Exploring Interactive Design Strategies of Online Learning Platform Based on Cognitive Load Theory

Human Factors and Systems Interaction Pub Date : 1900-01-01 DOI:10.54941/ahfe1003585

Bian Kun, Wang Yan, Dong-Yup Han

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Abstract

In recent years, online learning has been increasingly popular due to its convenience and accessibility. To improve the quality of online learning, it is essential to understand the learners' cognitive load during online learning interaction. Cognitive load theory and teaching interaction hierarchy theory are employed to explore the impact of learners' cognitive load during online learning interaction. Based on these theories, this study utilizes EEG technology and subjective measurement to measure the cognitive load of learners' operational interaction and information interaction during online learning interaction. Six typical tasks were studied, including login, search, browse, share, and discuss. The results demonstrate that the login and search tasks have a higher cognitive load and the browse and share tasks have a lower cognitive load among the six typical tasks, virtual reality learning environments have a lower cognitive load than online learning environments. Therefore, by correctly identifying the cognitive load of tasks in operational and information interaction, optimization strategies can help to reduce the cognitive load of learners during online learning interaction and improve the quality of online learning.

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基于认知负荷理论的在线学习平台交互设计策略探讨

近年来，由于其便利性和可访问性，在线学习越来越受欢迎。为了提高在线学习的质量，了解学习者在在线学习互动中的认知负荷是至关重要的。运用认知负荷理论和教学互动层次理论探讨学习者认知负荷对在线学习互动的影响。在此基础上，本研究采用脑电图技术和主观测量法对学习者在线学习交互过程中操作交互和信息交互的认知负荷进行测量。研究了六种典型的任务，包括登录、搜索、浏览、分享和讨论。结果表明，在6种典型任务中，登录和搜索任务的认知负荷较高，浏览和共享任务的认知负荷较低，虚拟现实学习环境的认知负荷低于在线学习环境。因此，通过正确识别操作交互和信息交互任务的认知负荷，优化策略有助于降低学习者在在线学习交互过程中的认知负荷，提高在线学习质量。

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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Human Factors and Systems Interaction

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