Using Structural Equation Modeling to Examine the Relationship Between Preservice Teachers’ Computational Thinking Attitudes and Skills

Bibliographic Data

ID20294224
AuthorsMaria Cutumisu (0000-0003-2475-9647, University of Alberta), Catherine Adams (0000-0003-4413-0803, University of Alberta), Florence Glanfield (0000-0002-0228-5199, University of Alberta), Connie Yuen (0000-0002-9995-4503, University of Alberta), Chang Lu (0000-0001-6331-622X, University of Alberta)
Year2022
Volume65
Issue2
Pages177-183
Publication date2022-05-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueIEEE Transactions on Education (JOURNAL)
Journal identifiersISSN: 0018-9359 • E-ISSN: 1557-9638
PublisherInstitute of Electrical and Electronics Engineers (IEEE) (PUBLISHER)
DOI10.1109/te.2021.3105938
OpenAlexW3206410723
LanguageEN
Citations received2
References cited31

The growing interest of educational researchers in computational thinking (CT) has led to an expanding literature on assessments of CT skills and attitudes. However, few studies have examined whether CT attitudes influence CT skills. The present study examines the relationship between CT attitudes and CT skills for preservice teachers (PSTs). The Callysto CT test (CCTt) for Teachers was administered to $n\,\,=$ 105 PSTs to measure their CT attitudes and skills. Structural equation modeling was used to examine the relationship of participants’ CT and problem-solving skills with their attitudes toward CT, technology, coding, and data. Findings revealed that CT attitudes predicted CT skills and provided the first step in exploring the validity and reliability of the CCTt instrument

Arithmetic · Coding (social sciences) · Computational Thinking · Mathematics education · Notation · Reliability (semiconductor) · Statistics · Structural equation modeling · Test (biology) · Gender and Technology in Education · Mathematics · Online Learning and Analytics · Psychology · Teaching and Learning Programming

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