Measuring Children’s Computational Thinking and Problem-Solving in a Block-Based Programming Game - Kayla Teng; Gregory K. W. K. Chung (Education Sciences)
Computational thinking (CT) in young children (K to three) has been of much interest among educational researchers due to the applicability of CT to solving problems in daily life and various academic disciplines. This study uses existing data from children’s gameplay in a block-based programming game called codeSpark Academy to examine the extent to which we can use children’s gameplay behavior to measure their CT and, more generally, their problem-solving skills. The objectives of the study are to operationalize CT and problem-solving constructs using gameplay data, investigate the relationship between CT and problem-solving, and position codeSpark Academy as a valid assessment tool. A total of 72 elementary students (aged 6–9) played codeSpark Academy once a week for six weeks. TechCheck, an externally developed and validated measure of CT, was administered before the first game day and after the last game day. Using fine-grained, moment-to-moment gameplay data, we developed and validated seven game-based indicators (GBIs) of CT using correlational analysis and nonparametric tests and integrated them into a problem-solving framework. Our findings showed that children’s gameplay behavior can be used to measure their CT and problem-solving skills.