Using the CBR Dynamic Method to Correct the Generates Learning Path in the Adaptive Learning System

In the current study of learning process that show learners will take a different way and use different types of learning resources in order to learning better. Any many researchers also agree that learning materials must be able to meet the various learning styles of learners. Therefore, let learners can effective to improve their learning, for different learning styles of learners should be given different types of learning materials. In this paper the authors propose a learner's learning style-based adaptive learning system architecture that is designed to help learners advance their on-line learning along an adaptive learning path. The investigation emphasizes the relationship of learning content to the learning style of each participant in adaptive learning. An adaptive learning rule was developed to identify how learners of different learning styles may associate those contents which have the higher probability of being useful to form an optimal learning path. In this adaptive learning system architecture, it will according to different learning styles given different types of learning materials and will according to learner's profile to adjust learner's learning style for providing suitable learning materials.

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The Learning Style-Based Adaptive Learning System Architecture

Special and Gifted Education ◽

10.4018/978-1-5225-0034-6.ch025 ◽

2016 ◽

pp. 608-618

Author(s):

Chyun-Chyi Chen ◽

Po-Sheng Chiu ◽

Yueh-Min Huang

Keyword(s):

Learning Styles ◽

Learning Style ◽

Adaptive Learning ◽

System Architecture ◽

Learning System ◽

Learning Materials ◽

Learning Path ◽

Different Types ◽

Learning System Architecture ◽

Adaptive Learning System

In the current study of learning process that show learners will take a different way and use different types of learning resources in order to learning better. Any many researchers also agree that learning materials must be able to meet the various learning styles of learners. Therefore, let learners can effective to improve their learning, for different learning styles of learners should be given different types of learning materials. In this paper the authors propose a learner's learning style-based adaptive learning system architecture that is designed to help learners advance their on-line learning along an adaptive learning path. The investigation emphasizes the relationship of learning content to the learning style of each participant in adaptive learning. An adaptive learning rule was developed to identify how learners of different learning styles may associate those contents which have the higher probability of being useful to form an optimal learning path. In this adaptive learning system architecture, it will according to different learning styles given different types of learning materials and will according to learner's profile to adjust learner's learning style for providing suitable learning materials.

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Implementation of an Adaptive Learning System based on Agents and Web Services

International Journal of Emerging Technologies in Learning (iJET) ◽

10.3991/ijet.v15i14.13837 ◽

2020 ◽

Vol 15 (14) ◽

pp. 162

Author(s):

Nihad El Ghouch ◽

EL Mokhtar En-Naimi ◽

Mohamed Kouissi

Keyword(s):

Web Services ◽

Adaptive Learning ◽

Learning System ◽

Case Based Reasoning ◽

Internet Applications ◽

Learning Path ◽

Multi Agent ◽

Adaptive Learning System ◽

Future Learning ◽

Case Based

Today, the integration of web services and agent technology into Internet applications has attracted the attention of many researchers, so that these applications allow a web service to call an agent service and vice versa. Web services are emerging and promising technologies for the development, deploy-ment and integration of the Internet applications and the use of agents makes them dynamic and automatic, they can provide updates when there is new infor-mation available and improve the qualities of web services by exploiting the ca-pacities and the characteristics of agents. In this context, we propose a prototype of a multi-agent adaptive learning system based on Incremental Hybrid Case Based Reasoning in order to support the learner in his learning process by offer-ing him a learning path adapted to his profile and predict his future learning. This support will be achieved through the execution of a hybrid cycle of Case Based Reasoning which brings together a set of agents collaborating and interacting with each other to provide specific services.

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