Empowering learners with AI‐generated content for programming learning and computational thinking: The lens of extended effective use theory.
Background: Artificial intelligence–generated content (AIGC) has stepped into the spotlight with the emergence of ChatGPT, making effective use of AIGC for education a hot topic. Objectives: This study seeks to explore the effectiveness of integrating AIGC into programming learning through debugging...
| Publicado en: | Journal of Computer Assisted Learning Vol. 40; no. 4; pp. 1941 - 1959 |
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| Autores principales: | , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Aug2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=178531926&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178531926 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Aug2024 vid: 40 iid: 4 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 178531926 177103817 178531926 178531926 10.1111/jcal.12996 178531926 ppf: 1941 ppct: 18 formats: tig: atl: Empowering learners with AI‐generated content for programming learning and computational thinking: The lens of extended effective use theory. aug: au: Shanshan, Shang Sen, Geng affil: School of Business and Management, Shanghai International Studies University, Shanghai, China sug: subj: Artificial Intelligence, Generative Programming Languages Education Learning Methods Outcomes of Education Human Funding Source Male Female Adolescence Young Adult Questionnaires Experimental Studies Summated Rating Scaling Chi Square Test Pearson's Correlation Coefficient Coefficient alpha One-Way Analysis of Variance Confidence Intervals Empowerment Adolescent: 13-18 years Male Female ab: Background: Artificial intelligence–generated content (AIGC) has stepped into the spotlight with the emergence of ChatGPT, making effective use of AIGC for education a hot topic. Objectives: This study seeks to explore the effectiveness of integrating AIGC into programming learning through debugging. First, the study presents three levels of AIGC integration based on varying levels of abstraction. Then, drawing on extended effective use theory, the study proposes the underlying mechanism of how AIGC integration impacts programming learning performance and computational thinking. Methods: Three debugging interfaces integrated with AIGC by ChatGPT were developed for this study according to three levels of AIGC integration design. The study conducts a between‐subject experiment with one control group and three experimental groups. Analysis of covariance and a structural equation model are employed to examine the effects. Results and Conclusions: The results show that the second and third levels of abstraction in AIGC integration yield better learning performance and computational thinking, but the first level shows no difference compared to traditional debugging. The underlying mechanism suggests that the second and third levels of abstraction promote transparent interaction, which enhances representational fidelity and consequently impacts learning performance and computational thinking, as evidenced in test of the mechanism. Moreover, the study finds that learning fidelity weakens the effect of transparent interaction on representational fidelity. Our research offers valuable theoretical and practical insights. Lay Description: What is currently known about the subject matter?: In the present information era, programming and computational thinking are important.AIGC has attracted remarkable attention from both academics and managers.If it is appropriately utilised, AIGC can facilitate education. What this paper adds: Three forms of AIGC integration based on the level of abstraction, which enhance programming learning and computational thinking.Application of extended effective use theory to propose an underlying mechanism for how AIGC integration affects learning performance and computational thinking.Concrete information on the utilisation of AIGC in the education domain.Evidence that shows the importance of interaction transparency and representational fidelity for leveraging information technologies in education. Implications of the study findings for practitioners: AIGC can be an effective tool for teachers, learners, and institutions.Platform designers and teachers should carefully design AICG integration.Platform designers and teachers could make use of various methods and other forms of AIGC integration to promote interaction transparency. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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