AI-Assisted Assessment of Inquiry Skills in Socioscientific Issue Contexts

被引：1

作者：

Zhang, Wen Xin ^{[1
]}

Lin, John J. H. ^{[1
]}

Hsu, Ying-Shao ^{[1
]}

机构：

[1] Natl Taiwan Normal Univ, Grad Inst Sci Educ, Taipei, Taiwan

来源：

JOURNAL OF COMPUTER ASSISTED LEARNING | 2025年 / 41卷 / 01期

关键词：

auto scoring; inquiry-based learning; learning assessment; socioscientific issues; NEURAL-NETWORKS; SUSTAINABILITY SCIENCE; LSTM; VALIDATION; CHALLENGES; EDUCATION; FUTURE;

D O I：

10.1111/jcal.13102

中图分类号：

G40 [教育学];

学科分类号：

040101 ; 120403 ;

摘要：

Background StudyAssessing 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.ObjectivesThe 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 ConclusionThe 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.

引用

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