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The European project entitled BIG GAME: Immersive and Multidisciplinary STEM Learning through a Cooperative Story-Driven Game was written and coordinated by the University of Turku (Finland) in cooperation with Pixel, also supporting the coordination of the planning process. The project was funded by the European Commission in the framework of the Erasmus+ Programme, KA2 - Cooperation Partnerships in School Education. The project aims at promoting interest and excellence in science, technology, engineering, and mathematics (STEM) through multidisciplinary and problem-solving learning in a form of a serious learning game.
Funding Programme
Erasmus+ Programme, KA2 - Cooperation Partnerships in School Education
Theme
STEM
Target group
Higher Education, School Education
Project Reference
2021-1-FI01-KA220-SCH-000024098
Date
01-11-2021 | 01-05-2024
Role of Pixel
Partner
Project Details
Project Title
Immersive and Multidisciplinary STEM Learning through a Cooperative Story-Driven Game
Objectives
The BIG GAME project addresses the three common needs identified at the European level, which are:
- to promote the STEM approach in learning
- to support digital transformation
- to support the fight against climate change.
First, as interest in studying science subjects has declined across Europe, according to research findings, there is a perceived Europe-wide recognition of the need to promote and increase the STEM approach in teaching.
Activities
The project activities will take place from December 2021 to May 2024 for 30 months, including the organisation of transnational project meetings, multiplier events and the piloting phase to test the methodology and the learning scenarios produced in Finland, Estonia, Italy and Romania.
Target Group
The direct participants are teachers from secondary school with 11-16 years old students coming from Finland, Estonia, Italy and Romania.
Results
- The BIG GAME Learning Concept and Model
- Handbook and Toolkit on Digital Storytelling approach in STEM
- Digital Bank of Environmental STEM learning objects