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Computational Thinking Assessment – Towards More Vivid Interpretations

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dc.contributor.authorGuggemos, Josef
dc.contributor.authorSeufert, Sabine
dc.contributor.authorRomán González, Marcos
dc.date.accessioned2026-02-11T07:51:08Z
dc.date.available2026-02-11T07:51:08Z
dc.date.issued2023-06-01
dc.descriptionThe registered version of this article, first published in “Tech Know Learn 28, 2023", is available online at the publisher's website: Springer, https://doi.org/10.1007/S10758-021-09587-2
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en “Tech Know Learn 28, 2023", está disponible en línea en el sitio web del editor: Springer, https://doi.org/10.1007/S10758-021-09587-2
dc.description.abstractComputational thinking (CT) is an important 21st-century skill. This paper aims at more useful CT assessment. Available evaluation instruments are reviewed; two generally accepted CT evaluation tools are selected for a comprehensive CT assessment: the CTt, a performance test, and the CTS, a self-assessment instrument. The sample comprises 202 high school students from German-speaking Switzerland. Concerning the CTt, Rasch-scalability is demonstrated. Utilizing the approach of the PISA studies, proficiency levels are formed that comprise tasks with specific characteristics that students are systematically able to master. This could help teachers to offer individual support to their students. In terms of the CTS, the original version is refined using confirmatory factor and measurement-invariance analysis. A latent profile analysis yielded four profiles, two of which are of particular interest. One profile comprises students with, on the one hand, moderate to high creative thinking ability, cooperativity, and critical thinking skills and, on the other hand, low algorithmic thinking ability. The second remarkable profile consists of students with particularly low cooperativity. Based on these strength and weakness profiles, teachers could offer support tailored to student needs.en
dc.description.provenanceMade available in DSpace on 2026-02-11T07:51:08Z (GMT). No. of bitstreams: 1 Roman Gonzalez_Marcos_Postprint.pdf: 389057 bytes, checksum: 02fdd8e510e40868afc04b8a31683650 (MD5) Previous issue date: 2023-06-01en
dc.description.versionversión final
dc.identifier.citationGuggemos, J., Seufert, S. & Román-González, M. Computational Thinking Assessment – Towards More Vivid Interpretations. Tech Know Learn 28, 539–568 (2023). https://doi.org/10.1007/s10758-021-09587-2
dc.identifier.doihttps://doi.org/10.1007/S10758-021-09587-2
dc.identifier.eissn2211-1670
dc.identifier.issn2211-1662
dc.identifier.urihttps://hdl.handle.net/20.500.14468/31792
dc.journal.titleTechnology, Knowledge and Learning
dc.journal.volume28
dc.language.isoen
dc.page.final568
dc.page.initial539
dc.publisherSpringer
dc.relation.centerFacultad de Educación
dc.relation.departmentMétodos de Investigación y Diagnóstico en Educación I
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject58 Pedagogía
dc.subject.keywordscomputational thinkingen
dc.subject.keywordsperformance testen
dc.subject.keywordsitem response theoryen
dc.subject.keywordslatent profile analysisen
dc.subject.keywordsperson-centered assessmenten
dc.subject.keywordsproficiency level modelen
dc.titleComputational Thinking Assessment – Towards More Vivid Interpretationsen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
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relation.isAuthorOfPublication.latestForDiscoveryf326c028-97b6-4d2b-bfdf-25491ee66c8a
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