Next Generation Self-learning Style in Pervasive Computing Environments
Kaoru Ota, Mianxiong Dong, Long Zheng, Jun Ma, Li Li, Daqiang Zhang, Minyi Guo · Advances in Computer Science and Engineering · 2011
With the great progress of technologies, computers are embedded into everywhere to make our daily life convenient, efficient and comfortable [10][11][12] in a pervasive computing environment where services necessary for a user can be provided without demanding intentionally.This trend also makes a big influence even on the education field to make support methods for learning more effective than some traditional ways such as WBT (Web-Based Training) and e-learning [13, 14].For example, some WBT systems for educational using in some universities [1, 2, 9], a system for teacher-learners' interaction in learner oriented education [3], and real e-learning programs for students [7, 8] had succeeded in the field.However, a learner's learning time is more abundant in the real world than in the cyber space, and learning support based on individual situation is insufficient only with WBT and e-learning.In addition, some researches show that it is difficult for almost all learners to adopt a self-directed learning style and few of learners can effectively follow a self-planned schedule [4].Therefore, support in the real world is necessary for learners to manage a learning schedule to study naturally and actively with a self-learning style.Fortunately, with the rapid development of embedded technology, wireless networks, and individual detecting technology, these pervasive computing technologies make it possible to support a learner anytime and anywhere kindly, flexibly, and appropriately.Moreover, it comes to be able to provide the support more individually as well as comfortable surroundings for each learner through analyzing the context information (e.g.location, time, actions, and so on) which can be acquired in the pervasive computing environment.In this chapter, we address a next-generation self-learning style with the pervasive computing and focus on two aspects: providing proper learning support to individuals and making learning environments suitable for individuals.Especially, a support method is proposed to encourage a learner to acquire his/her learning habit based on Behavior Analysis through a scheduler system called a Ubiquitous Learning Scheduler (ULS).In our design, the learner's situations are collected by sensors and analyzed by comparing them to his/her learning histories.Based on this information, supports are provided to the learner in order to help him/her forming a good learning style.For providing comfortable www.intechopen.comAdvanced in Computer Science and Engineering 4 surroundings, we improve the ULS system by utilizing data sensed by environments like room temperature and light for the system, which is called a Pervasive Learning Scheduler (PLS).The PLS system adjusts each parameter automatically for individuals to make a learning environment more comfortable.Our research results revealed that the ULS system not only benefits learners to acquire their learning habits but also improved their selfdirected learning styles.In addition, experiment results show the PLS system get better performance than the ULS system.The rest of the chapter consists as follows.In the section 2, we propose the ULS system and describe the design of the system in detail followed by showing implementation of the system with experimental results.In section 3, the PLS system is proposed and we provide an algorithm to find an optimum parameter to be used in the PLS system.The PLS system is also implemented and evaluated comparing to the ULS system.Finally, section 4 concludes this chapter.