The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
유비쿼터스 기술의 발전과 함께 유비쿼터스 학습은 학습자에게 새로운 기회를 제공합니다. 적절한 시간, 적절한 장소, 적절한 상황에 지원을 제공하기 위해 센서, RF-ID, 카메라 등을 통해 수집된 학습자의 행동을 분석하여 학습자의 상황을 파악할 수 있습니다. 트레이닝 현실 세계에서의 운동과 경험을 통해 기술을 습득하고 신체 능력을 향상시키는 것은 u-러닝의 중요한 영역입니다. 훈련 프로그램은 며칠 동안 지속될 수 있으며 하루에 하나 이상의 훈련 단위(운동)로 구성됩니다. 단위 내 학습자의 성과는 다음과 같이 간주됩니다. 단기 상태. 일련의 단위에서의 성과는 패턴(진행, 정체, 하락)에 따라 변경될 수 있습니다. 장기 상태 일련의 단위는 단기 상태를 기반으로 누적적으로 계산됩니다. 학습/훈련 프로그램에서는 학습자의 다양한 상태에 적응하기 위해 다양한 지원 전략을 적용하는 것이 필요합니다. 학습지원에 있어 적응은 중요한데, 적응하지 않으면 학습자는 쉽게 흥미를 잃게 되기 때문이다. 적응적 지원을 갖춘 시스템은 일반적으로 학습자에게 자극을 제공하며, 학습자는 학습 초기에 큰 동기를 가질 수 있습니다. 그러나 자극 요인이 특정 수준에 도달하면 학습자의 장기적 상태가 동적으로 변하기 때문에 학습자는 동기를 잃을 수 있습니다. 이는 진행 상태가 정체 상태 또는 감소 상태로 바뀔 수 있음을 의미합니다. 다양한 장기 학습 상태에서는 다양한 유형의 자극제가 필요합니다. 그러나 기존 시스템이 제공하는 자극과 조언은 변경 가능한 지원 전략 없이 단조롭습니다. 우리는 상호 적응 지원. 상호 적응은 시스템과 학습자가 각각 고유한 상태를 갖는다는 것을 의미합니다. 한편으로 시스템은 적응형 지원을 제공하기 위해 학습자의 상태에 적응하기 위해 상태를 변경하려고 시도합니다. 반면에 학습자는 시스템 상태에 따라 주어진 조언에 따라 성과를 변경할 수 있습니다. 우리는 시스템을 비유하여 유비쿼터스 애완동물(u-pet)을 만듭니다. u-pet은 학습자와 항상 함께하며, 학습자가 적절한 시기에 훈련을 시작하고 원활하게 훈련할 수 있도록 격려합니다. u-pet은 훈련 중인 학습자와 함께 행동을 수행할 수 있고, 학습자의 속성에 따라 자신의 속성을 변경할 수 있으며, 학습 기능을 통해 자신의 학습률을 조정할 수도 있습니다. 유펫은 학습자의 상태를 파악하고, 학습자의 단기 및 장기 상태에 따라 학습자의 훈련에 다양한 훈련 지원 전략을 채택합니다.
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Xianzhi YE, Lei JING, Mizuo KANSEN, Junbo WANG, Kaoru OTA, Zixue CHENG, "A Support Method with Changeable Training Strategies Based on Mutual Adaptation between a Ubiquitous Pet and a Learner" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 4, pp. 858-872, April 2010, doi: 10.1587/transinf.E93.D.858.
Abstract: 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 state. The performance in a series of units may change with patterns: progress, plateau, and decline. Long term state 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 states. 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.858/_p
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@ARTICLE{e93-d_4_858,
author={Xianzhi YE, Lei JING, Mizuo KANSEN, Junbo WANG, Kaoru OTA, Zixue CHENG, },
journal={IEICE TRANSACTIONS on Information},
title={A Support Method with Changeable Training Strategies Based on Mutual Adaptation between a Ubiquitous Pet and a Learner},
year={2010},
volume={E93-D},
number={4},
pages={858-872},
abstract={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 state. The performance in a series of units may change with patterns: progress, plateau, and decline. Long term state 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 states. 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.},
keywords={},
doi={10.1587/transinf.E93.D.858},
ISSN={1745-1361},
month={April},}
부
TY - JOUR
TI - A Support Method with Changeable Training Strategies Based on Mutual Adaptation between a Ubiquitous Pet and a Learner
T2 - IEICE TRANSACTIONS on Information
SP - 858
EP - 872
AU - Xianzhi YE
AU - Lei JING
AU - Mizuo KANSEN
AU - Junbo WANG
AU - Kaoru OTA
AU - Zixue CHENG
PY - 2010
DO - 10.1587/transinf.E93.D.858
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2010
AB - 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 state. The performance in a series of units may change with patterns: progress, plateau, and decline. Long term state 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 states. 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.
ER -