Informatics in Education

The development of students’ computational thinking (CT) skills is a crucial aspect in today’s world. We used 12 Bebras-based items mapped to combinations of four CT components (abstraction, algorithmic thinking, pattern recognition, decomposition) among 848 Estonian lower-secondary students. Overall, in this sample, girls outperformed boys, and ninth graders were overrepresented among High Achievers. However, these grade-level and gender differences should be interpreted cautiously because the ninth graders’ subgroup was comparatively small and gender-imbalanced. The k-means analysis identified distinct patterns of performance across CT tasks. Because each task involved multiple CT components, the resulting clusters were interpreted as learner profiles rather than as indicators of specific CT subskills. Cluster membership was significantly associated with two single-item self-report indicators of IT-related intentions: High Achievers reported the greatest likelihood of IT studies/careers. Findings suggest CT competence (especially mastery across interdependent subskills) co-occurs with, but does not causally predict, higher self-reported intentions to pursue IT studies and careers.

Computer science concepts have an important part in other subjects and thinking computationally is being recognized as an important skill for everyone, which leads to the increasing interest in developing computational thinking (CT) as early as at the comprehensive school level. Therefore, research is needed to have a common understanding of CT skills and develop a model to describe the dimensions of CT. Through a systematic literature review, using the EBSCO Discovery Service and the ACM Digital Library search, this paper presents an overview of the dimensions of CT defined in scientific papers. A model for developing CT skills in three stages is proposed: i) defining the problem, ii) solving the problem, and iii) analyzing the solution. Those three stages consist of ten CT skills: problem formulation, abstraction, problem reformulation, decomposition, data collection and analysis, algorithmic design, parallelization and iteration, automation, generalization, and evaluation.