Using Eye-Tracking to Unveil Differences Between Kids and Teens in Coding Activities
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Computational thinking and coding is gradually becoming an important part of K-12 education. Most parents, policy makers, teachers, and industrial stakeholders want their children to attain computational thinking and coding competences, since learning how to code is emerging as an important skill for the 21st century. Currently, educators are leveraging a variety of technological tools and programming environments, which can provide challenging and dynamic coding experiences. Despite the growing research on the design of coding experiences for children, it is still difficult to say how children of different ages learn to code, and to cite differences in their task-based behaviour. This study uses eye-tracking data from 44 children (here divided into “kids” [age 8–12] and “teens” [age 13–17]) to understand the learning process of coding in a deeper way, and the role of gaze in the learning gain and the different age groups. The results show that kids are more interested in the appearance of the characters, while teens exhibit more hypothesis-testing behaviour in relation to the code. In terms of collaboration, teens spent more time overall performing the task than did kids (higher similarity gaze). Our results suggest that eye-tracking data can successfully reveal how children of different ages learn to code.