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Sabitha, A. Sai; Mehrotra, Deepti; Bansal, Abhay – Education and Information Technologies, 2016
e-Learning industry is rapidly changing and the current learning trends are based on personalized, social and mobile learning, content reusability, cloud-based and talent management. The learning systems have attained a significant growth catering to the needs of a wide range of learners, having different approaches and styles of learning. Objects…
Descriptors: Multivariate Analysis, Electronic Learning, Cognitive Style, Knowledge Management
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Rupp, André A.; Nugent, Rebecca; Nelson, Brian – Journal of Educational Data Mining, 2012
In recent years the educational community has increasingly embraced digital technologies for the purposes of developing alternative learning environments, providing diagnostic feedback, and fostering the development of so-called 21st-century skills. This special issue is dedicated to bridging recent work from the disciplines of educational and…
Descriptors: Electronic Learning, Psychometrics, Educational Environment, Educational Technology
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Xu, Beijie; Recker, Mimi – Journal of Educational Data Mining, 2011
This article describes the Knowledge Discovery and Data Mining (KDD) process and its application in the field of educational data mining (EDM) in the context of a digital library service called the Instructional Architect (IA.usu.edu). In particular, the study reported in this article investigated a certain type of data mining problem, clustering,…
Descriptors: Electronic Libraries, Library Services, Multivariate Analysis, Electronic Learning
Anaya, Antonio R.; Boticario, Jesus G. – International Working Group on Educational Data Mining, 2009
Data mining methods are successful in educational environments to discover new knowledge or learner skills or features. Unfortunately, they have not been used in depth with collaboration. We have developed a scalable data mining method, whose objective is to infer information on the collaboration during the collaboration process in a…
Descriptors: Data Analysis, Cooperative Learning, College Students, Adult Students
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Amershi, Saleema; Conati, Cristina – Journal of Educational Data Mining, 2009
In this paper, we present a data-based user modeling framework that uses both unsupervised and supervised classification to build student models for exploratory learning environments. We apply the framework to build student models for two different learning environments and using two different data sources (logged interface and eye-tracking data).…
Descriptors: Supervision, Classification, Models, Educational Environment