Combining Data-Driven Models and Expert Knowledge for Personalized Support to Foster Computational Thinking Skills
Sébastien Lallé, Özge Nilay Yalçın, Cristina Conati · 2021
Game-Design (GD) environments show promise in fostering Computational Thinking (CT) skills at a young age. However, such environments can be challenging to some students due to their highly open-ended nature. We propose to alleviate this difficulty by learning interpretable student models from data that can drive personalization of a real-world GD learning environment to the student’s needs. We apply our approach on a dataset collected in ecological settings and evaluate the ability of the generated student models at predicting ineffective learning behaviors over the course of the interaction. We then discuss how these behaviors can be used to define personalized support in GD learning activities, by conducting extensive interviews with experienced instructors.