Sabine Graf

Work place: School of Computing and Information Systems, Athabasca University, Edmonton, T5J3S8, Canada



Research Interests: Computer systems and computational processes, Artificial Intelligence, Computational Learning Theory, Data Structures and Algorithms, Program Analysis and Transformation


Sabine Graf received her PhD from Vienna University of Technology, Austria, in 2007. She is presently an Associate Professor at Athabasca University, School of Computing and Information Systems, in Canada. She has published more than 120 peer-reviewed journal papers, book chapters, and conference papers which have been cited over 3,000 times and four conference papers were awarded with a best paper award. Dr Graf is Executive Board Member of the IEEE Technical Committee on Learning Technologies, Editor of the Bulletin of the IEEE Technical Committee on Learning Technology, and Associate Editor of the International Journal of Interaction Design and Architectures. Her research aims at making information systems, especially learning systems, more personalized, intelligent and adaptive. Her research expertise and interests include adaptivity and personalization, student modeling, ubiquitous and mobile learning, artificial intelligence, and learning analytics.

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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