Towards Teaching Abstraction: Approaching Modeling and Problem-Solving Skills with Graph Grammars and Game-Based Learning

Abstract

Computing education has risen and evolved, conquering new spaces and acquiring recognition from governments and institutions around the world. Beyond the con sistent and increasing demand for professionals in Information and Communications Technology, computing pervasiveness called for its introduction in basic education, for all citizens. Powerful education trends, such as Computational Thinking (CT), problem and project-based learning, maker’s culture, gamification and educational games progressed powered by the advance of computing. A set of problem-solving skills based on computing is how CT was conceptualized and gained popularty. However, the difficulty in defining and treating those skills, such as abstraction, algorithmic thinking, decomposition and pattern recognition has made CT manifest mostly through programming. This reignites an old concern, computing education has struggled to demystify being equated to programming. Computing being far more than the technical competence to code is the main argument for its introduction in general education. And this is important even for programming, which should be a step deeper into computing, not the first. In this regard, this work is an effort to advance the knowledge and operationability of CT, treating specifically abstraction, centered on the iconic form it presents itself in computing: the layers of abstraction. The proposed approach revolves around using Graph Grammars (GG) to specify and play "graph games" (grames, for short), which is the concept of "graming". It includes the development of: an educational game engine based on GGs; tools to manage layers of abstraction based on Hierarchical GG; and an assessment grame to evaluate competencies related to layers of abstraction, under the principles of the Evidence-Centered Design (ECD), guiding the creation of proficiency, task and evidence models. This whole ecosystem showed that it is possible to approach deep, fundamental and abstract skills, such as abstraction itself, as solid psychometric constructs to be reliably operationalized while preserving creative and engaging environments. This work also opened up a wide range of future applications and investigations when it brought GG from its formal specification origins to game-based learning in basic education. At last, the engine reinforces the emerging potential of educational tools when powered by AI.