Deep Learning Design for Sustainable Innovation within Shifting Learning Landscapes

Changes in the underpinning technologies for TEL is occurring at a pace that we have never before experienced, and this is unlikely to slow down. This necessitates a broader and more profound understanding of design that needs to be more future-proof than relying on the latest or emerging technologies and yet embraces the collaborative, multimodal and ubiquitous nature of learning in 21C. In addressing this challenge this article develops, exemplifies and tests the approach of Deep Learning Design (DLD), which has led to relatively large-scale and sustainable innovations and also outlined clear directions for near-future developments. Specifically, in this article we: justify why DLD is necessary and describe its key principles; exemplify these principles through four TEL initiatives; and, draw some implications and conclusions from across these projects about DLD and future learning.

• Type: Conference
• Year: 2010
• Where: ECTEL
• Authors: Andrew Ravenscroft, Tom Boyle, John Cook, Andreas Schmidt
• Keywords: Deep Learning Design, ECTEL 2010, Machine Learning, Profound Understanding, Underpinning Technologies