IJMECS Vol. 9, No. 7, 8 Jul. 2017
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Context-aware learning system, mobile learning, adaptive learning, classification framework, ubiquitous learning, context-awareness
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.
Richard A.W. Tortorella, Kinshuk, Nian-Shing Chen, Sabine Graf, "A Classification Framework for Context-aware Mobile Learning Systems", International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.7, pp.1-11, 2017. DOI:10.5815/ijmecs.2017.07.01
[1]Lucke, U. and C. Rensing, A survey on pervasive education. Pervasive and Mobile Computing, 2014. 14: p. 3-16.
[2]Zamzuri, N.H., E.S. Kassim, and M. Shahrom. The Role of Cognitive Styles in Investigating E-learning Usability. in e-Education, e-Business, e-Management, and e-Learning, 2010. IC4E'10. International Conference on. 2010. IEEE.
[3]Wong, L.-H. and C.-K. Looi, What seams do we remove in mobile-assisted seamless learning? A critical review of the literature. Computers & Education, 2011. 57(4): p. 2364-2381.
[4]Lane, N.D., et al., A survey of mobile phone sensing, in Communications Magazine. 2010, IEEE. p. 140-150.
[5]Schilit, B., N. Adams, and R. Want. Context-aware computing applications. in Mobile Computing Systems and Applications, 1994. WMCSA 1994. First Workshop on. 1994. IEEE.
[6]Malek, J., M. Laroussi, and A. Derycke. A multi-layer ubiquitous middleware for bijective adaptation between context and activity in a mobile and collaborative learning. in Systems and Networks Communications, 2006. ICSNC'06. International Conference on. 2006. IEEE.
[7]Weiser, M., R. Gold, and J.S. Brown, The origins of ubiquitous computing research at PARC in the late 1980s. IBM systems journal, 1999. 38(4): p. 693-696.
[8]Cope, B. and M. Kalantzis, Ubiquitous learning: An agenda for educational transformation. Proceedings of the 6th Networked Learning, 2008.
[9]Hwang, G.-J., et al., A context-aware ubiquitous learning environment for conducting complex science experiments. Computers & Education, 2009. 53(2): p. 402-413.
[10]Yau, J. and M. Joy. A Context-aware and Adaptive Learning Schedule framework for supporting learners' daily routines. in Systems, 2007. ICONS'07. Second International Conference on. 2007. IEEE.
[11]Hwang, G.J. and C.C. Tsai, Research trends in mobile and ubiquitous learning: A review of publications in selected journals from 2001 to 2010. British Journal of Educational Technology, 2011. 42(4): p. E65-E70.
[12]Baldauf, M., S. Dustdar, and F. Rosenberg, A survey on context-aware systems. International Journal of Ad Hoc and Ubiquitous Computing, 2007. 2(4): p. 263-277.
[13]Hong, J.-y., E.-h. Suh, and S.-J. Kim, Context-aware systems: A literature review and classification. Expert Systems with Applications, 2009. 36(4): p. 8509-8522.
[14]Perera, C., et al., Context Aware Computing for The Internet of Things: A Survey. Communications Surveys & Tutorials, IEEE, 2014. 16(1): p. 414-454.
[15]Wu, H.-K., et al., Current status, opportunities and challenges of augmented reality in education. Computers & Education, 2013. 62: p. 41-49.
[16]Hwang, G.-J. and P.-H. Wu, Applications, impacts and trends of mobile technology-enhanced learning: a review of 2008-2012 publications in selected SSCI journals. Int. J. Mob. Learn. Organ., 2014. 8(2): p. 83-95.
[17]Santos, P., et al., QuesTInSitu: From tests to routes for assessment in situ activities. Computers & Education, 2011. 57(4): p. 2517-2534.
[18]Kranz, M., et al., The mobile fitness coach: Towards individualized skill assessment using personalized mobile devices. Pervasive and Mobile Computing, 2013. 9(2): p. 203-215.
[19]Chu, H.-C., et al., A two-tier test approach to developing location-aware mobile learning systems for natural science courses. Computers & Education, 2010. 55(4): p. 1618-1627.
[20]Buttussi, F. and L. Chittaro, Smarter Phones for Healthier Lifestyles: An Adaptive Fitness Game. Pervasive Computing, IEEE, 2010. 9(4): p. 51-57.
[21]Huizenga, J., et al., Mobile game-based learning in secondary education: engagement, motivation and learning in a mobile city game. Journal of Computer Assisted Learning, 2009. 25(4): p. 332-344.
[22]Dib, C.Z. Formal, non-formal and informal education: Concepts/applicability. in Cooperative Networks in Physics Education-Conference Proceedings. 1988.
[23]Hwang, G.-J., P.-H. Wu, and H.-R. Ke, An interactive concept map approach to supporting mobile learning activities for natural science courses. Computers & Education, 2011. 57(4): p. 2272-2280.
[24]Kamarainen, A.M., et al., EcoMOBILE: Integrating augmented reality and probeware with environmental education field trips. Computers & Education, 2013. 68(0): p. 545-556.