus Technology, 2012
In the case of higher education, the hills are more like mountains of data that "we're accumulating at a ferocious rate," according to Gerry McCartney, CIO of Purdue University (Indiana). "Every higher education institution has this data, but it just sits there like gold in the ground," complains McCartney. Big Data and the new tools people are…
Descriptors: Higher Education, Educational Change, Data, Data Processing
Kagklis, Vasileios; Karatrantou, Anthi; Tantoula, Maria; Panagiotakopoulos, Chris T.; Verykios, Vassilios S. – European Journal of Open, Distance and E-Learning, 2015
Online fora have become not only one of the most popular communication tools in e-learning environments, but also one of the key factors of the learning process, especially in distance learning, as they can provide to the students involved, motivation for collaboration in order to achieve a common goal. The purpose of this study is to analyse data…
Descriptors: Case Studies, Distance Education, Electronic Learning, Foreign Countries
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Huebner, Richard A. – Research in Higher Education Journal, 2013
Educational data mining (EDM) is an emerging discipline that focuses on applying data mining tools and techniques to educationally related data. The discipline focuses on analyzing educational data to develop models for improving learning experiences and improving institutional effectiveness. A literature review on educational data mining topics…
Descriptors: Educational Research, Data Processing, Data Analysis, Organizational Change
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Todnem, Guy R.; Warner, Michael P. – Journal of Staff Development, 1995
This article discusses how to use technology to collect, store, sort, and reformat assessment data on the school improvement process, explaining the use of desktop computers with databases and the use of portable devices in the assessment process. (SM)
Descriptors: Computer Software, Computer Uses in Education, Data Processing, Databases
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Bernhardt, Victoria L. – Educational Leadership, 2003
A primer for schools attempting to analyze the data they collect. Describes ways schools can get a better picture of how to improve learning by gathering, intersecting, and organizing four categories of data more efficiently: (1) demographic data; (2) student-learning data; (3) perceptions data; and (4) school-processes data. (WFA)
Descriptors: Data Analysis, Data Collection, Data Interpretation, Data Processing