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Romero-Zaldivar, Vicente-Arturo; Pardo, Abelardo; Burgos, Daniel; Delgado Kloos, Carlos – Computers & Education, 2012
The interactions that students have with each other, with the instructors, and with educational resources are valuable indicators of the effectiveness of a learning experience. The increasing use of information and communication technology allows these interactions to be recorded so that analytic or mining techniques are used to gain a deeper…
Descriptors: Academic Achievement, Prediction, Learning Experience, Data
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Guruler, Huseyin; Istanbullu, Ayhan; Karahasan, Mehmet – Computers & Education, 2010
Knowledge discovery is a wide ranged process including data mining, which is used to find out meaningful and useful patterns in large amounts of data. In order to explore the factors having impact on the success of university students, knowledge discovery software, called MUSKUP, has been developed and tested on student data. In this system a…
Descriptors: Income, Computer Software, Databases, Data Analysis
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Chen, Chao-hsiu – Computers & Education, 2010
Recently, more and more researchers have been exploring uses of mobile technology that support new instructional strategies. Based on research findings related to peer and self assessment, this study developed a Mobile Assessment Participation System (MAPS) using Personal Digital Assistants (PDAs) as the platform. In addition, the study proposes…
Descriptors: Feedback (Response), Educational Strategies, Education Courses, Self Evaluation (Individuals)
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Macfadyen, Leah P.; Dawson, Shane – Computers & Education, 2010
Earlier studies have suggested that higher education institutions could harness the predictive power of Learning Management System (LMS) data to develop reporting tools that identify at-risk students and allow for more timely pedagogical interventions. This paper confirms and extends this proposition by providing data from an international…
Descriptors: Network Analysis, Academic Achievement, At Risk Students, Prediction