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
Dengan kemajuan teknologi di mana-mana, pembelajaran di mana-mana memberikan peluang baharu kepada pelajar. Situasi pelajar boleh difahami melalui menganalisis tindakan pelajar yang dikumpulkan oleh penderia, RF-ID atau kamera untuk memberikan sokongan pada masa yang sesuai, tempat yang sesuai dan situasi yang betul. Latihan untuk memperoleh kemahiran dan meningkatkan kebolehan fizikal melalui senaman dan pengalaman dalam dunia sebenar adalah domain penting dalam u-learning. Program latihan mungkin berlangsung selama beberapa hari dan mempunyai satu atau lebih unit latihan (latihan) untuk sehari. Prestasi pelajar dalam unit dianggap sebagai keadaan jangka pendek. Prestasi dalam satu siri unit mungkin berubah mengikut corak: kemajuan, dataran tinggi dan penurunan. Negeri jangka panjang dalam satu siri unit dikira secara terkumpul berdasarkan keadaan jangka pendek. Dalam program pembelajaran/latihan, adalah perlu untuk menggunakan strategi sokongan yang berbeza untuk menyesuaikan diri dengan keadaan pelajar yang berbeza. Penyesuaian dalam sokongan pembelajaran adalah penting, kerana pelajar kehilangan minatnya dengan mudah tanpa penyesuaian. Sistem dengan sokongan penyesuaian biasanya menyediakan perangsang kepada pelajar, dan pelajar boleh mempunyai motivasi yang hebat dalam pembelajaran pada permulaan. Walau bagaimanapun, apabila stimulator mencapai beberapa tahap, pelajar mungkin kehilangan motivasinya, kerana keadaan jangka panjang pelajar berubah secara dinamik, yang bermaksud keadaan kemajuan mungkin berubah kepada keadaan dataran tinggi atau keadaan penurunan. Dalam keadaan pembelajaran jangka panjang yang berbeza, pelbagai jenis perangsang diperlukan. Walau bagaimanapun, perangsang dan nasihat yang disediakan oleh sistem sedia ada adalah monoton tanpa strategi sokongan yang boleh diubah. Kami mencadangkan a sokongan penyesuaian bersama. Penyesuaian bersama bermakna setiap sistem dan pelajar mempunyai keadaan mereka sendiri. Di satu pihak, sistem cuba mengubah keadaannya untuk menyesuaikan diri dengan keadaan pelajar untuk menyediakan sokongan penyesuaian. Sebaliknya, pelajar boleh mengubah prestasinya mengikut nasihat yang diberikan berdasarkan keadaan sistem. Kami mencipta haiwan peliharaan di mana-mana (u-pet) sebagai metafora sistem kami. U-pet sentiasa bersama pelajar dan menggalakkan mereka yang lebih kurus untuk memulakan latihan pada masa yang sesuai dan melakukan latihan dengan lancar. u-pet boleh melakukan tindakan dengan pelajar dalam latihan, menukar atributnya sendiri berdasarkan atribut pelajar dan melaraskan kadar pembelajarannya sendiri mengikut fungsi pembelajaran. u-pet memahami keadaan pelajar dan mengamalkan strategi sokongan latihan yang berbeza untuk latihan pelajar berdasarkan keadaan jangka pendek dan panjang pelajar.
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Salinan
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
Salinan
@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},}
Salinan
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 -