File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: A multigroup structural equation modeling analysis of students’ perception, motivation, and performance in computational thinking

TitleA multigroup structural equation modeling analysis of students’ perception, motivation, and performance in computational thinking
Authors
Keywordscomputational thinking
gender difference
motivation
perception
structural equation model
Issue Date7-Sep-2022
PublisherFrontiers Media
Citation
Frontiers in Psychology, 2022, v. 13 How to Cite?
Abstract

Students’ perceptions of learning are important predictors of their learning motivation and academic performance. Examining perceptions of learning has meaningful implications for instruction practices, while it has been largely neglected in the research of computational thinking (CT). To contribute to the development of CT education, we explored the influence of students’ perceptions on their motivation and performance in CT acquisition and examined the gender difference in the structural model using a multigroup structural equation modeling (SEM) analysis. Two hundred and eighty-five students from a Chinese urban high school were recruited for the study. The analysis revealed that students’ perceptions of CT positively influenced their CT performance and learning motivation, and some motivational constructs, namely self-efficacy and learning goal orientation (LGO), also positively influenced their CT performance. Furthermore, in the male student group, perceptions of CT exhibited significant correlations with both self-efficacy and LGO. However, no significant correlation was found in the female student group. Implications for research and teaching practice in CT education are presented herein.


Persistent Identifierhttp://hdl.handle.net/10722/339718
ISSN
2023 Impact Factor: 2.6
2023 SCImago Journal Rankings: 0.800
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYe, Jiachu-
dc.contributor.authorLai, Xiaoyan-
dc.contributor.authorWong, Gary Ka Wai-
dc.date.accessioned2024-03-11T10:38:49Z-
dc.date.available2024-03-11T10:38:49Z-
dc.date.issued2022-09-07-
dc.identifier.citationFrontiers in Psychology, 2022, v. 13-
dc.identifier.issn1664-1078-
dc.identifier.urihttp://hdl.handle.net/10722/339718-
dc.description.abstract<p>Students’ perceptions of learning are important predictors of their learning motivation and academic performance. Examining perceptions of learning has meaningful implications for instruction practices, while it has been largely neglected in the research of computational thinking (CT). To contribute to the development of CT education, we explored the influence of students’ perceptions on their motivation and performance in CT acquisition and examined the gender difference in the structural model using a multigroup structural equation modeling (SEM) analysis. Two hundred and eighty-five students from a Chinese urban high school were recruited for the study. The analysis revealed that students’ perceptions of CT positively influenced their CT performance and learning motivation, and some motivational constructs, namely self-efficacy and learning goal orientation (LGO), also positively influenced their CT performance. Furthermore, in the male student group, perceptions of CT exhibited significant correlations with both self-efficacy and LGO. However, no significant correlation was found in the female student group. Implications for research and teaching practice in CT education are presented herein.</p>-
dc.languageeng-
dc.publisherFrontiers Media-
dc.relation.ispartofFrontiers in Psychology-
dc.subjectcomputational thinking-
dc.subjectgender difference-
dc.subjectmotivation-
dc.subjectperception-
dc.subjectstructural equation model-
dc.titleA multigroup structural equation modeling analysis of students’ perception, motivation, and performance in computational thinking-
dc.typeArticle-
dc.identifier.doi10.3389/fpsyg.2022.989066-
dc.identifier.scopuseid_2-s2.0-85138499879-
dc.identifier.volume13-
dc.identifier.eissn1664-1078-
dc.identifier.isiWOS:000856253800001-
dc.identifier.issnl1664-1078-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats