Elizabeth Koh;Lishan Zhang;Alwyn Vwen Yen Lee;Hongye Wang
{"title":"Revolutionizing Word Clouds for Teaching and Learning With Generative Artificial Intelligence: Cases From China and Singapore","authors":"Elizabeth Koh;Lishan Zhang;Alwyn Vwen Yen Lee;Hongye Wang","doi":"10.1109/TLT.2024.3385009","DOIUrl":null,"url":null,"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.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1416-1427"},"PeriodicalIF":2.9000,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Learning Technologies","FirstCategoryId":"95","ListUrlMain":"https://ieeexplore.ieee.org/document/10492688/","RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
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.
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.