A Support Method with Changeable Training Strategies Based on Mutual Adaptation between a Ubiquitous Pet and a Learner
With the progress of ubiquitous technology, ubiquitous learning presents new opportunities to learners Situations of a learner can be grasped through analyzing the learner's actions collected by sensors. RF-IDs, or cameras in order to provide support at proper time. proper place. and proper situation Training for acquiring skills and enhancing physical abilities through exercise and experience in the real world is an important domain in u-learning A training program may last for several days and has one or more training units (exercises) for a day A learner's performance in a unit is considered as short term slate The performance in a series of units may change with patterns progress, plateau, and decline Long term stale in a series of units is accumulatively computed based on short term states In a learning/training program, it is necessary to apply different support strategies to adapt to different states of the learner Adaptation in learning support is significant, because a learner loses his/her interests easily without adaptation Systems with the adaptive support usually provide stimulators to a learner. and a learner can have a great motivation in learning. at beginning However, when the stimulators reach some levels. the learner may lose his/her motivation because the long term state of the learner changes dynamically, which means a progress state may change to a plateau state or a decline state In different long term learning states, different types of stimulators are needed However, the stimulators and advice provided by the existing systems are monotonic without changeable support strategies We propose a mutual adaptive support The mutual adaptation means each of the system and the learner has their own suites On one hand. the system tries to change its state to adapt to the learner's state for providing adaptive support On the other hand the learner can change its performance following the advice given based on the state of the system We create a ubiquitous pet (u-pet) as a metaphor of our system A u-pet is always with the learner and encourage the leaner to start training at proper time and to do training smoothly The u-pet can perform actions with the learner in training. change its own attributes based on the learner's attributes. and adjust its own learning rate by a learning function The u-pet grasps the state of the learner and adopts different training support strategies to the learner's training based on the learner's short and long term states.
mutually adaptation ubiquitous pet ubiquitous learning changeable support strategies training support