GAMEFUL. Videogame-based Assessment of Executive Functions through machine Learning

Abstract / Presentazione del progetto:

In the present project we focus on personalized videogame-based assessment of Executive Functions (EFs). In particular, the project is aimed at developing a working tool prototype that, using Machine Learning (ML) and Artificial Intelligence (AI) in the scope of Learning Analytics (LA), can identify patterns of EF functioning by using the data that children generate through their participation in a videogame-based training of EFs. The experimented approach is included in precision education, which is concerned with tailoring educational practices to individuals on the basis of their specific characteristics and functioning.

Obiettivi del progetto

(1) to identify children’s profiles of EF functioning by analyzing children’s performance when interacting with a planning task of a videogame-based EF training;

(2) to identify trajectories of change in planning skills across the two months during which children will interact repeatedly with the planning task.

Durata prevista del progetto:

•  11/2023 - 11/2025

Settori ERC di riferimento:

• SH4_1 Cognitive basis of human development and education, developmental disorders; comparative cognition 2.

Responsabile / coordinatore scientifico:

• Viterbori Paola - paola.viterbori@unige.it

Collaboratori / gruppo di ricerca:

• Chiara Pecini   / Responsabile unità di ricerca FORLILPSI