Fostering computational thinking through robotics in kindergarten: A systematic review of learning strategies, frameworks, and assessment

Abstract

Computational Thinking (CT) is considered a core 21st-century skill that should be developed early. Educational robotics has been introduced to kindergarten settings to foster the CT learning process. However, there is no systematic review documenting the learning strategies and CT frameworks employed for CT development in kindergarten through robotics. Similarly, there is no updated presentation of the examined CT skills at this level. Furthermore, there is no up-to-date systematic record and classification of the CT assessment tools used to measure CT skills specifically in the kindergarten context. The existing reviews of CT assessment examine broad educational settings ranging from kindergarten through higher education, and do not focus exclusively on educational robotics. Therefore, this systematic review analyses 53 out of 2306 studies published from 2015 to March 2026 across 7 databases, examining robot-mediated CT development in kindergarten. It explored the learning strategies, CT frameworks, CT skills, and assessment tools employed. Our findings indicate that problem-based learning, storytelling, and scaffolding are the most frequently employed strategies in robot-mediated CT learning. In the reviewed articles, four CT frameworks were identified, although most of the studies did not specify any framework. CT concepts and practices are most frequently examined, while CT perspectives remain underexplored. Total CT, sequencing, debugging, and algorithmic design are the most assessed CT skills. Regarding CT assessment tools, TechCheck-K is the most widely used, although in many cases, the assessment tools are developed ad hoc and lack formal validation.