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Technology, Knowledge and Learning
Erschienen in: Technology, Knowledge and Learning 1/2023

31.08.2021 | Original research

Modeling of Relationship of Personal and Affective Variables With Computational Thinking and Programming

verfasst von: Hatice Yildiz Durak, Mustafa Saritepeci, Aykut Durak

Erschienen in: Technology, Knowledge and Learning | Ausgabe 1/2023

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Abstract

Computational thinking skill is one of the basic skills required for every individual, such as reading and writing. For the development of CT, programming education is seen as the key. In the context of programming and CT relationship, it is very important to model individual characteristics and various affective variables with a holistic approach in the programming process. The purpose of this study is to determine and model the relationships of some individual characteristics, personal and affective variables for programming, with CT. One hundred and eighty-one middle school students participated in the implementation. As a result of the research, it was determined that there is a significant relationship between personal variables and attitude towards programming and interest in programming. On the other hand, it has been determined that there is no significant relationship between personal characteristics and self-efficacy for programming and the importance given to programming. In this model, the most influential predictor of attitude and interest towards programming was gender.
Vorheriger Artikel Exploring the Effects of Background Music on Real-Time Emotional Expressions, Performance, and Confusion Mediation in Middle School Students
Nächster Artikel Spelling Errors in Korean Students’ Constructed Responses and the Efficacy of Automatic Spelling Correction on Automated Computer Scoring

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Metadaten
Titel
Modeling of Relationship of Personal and Affective Variables With Computational Thinking and Programming
verfasst von
Hatice Yildiz Durak
Mustafa Saritepeci
Aykut Durak
Publikationsdatum
31.08.2021
Verlag
Springer Netherlands
Erschienen in
Technology, Knowledge and Learning / Ausgabe 1/2023
Print ISSN: 2211-1662
Elektronische ISSN: 2211-1670
DOI
https://doi.org/10.1007/s10758-021-09565-8

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