Context awareness system on ubiquitous learning with case based reasoning and nearest neighbor algorithm

Several learning technologies have been developed over the years in order to help distance learning process. However, those technologies are still not enough to give the proper interaction between their users and the technologies themselves. It is caused by the absence of the technologies ability to give context based on their users conditions. When human interacts with each other, they understand each other and use information that is situational or in the form of context that can enhance the quality of the interaction. This human capability is not present in computers to interact with human. Those learning technologies can be developed to give context that are self-adjusting to the users conditions based on ubiquitous computing accompanied by context awareness system. This kind of technology is called ubiquitous learning. Context awareness system acts as the system to control the context passing to the user. This paper will discuss the design and test plans of the context awareness system that is developed by using Case Based Reasoning and Nearest Neighbor algorithms to build or develop a system that is capable of giving appropriate context to learners in a learning technology.

[1]  Adebayo Kolawole John,et al.  Temperament and Mood Detection Using Case- Based Reasoning , 2014 .

[2]  William R. Watson,et al.  An Argument for Clarity: What are Learning Management Systems, What are They Not, and What Should They Become? , 2007 .

[3]  Matthias Baldauf,et al.  A survey on context-aware systems , 2007, Int. J. Ad Hoc Ubiquitous Comput..

[4]  Birgit Bomsdorf,et al.  Adaptation of Learning Spaces: Supporting Ubiquitous Learning in Higher Distance Education , 2005, Mobile Computing and Ambient Intelligence.

[5]  Gregory D. Abowd,et al.  Towards a Better Understanding of Context and Context-Awareness , 1999, HUC.

[6]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[8]  Vijay Athavale,et al.  Case retrieval optimization of Case-based reasoning through Knowledge-intensive Similarity measures , 2011 .

[9]  Monica Vladoiu,et al.  U-Learning Within A Context-Aware Multiagent Environment , 2011, ArXiv.

[10]  Erny Arniza Ahmad,et al.  The definition and characteristics of ubiquitous learning : A discussion , 2010 .

[11]  Monica Vladoiu.  U-LEARNING SCENARIOS WITHIN CONTEXT AWARE UBIQUITOUS LEARNING ENVIRONMENTS , 2011 .

[12]  Tzung-Shi Chen,et al.  Context-Aware Writing in Ubiquitous Learning Environments , 2008, Fifth IEEE International Conference on Wireless, Mobile, and Ubiquitous Technology in Education (wmute 2008).

[13]  Joung-Souk Sung.  U-Learning Model Design Based on Ubiquitous Environment , 2010 .

[14]  Hajo A. Reijers,et al.  Case-based reasoning as a technique for knowledge management in business process redesign , 2003 .

[15]  Hiroaki Ogata,et al.  Context-aware Multimodal Interfaces Enhancing Ubiquitous Learning , 2012, mLearn.