PREDICT – Prediction Generation as a Tool to Activate Children’s Prior Knowledge and Improve Learning
This project evaluates the potential of asking students to generate predictions to improve their learning. Further, it investigates the mechanisms that determine its success and asks whether there are age-related differences in its effectiveness.
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.
- 01/2019 till 12/2021: Jacobs Foundation
- 10/2019 till 09/2022: DFG (PREDICT I)
- 10/2022 till 09/2024: DFG (PREDICT II)
- DIPF
- Prof. Dr. Elizabeth Bonawitz, Harvard University, Cambridge
- Associate Prof. Dr. Dietsje Jolles, Leiden University, The Netherlands
Status: Current projectAreas of focus Department: Education and Human Development Unit: Individualised Interventions Education Sectors: Higher Education, Primary and Secondary Education Duration: 01/2017 – 07/2026Funding: External fundingContact: Dr. Lucas Lörch, Academic Staff
