Formalizing Problem Solving in Computational Thinking : an Ontology approach

Chloé Mercier (1) , Lisa Roux (2) , Margarida Romero (2) , Frédéric Alexandre (1) , Thierry Viéville (1, 2)

1 Mnemosyne - Mnemonic Synergy
2 LINE - Laboratoire d'Innovation et Numérique pour l'Education

We introduce the idea of a symbolic description of a complex human learning task, in order to contribute to better understand how we learn. The learner is modeled on the basis of knowledge from learning sciences with the contribution of cognitive neurosciences, including machine learning formalism, in the very precise framework of a task, named #CreaCube reviewed here, related to initiation to computational thinking presented as an open-ended problem, which involves solving a problem and appealing to creativity. We target problem-solving tasks using tangible interfaces for computational thinking initiation, and describe in details how we model the task and the learner behavior in this task, including goal-driven versus stimulus-driven behavior and the learner knowledge construction. We show how formalizing these elements using an ontology offers a well-defined computational model and the possibility of inferences about model elements, analyzing and predicting the learner behavior. This operationalization of a creative problem-solving task is still at a preliminary stage, but an effective proof of concept is described in this study.

Mots clés: Cognitive Neuroscience; Ontology; Problem Solving; Learning Sciences; Computational Thinking

Domaines: Intelligence artificielle [cs.AI]; Neurosciences; Education

Chloé Mercier, Lisa Roux, Margarida Romero, Frédéric Alexandre, Thierry Viéville. Formalizing Problem Solving in Computational Thinking : an Ontology approach. IEEE ICDL 2021 – International Conference on Development and Learning 2021, Aug 2021, Beijing, China. ⟨hal-03324136⟩