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

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.

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.

Cooperations

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

Project Management

Project Team

Jasmin Breitwieser

Project Details

State:
Current projects
Duration:
01/2017 – 12/2020
Funding:
DIPF
Department: Education and Human Development
last modified Jan 15, 2018

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