Computational thinking (CT) is a fundamental ability for learners in today's society. Although CT assessments and interventions have been studied widely, little is known about CT predictions. This study predicted students' CT achievement in the ICILS 2018 using five machine learning models. These models were trained on the data from five European countries and then tested on the Korean and the Danish sample, respectively. Results indicate that the models trained on the individualistic-European data were generalizable to the individualistic European country, Denmark, but not to the collectivistic Asian country, Korea. This study fills a void in the CT literature and highlights the importance of considering the contextual relevance of data sources when making algorithmic predictions.