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用于学生与智能机器协同学习的基于人类智能的元宇宙。

Human intelligence-based metaverse for co-learning of students and smart machines.

作者信息

Lee Chang-Shing, Wang Mei-Hui, Reformat Marek, Huang Sheng-Hui

机构信息

Department of Computer Science and Information Engineering, National University of Tainan, Tainan, Taiwan.

Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada.

出版信息

J Ambient Intell Humaniz Comput. 2023;14(6):7695-7718. doi: 10.1007/s12652-023-04580-2. Epub 2023 Mar 9.

Abstract

This paper proposes a as an educational environment for co-learning of students and machines. The is based on the spirit of the that equips the environment with teaching principles and cognitive intelligence of ancient words of wisdom. There are four stages of the Metaverse: preparation and collection of learning data, data preprocessing, data analysis, and data evaluation. During the data preparation stage, the domain experts construct a learning dictionary with fuzzy concept sets describing different terms and concepts related to the course domains. Then, the students and teachers use the developed CI&AI-FML learning tools to interact with machines and learn together. Once the teachers prepare relevant material, students provide their inputs/texts representing their levels of understanding of the learned concepts. A Natural Language Processing (NLP) tool, Chinese Knowledge Information Processing (CKIP), is used to process data/text generated by students. A focus is put on speech tagging, word sense disambiguation, and named entity recognition. Following that, the quantitative and qualitative data analysis is performed. Finally, the students' learning progress, measured using , is evaluated and analyzed. The experimental results reveal that the proposed HI-based CI&AI-FML Metaverse can foster students' motivation to learn and improve their performance. It has been shown in the case of young students studying Software Engineering and learning English.

摘要

本文提出了一种作为学生和机器共同学习的教育环境。该环境基于古代智慧名言的教学原则和认知智能的精神。元宇宙有四个阶段:学习数据的准备和收集、数据预处理、数据分析和数据评估。在数据准备阶段,领域专家构建一个学习词典,其中包含描述与课程领域相关的不同术语和概念的模糊概念集。然后,学生和教师使用开发的CI&AI-FML学习工具与机器进行交互并一起学习。一旦教师准备好相关材料,学生就提供代表他们对所学概念理解水平的输入/文本。使用自然语言处理(NLP)工具中文知识信息处理(CKIP)来处理学生生成的数据/文本。重点放在语音标记、词义消歧和命名实体识别上。随后,进行定量和定性数据分析。最后,使用 对学生的学习进度进行评估和分析。实验结果表明,所提出的基于HI的CI&AI-FML元宇宙可以激发学生的学习动机并提高他们的表现。在学习软件工程和学习英语的年轻学生案例中已经得到证明。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e5f4/9998022/9ed530fa088f/12652_2023_4580_Fig1_HTML.jpg

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