PREDICT

Project PREDICT

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

This project evaluates the potential of predictions generated by students 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 predictions generated by students to improve their learning. It further investigates the mechanisms that determine its success. More specifically, several plausible mechanims are investigated and compared, including enhanced curiosity and surprise. Changes in these learning-related emotions induced by generated predictions are assessed by measuring pupil reactivity. 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 classroom settings using technological devices.

Cooperation

Prof. Dr. Silvia Bunge, University of California, Berkeley