Revolutionizing Word Clouds for Teaching and Learning With Generative Artificial Intelligence: Cases From China and Singapore

IF 2.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS IEEE Transactions on Learning Technologies Pub Date : 2024-04-04 DOI:10.1109/TLT.2024.3385009
Elizabeth Koh;Lishan Zhang;Alwyn Vwen Yen Lee;Hongye Wang
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Abstract

Generative artificial intelligence (AI) has the potential to revolutionize teaching and learning applications. This article examines the word cloud, a toolkit often used to scaffold teaching and learning for reflection, critical thinking, and content learning. Addressing the issues in traditional word clouds, semantic word clouds have been developed but they are technically challenging to develop and still problematic. However, generative AI has the potential to develop efficient, accurate, creative, and accessible word clouds. Three different methods representing three major approaches of word cloud generation were developed, implemented, and user evaluated—traditional (baseline), semantic (natural language processing enhanced), and generative AI (generative pretrained transformer based)—in two different language contexts—Chinese (China case) and English (Singapore case). The findings of the study show the technical robustness of the methods, as well as provide key pedagogical insights from the user perspective of instructors of higher education courses in China and Singapore. Implications to the design of word clouds and their application in teaching and learning are discussed.
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利用生成式人工智能革新词云教学:中国和新加坡的案例
生成式人工智能(AI)有可能彻底改变教学应用。本文对词云进行了研究,词云是一种常用于为反思、批判性思维和内容学习提供教学支架的工具包。为了解决传统词云中存在的问题,人们开发了语义词云,但开发语义词云在技术上具有挑战性,而且仍然存在问题。然而,生成式人工智能有可能开发出高效、准确、有创意且易于使用的词云。在两种不同的语言环境中--中文(中国案例)和英文(新加坡案例)--开发、实施了代表词云生成三大方法的三种不同方法,并进行了用户评估--传统方法(基线)、语义方法(自然语言处理增强型)和生成式人工智能方法(基于生成式预训练转换器)。研究结果表明了这些方法在技术上的稳健性,并从中国和新加坡高等教育课程教师的用户角度提供了重要的教学见解。研究还讨论了词云设计及其在教学中应用的意义。
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来源期刊
IEEE Transactions on Learning Technologies
IEEE Transactions on Learning Technologies COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
7.50
自引率
5.40%
发文量
82
审稿时长
>12 weeks
期刊介绍: The IEEE Transactions on Learning Technologies covers all advances in learning technologies and their applications, including but not limited to the following topics: innovative online learning systems; intelligent tutors; educational games; simulation systems for education and training; collaborative learning tools; learning with mobile devices; wearable devices and interfaces for learning; personalized and adaptive learning systems; tools for formative and summative assessment; tools for learning analytics and educational data mining; ontologies for learning systems; standards and web services that support learning; authoring tools for learning materials; computer support for peer tutoring; learning via computer-mediated inquiry, field, and lab work; social learning techniques; social networks and infrastructures for learning and knowledge sharing; and creation and management of learning objects.
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