Abstract
Today people learn in many diverse locations and contexts, beyond the confines of classical brick and mortar classrooms. This trend is ever increasing, progressing hand-in-hand with the progress of technology. Context-aware learning systems are systems which adapt to the learner’s context, providing tailored learning for a particular learning environment. Existing context-aware learning systems often utilize sui generis systems, which require extensive work to develop and implement, can be difficult to re-use, and do not necessarily facilitate collaboration. In an effort to alleviate problems found in existing context-aware learning systems, this paper proposes a framework, and subsequent evaluation, for the development of context-aware learning systems. The proposed framework allows for the design and develop learning systems for different types of learning scenarios, with the ability to adapt to the context in which the learner is situated.
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References
Anagnostopoulos, C., & Hadjiefthymiades, S. (2009). Advanced inference in situation-aware computing. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 39(5), 1108–1115.
Baldauf, M., Dustdar, S., & Rosenberg, F. (2007). A survey on context-aware systems. International Journal of Ad Hoc and Ubiquitous Computing, 2(4), 263–277.
Bardram, J. E. (2005). The Java Context Awareness Framework (JCAF) – A Service Infrastructure and Programming Framework for Context-Aware Applications. In H. W. Gellersen, R. Want, & A. Schmidt (Eds.), Pervasive Computing: Third International Conference, PERVASIVE 2005, Munich, Germany, May 8–13, 2005. Proceedings (pp. 98–115). Berlin, Heidelberg: Springer Berlin Heidelberg.
Capel-Cuevas, S., Cuellar, M.P., del Pegalajar M.C., de Orbe-Paya I., & Capitan-Vallvey, L..F. (2012). An expert system for full pH range prediction using a disposable optical sensor Array. Sensors Journal, 12(5), 1197–1206.
Chen, Z. (1994). Attaching a connectionist learning assistant to inference engine. Paper presented at the Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 I.E. International Conference on.
Chu, H.-C., Hwang, G.-J., Tsai, C.-C., & Tseng, J. C. R. (2010). A two-tier test approach to developing location-aware mobile learning systems for natural science courses. Computers & Education, 55(4), 1618–1627. doi:10.1016/j.compedu.2010.07.004.
Cooper, D. (1994). A dual-sensor rate-responsive pacemaker algorithm incorporating a fuzzy logic expert system. Paper presented at the Computers in Cardiology 1994.
Franklin, J.E., Carmody, C.L., Keller, K., Levitt, T.S., & Buteau, B.L. (1988). Expert system technology for the military: Selected samples. Paper presented at the Proceedings of the IEEE.
Huang, Y. M., & Chiu, P. S. (2015). The effectiveness of a meaningful learning-based evaluation model for context-aware mobile learning. British Journal of Educational Technology, 46(2), 437–447.
Hwang, G.-J. (2014). Definition, framework and research issues of smart learning environments - a context-aware ubiquitous learning perspective. Smart Learning Environments, 1(1), 4. doi:10.1186/s40561-014-0004-5.
Kranz, M., Möller, A., Hammerla, N., Diewald, S., Plötz, T., Olivier, P., & Roalter, L. (2013). The mobile fitness coach: Towards individualized skill assessment using personalized mobile devices. Pervasive and Mobile Computing, 9(2), 203–215. doi:10.1016/j.pmcj.2012.06.002.
Kusiak, A. (1987). Artificial intelligence and operations research in flexible manufacturing systems. INFOR: Information Systems and Operational Research, 25, 2–12.
Kusiak, A. (1989). Expert systems and optimization. IEEE Transactions on Software Engineering, 15(8), 1017–1020.
Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. T. (2010). A survey of mobile phone sensing. Communications Magazine, 48, 140–150.
Levi, K. (1989). Expert systems should be more accurate than human experts: Evaluation procedures from human judgement and decision making. IEEE Transactions on Systems, Man, and Cybernetics, 19(3), 647–657.
Liu, G. Z., & Hwang, G. J. (2010). A key step to understanding paradigm shifts in e-learning: Towards context-aware ubiquitous learning. British Journal of Educational Technology, 41, E1–E9.
McBryan, B., Meyer, S.M., & Knabach, M.D. (1990). A sensor management expert system for multiple sensor integration (MSI). Paper presented at the National Aerospace and Electronics Conference. NAECON 1990.
de Oliveira, L., & Loureiro, A. (2011). Code Droid: A Framework to Develop Context-Aware Applications. Paper presented at the The Fourth International Conferences on Advances in Human-oriented and Porsonalized Mechanisms, Technologies, and Services.
Park, Y. (2011). A pedagogical framework for mobile learning: Categorizing educational applications of mobile technologies into four types. The International Review of Research in Open and Distributed Learning, 12(2), 78–102.
Prabhakaran, S., Sharon Rosy, S., & Shakena Grace, S. (2010). Detecting and spotting bombs using wireless sensors and expert systems. Paper presented at the IEEE International Conference on Communication Control and Computing Technologies (ICCCCT).
Schilit, B., Adams, N., & Want, R. (1994). Context-aware computing applications. Paper presented at the First Workshop on Mobile Computing Systems and Applications.
Sharples, M., Taylor, J., & Vavoula, G. (2010). A theory of learning for the mobile age Medienbildung in neuen Kulturräumen (pp. 87–99): Springer.
Shih, J.L., Chuang, C.W., Cheng, J.J., & Hwang, G.J. (2010). The development of mobile learning system for local history and geography of Taiwan. Paper presented at the 2nd international Asia conference on informatics in control, Automation and Robotics.
Singh, S., & Karwayun, R. (2010). A Comparative Study of Inference Engines. Paper presented at the Information Technology: New Generations (ITNG), 2010 Seventh International Conference on.
Stratulat, B., & Popa, M. (2011). Data collection in a hybrid architecture of mobile devices and sensors. Paper presented at the 2011 6th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI).
Won, S., Golnaraghi, F., & Melek, W. W. (2009). A fastening tool tracking system using an IMU and a position sensor with Kalman filters and a fuzzy expert system. IEEE Transactions on Industrial Electronics, 56(5), 1782–1792.
Yau, J., & Joy, M. (2007). A Context-aware And Adaptive Learning Schedule Framework For Supporting Learners' Daily Routines. Paper presented at the Second International Conference on Systems (ICONS).
Zamzuri, N.H., Kassim, E.S., & Shahrom, M. (2010). The Role Of Cognitive Styles In Investigating E-learning Usability. Paper presented at the International Conference on e-Education, e-Business, e-Management, and e-Learning.
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The authors would like to acknowledge NSERC for partial support of this research.
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Tortorella, R.A.W., Kinshuk, D. & Chen, NS. Framework for designing context-aware learning systems. Educ Inf Technol 23, 143–164 (2018). https://doi.org/10.1007/s10639-017-9591-4
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DOI: https://doi.org/10.1007/s10639-017-9591-4