On assessment of brain function adaptability in Open Learning systems using Neural Networks modeling (cognitive styles approach)

H. Mustafa, S. Badran
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引用次数: 14

Abstract

The piece of research presents a conceptual overview on diverse cognitive styles reflections in adaptable Open Learning systems. The main goal of this approach is quantitative forecasting the performance of adaptable Open Learning (equivalently e-learning) Systems using cognitive Neural Network modelling. Furthermore, analysis of interactive two diverse learners' cognitive styles with a friendly adaptable teaching environment(e-courses material). Consequently, presented paper provides e-learning systems' designers with relevant guide for learning performance enhancement. Additionally, it supports e-learners in fulfilment of better learning achievements during face to face tutoring. Accordingly, quantitative analysis of e-learning adaptability performed herein, via assessment of matching between learning style preferences and the instructor's teaching style and/or e-courses material. Interestingly, application of two realistic cognitive models using Artificial Neural Network gives an opportunity to experience well assessment of adaptable e-learning features. Such as adaptability mismatching, adaptation time convergence, and individual differences of e-learners' adaptability.
基于神经网络建模的开放式学习系统脑功能适应性评估(认知风格方法)
这篇研究对适应性开放学习系统中不同认知风格的反映进行了概念性概述。该方法的主要目标是使用认知神经网络建模定量预测适应性开放学习(相当于电子学习)系统的性能。进一步分析了两种不同学习者的认知风格与友好的适应性教学环境(电子课程材料)的互动。因此,本文为电子学习系统的设计者提供了提高学习绩效的相关指导。此外,它支持在线学习者在面对面辅导中实现更好的学习成果。因此,通过评估学习风格偏好与教师的教学风格和/或电子课程材料之间的匹配,本文对电子学习适应性进行了定量分析。有趣的是,使用人工神经网络的两种现实认知模型的应用为适应性电子学习特征的良好评估提供了机会。网络学习者的适应性存在适应性不匹配、适应时间收敛、个体差异等问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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