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首页> 外文期刊>International journal of knowledge and learning >A component-based knowledge domain model for adaptive human learning systems
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A component-based knowledge domain model for adaptive human learning systems

机译：自适应人类学习系统的基于组件的知识域模型

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摘要

Adaptive human learning systems (AHLSs) are important tools to personalise learning. However, the used domain representation formalisms lack the needed precision and flexibility those make the domains efficiently adaptable and intensively reusable. To address this issue, we propose a component based knowledge domain for an AHLS that aims to improve the adapting efficiency and provides intensive reuse of the pre-built (sub) knowledge domains. To show the feasibility and the benefits of the proposed AHLS, a prototype that experiments the explanation variants method is implemented. So, unlike the other, our AHLS achieves the adapted learning by (re)selecting and sequencing the appropriate linear combination of the component variants explaining the corresponding concepts. Also, as a more challenging task, to get a compromised solution of the conflicting learning goals and to reduce the substantial overhead, the adapting is formulated as a multi-objective component variants selection problem and it is implemented using Genetic Algorithms.
机译：自适应人类学习系统（AHLS）是个性化学习的重要工具。但是，所使用的域表示形式主义缺乏使域有效地适应性强和可重复使用的精度和灵活性。为解决此问题，我们为AHLS提出了一个基于组件的知识域，旨在提高适应效率并提供对预建（子）知识域的密集重用。为了显示所提出的AHLS的可行性和好处，实现了一个用于解释解释变量方法的原型。因此，与其他方法不同，我们的AHLS通过（重新）选择和排序解释对应概念的组件变体的适当线性组合来实现自适应学习。此外，作为一项更具挑战性的任务，为了获得有冲突的学习目标的折衷解决方案并减少大量开销，将适应性公式化为多目标组件变体选择问题，并使用遗传算法实现。

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