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首页> 外文会议>International Conference on Intelligent Systems Design and Applications >ULUL-ILM: The design of web-based adaptive educational hypermedia system based on learning style
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ULUL-ILM: The design of web-based adaptive educational hypermedia system based on learning style

机译：ULUL-ILM：基于学习风格的基于网络的自适应教育超媒体系统设计

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This paper explains about the architecture of ULUL-ILM : a web-based Adaptive Educational Hypermedia System (AEHS) that focuses on student's learning styles. It enables to recognize the student's learning style automatically in real time by means of Multi Layer Feed-Forward Neural network (MLFF). The MLFF is embedded to the system because of its ability to generalize and learn from specific examples, ability to be quickly updated with extra parameters, and speed in execution, making it suitable for real time applications. The system then enables to present and recommends a variety of learning contents adaptively towards each of the student's learning style identified in the student model through the adaptation model. The system then analyzes the learning content on each of the learning material, and then comes up with the generated teaching strategies by means of the teaching strategy generator and fragment sorting. The result of that analysis is called domain model. The adaptation model enables the system to adaptively presents the content, based on the student's learning style by combining the fragment sorting and adaptive annotation technique. The course player in ULUL-ILM enables the system to adaptively presents the content with various teaching strategies towards each of student's learning style. The purpose of ULUL-ILM is to provide the AEHS that can recognize student' learning style automatically in real-time and then presents the learning content adaptively based on their learning style. This paper is intended to elaborate the architecture of ULUL-ILM along with its user, domain and adaptive modeling technique used.
机译：本文介绍了ULUL-ILM的体系结构：一种基于Web的适应性教育超媒体系统（AEHS），其重点是学生的学习风格。它可以通过多层前馈神经网络（MLFF）实时自动识别学生的学习风格。 MLFF之所以嵌入到系统中，是因为它具有概括和学习特定示例的能力，可以用额外的参数快速更新的能力以及执行速度快等特点，从而使其适合于实时应用。然后，系统能够通过适应模型针对在学生模型中识别出的每个学生的学习风格自适应地呈现和推荐各种学习内容。然后，系统分析每种学习材料上的学习内容，然后借助教学策略生成器和片段排序提出生成的教学策略。该分析的结果称为域模型。自适应模型使系统能够通过结合片段排序和自适应注释技术，根据学生的学习风格来自适应地呈现内容。 ULUL-ILM中的课程播放器使系统能够针对每个学生的学习风格，以各种教学策略自适应地呈现内容。 ULUL-ILM的目的是提供一种AEHS，该AEHS可以自动实时识别学生的学习风格，然后根据他们的学习风格自适应地呈现学习内容。本文旨在详细阐述ULUL-ILM的体系结构及其使用的用户，域和自适应建模技术。

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