On the predictors of computational thinking and its growth at the high-school level

Abstract Computational thinking (CT) is a key 21st-century skill. This paper contributes to CT research by providing a comprehensive picture of CT predictors in a longitudinal and natural classroom setting among upper secondary students. The hypothesized predictors are grouped into three areas: student characteristics, home environment, and learning opportunities. CT is measured with the Computational Thinking Test (CTt), an established performance test. N = 202 high-school students, at three time points over one school year, act as the sample and latent growth curve modeling as the analysis method. CT self-concept, followed by reasoning skills and gender, show the strongest association with the level of CT. Computer literacy, followed by duration of computer use and formal learning opportunities during the school year, have the strongest association with CT growth. Overall, variables from all three areas seem to be important for predicting either CT level or growth. An explained variance of 70.4% for CT level and 61.2% for CT growth might indicate a good trade-off between the comprehensiveness and parsimony of the conceptual framework. Our findings contribute to a better understanding of CT as a construct and have implications for instruction, e.g., the role of computer science and motivation in CT learning.