posted by user: pixel || 5282 views || tracked by 7 users: [display]

FOE 2013 : The Future of Education - Extended Deadline for Abstracts Submission

FacebookTwitterLinkedInGoogle

Link: http://www.pixel-online.net/foe2013/
 
When Jun 13, 2013 - Jun 14, 2013
Where Florence, Italy
Submission Deadline Feb 28, 2013
Categories    media education, e-learning   learning games   studies on education   education and new technologies
 

Call For Papers

Extended Deadline for Abstracts Submission - 28 February 2013

The Call for Papers, within the Future of Education Conference, is addressed to teachers, researchers and experts in the field of education as well as to coordinators of education and training projects.
Experts in the field of teaching and learning are therefore invited to submit an abstract of a paper to be presented during the Future of Education International Conference. The abstract should be written in English (between 200 and 500 words) and sent via e-mail to foe@pixel-online.net no later than 28 February 2013.

Related Resources

FOE 2026   FOE 2026 | The Future of Education 16th Edition - International Conference
Blockchain, FL, and IoMT in eHealth 2026   Exploring the Nexus of Blockchain, Federated Learning, and IoMT in Healthcare: Unveiling Convergence and Future Trajectories
EAIT--EI 2026   2026 The 7th International Conference on Education and Artificial Intelligence Technologies (EAIT 2026)
IJE 2026   International Journal of Education
IJITE 2026   International Journal on Integrating Technology in Education
EAIT 2026   2026 The 7th International Conference on Education and Artificial Intelligence Technologies (EAIT 2026)
IECT 2026   2026 3rd International Conference on Intelligent Education and Computer Technology-EI/Scopus
DSML 2026   7th International Conference on Data Science and Machine Learning
Language Education 2026 Online 2026   International Thematic Conference on Language Education Research and Applications
VSI - Numbers of the Future 2027   Special Issue on Numbers of the Future - Uncertain Data for Quantitative Risk Analysis