Toward a Learning Game on Computational Thinking Driven by Competencies

This paper examines whether learning games can help French teachers deliver computational thinking (CT) instruction in primary school, a task for which many educators lack pre-service and in-service training. The authors argue that while teachers can understand the competencies set out in official referentials, they often lack guidance on how to build teaching sessions or select appropriate tools. Their proposed remedy is a learning game explicitly structured around competencies, which would let teachers assemble tailored activities. The work is grounded in the PIAF (Pensée Informatique et Algorithmique dans l'enseignement Fondamental) framework, which organizes CT into six competencies broken into 26 sub-competencies for elementary education.

The authors analysed three learning games selected from a pool of 48 identified CT games: Blockly Maze, Compute-it, and Kodu. Each was mapped against the PIAF sub-competencies. Blockly Maze covered 17 of 26, Kodu 16, and Compute-it only 4, with sequence-construction and iterative-testing competencies (particularly C6) being poorly represented across the set. The paper also finds that none of the 48 games explicitly documents how its mechanics correspond to targeted competencies, and that some games address concepts outside the PIAF scope, such as event-driven programming and recursion.

The core contribution is a design proposal for SPY, an open-source maze game in which players program a robot using block-based actions. The authors devised 22 gameplay features mapped to PIAF competencies, such as naming robots and containers, painting robots to pass colour-coded doors, and saving custom action sequences as icons. They also identified compatibility constraints between competencies and features, showing which combinations are required, forbidden, or mutually exclusive, which would govern how teachers can combine them. The work is positioned as the first two phases of a didactic engineering methodology; empirical classroom observation and evaluation remain future work.