A Framework for Deep Learning for Teacher Education
Byron Havard, Jianxia Du, Mississippi State University, United States
Society for Information Technology & Teacher Education International Conference, in Atlanta, GA, USA ISBN 978-1-880094-52-5 Publisher: Association for the Advancement of Computing in Education (AACE), Chesapeake, VA
A framework for deep learning in distance education is illustrated in this paper. This framework emphasizes teaching and learning online for teacher education and addresses the transfer of surface to deep learning. The foundation of the framework is based on three general processes: methods, information, and cognition. Deeper learning is encouraged through innovative and ill-structured problems students solve in a technology rich learning environment. As students explore and interpret these problems based on their surface understanding, a deeper understanding evolves. The surface understanding is described as adoptive in nature. Deep learning, developed through a deeper understanding, is adaptive and may be applied to a variety of novel situations and complex problems. The framework was applied to a distance education graduate level course in educational technology for teacher education. Learning technologies employed were mapped onto learning processes within the framework.
Havard, B. & Du, J. (2004). A Framework for Deep Learning for Teacher Education. In R. Ferdig, C. Crawford, R. Carlsen, N. Davis, J. Price, R. Weber & D. Willis (Eds.), Proceedings of SITE 2004--Society for Information Technology & Teacher Education International Conference (pp. 463-469). Atlanta, GA, USA: Association for the Advancement of Computing in Education (AACE).