PREDICT – Prediction Generation as a Tool to Activate Children’s Prior Knowledge and Improve Learning

Project Description

The project PREDICT evaluates the potential of asking students to generate predictions to improve their learning. It further investigates the mechanisms that determine its success. More specifically, several plausible candidate mechanims are investigated and compared, including enhanced curiosity and surprise. Changes in these learning-related emotions induced by making a prediction are assessed using pupillometry. Furthermore, it is investigated whether there are age-related differences in the effectiveness of student-generated predictions for improving learning. The overarching goal of this project is to attain a better understanding of the mechanisms underlying the effectiveness of student-generated predictions. Knowledge of these mechanisms shall be used to guide testing of this method in real classrooms using technological devices.

Funding

till 12/2022: DFG and Jacobs Foundation

Cooperations

• Associate Prof. Dr. Elizabeth Bonawitz, Harvard University, Cambridge
• Prof. Dr. Silvia Bunge, University of California, Berkeley

Publications

Breitwieser, J. & Brod, G. (2021). Cognitive prerequisites for generative learning: Why some learning strategies are more effective than others. Child Development, 92(1), 258-272. doi:10.1111/cdev.13393

Brod, G., Breitwieser, J., Hasselhorn, M., & Bunge, S. (2020). Being proven wrong elicits learning in children - but only in those with higher executive function skills. Developmental Science, 23(3), e12916. doi:10.1111/desc.12916

Brod, G., Hasselhorn, M., & Bunge, S. (2018). When generating a prediction boosts learning: The element of surprise. Learning and Instruction, 55, 22-31. doi:10.1016/j.learninstruc.2018.01.013