AI‐Assisted Assessment of Inquiry Skills in Socioscientific Issue Contexts.
Background Study: Assessing learners' inquiry‐based skills is challenging as social, political, and technological dimensions must be considered. The advanced development of artificial intelligence (AI) makes it possible to address these challenges and shape the next generation of science education....
| Publicado en: | Journal of Computer Assisted Learning Vol. 41; no. 1; pp. 1 - 19 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Feb2025
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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=183981451&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183981451 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Feb2025 vid: 41 iid: 1 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 183981451 183981451 183981451 10.1111/jcal.13102 183981451 ppf: 1 ppct: 18 formats: tig: atl: AI‐Assisted Assessment of Inquiry Skills in Socioscientific Issue Contexts. aug: au: Zhang, Wen Xin Lin, John J. H. Hsu, Ying‐Shao affil: Graduate Institute of Science Education, National Taiwan Normal University, Taipei, Taiwan sug: subj: Artificial Intelligence Communication Skills Evaluation Learning Methods Science Education Problem-Based Learning Student Attitudes Competency Assessment Human Funding Source Taiwan Learning Environment Natural Language Processing Questionnaires Male Female Conceptual Framework Descriptive Statistics Data Analysis Software ROC Curve Two-Way Analysis of Variance Algorithms Neural Networks (Computer) Reproducibility of Results Male Female ab: Background Study: Assessing learners' inquiry‐based skills is challenging as social, political, and technological dimensions must be considered. The advanced development of artificial intelligence (AI) makes it possible to address these challenges and shape the next generation of science education. Objectives: The present study evaluated the SSI inquiry skills of students in an AI‐enabled scoring environment. An AI model for socioscientific issues that can assess students' inquiry skills was developed. Responses to a learning module were collected from 1250 participants, and the open‐ended responses were rated by humans in accordance with a designed rubric. The collected data were then preprocessed and used to train an AI rater that can process natural language. The effects of two hyperparameters, the dropout rate and complexity of the AI neural network, were evaluated. Results and Conclusion: The results suggested neither of the two hyperparameters was found to strongly affect the accuracy of the AI rater. In general, the human and AI raters exhibited certain levels of agreement; however, agreement varied among rubric categories. Discrepancies were identified and are discussed both quantitatively and qualitatively. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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