Work place: College of Information, University of North Texas, Denton, 76203-5017, USA



Research Interests: Computer systems and computational processes, Computational Learning Theory, Computer Architecture and Organization, Systems Architecture, Data Structures and Algorithms


Kinshuk received his PhD from De Montfort University (Leicester), United Kingdom in 1996. He is the Dean of the College of Information at the University of North Texas. Prior to that, he held the NSERC/CNRL/Xerox/McGraw Hill Research Chair for Adaptivity and Personalization in Informatics, funded by the Federal government of Canada, Provincial government of Alberta, and by national and international industries. He was also Full Professor in the School of Computing and Information Systems and Associate Dean of Faculty of Science and Technology, at Athabasca University, Canada. His research interests include learning analytics; learning technologies; mobile, ubiquitous and location aware learning systems; cognitive profiling; and, interactive technologies.

Author Articles
A Classification Framework for Context-aware Mobile Learning Systems

By Richard A.W. Tortorella Kinshuk Nian-Shing Chen Sabine Graf

DOI:, Pub. Date: 8 Jul. 2017

The field of context awareness is ever increasing due to the proliferation and omnipresent nature of mobile computing devices. Not only is learning becoming ubiquitous, but the sensors in mobile devices are permitting learning systems to adapt to the context of the learners. This paper provides a classification framework for the field of context-aware mobile learning, which is applied to papers published within selected journals from January 2009 to December 2015 inclusive. Obtained from the combined fields of context awareness and educational technology, a total of 2,968 papers are reviewed, resulting in 41 papers being selected for inclusion in this study. The classification framework consists of three layers: hardware architecture layer, context architecture layer and an evaluation layer. The framework will allow researchers and practitioners to quickly and accurately summarize the status of the current field of context-aware mobile learning. Furthermore, it has the potential to aid in future system development and decision making processes by showing the direction of the field as well as viable existing methods of system design and implementation.

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