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Pixel Deep v Deep Learning: exploring an alternative design research approach as an intrinsic pedagogical model for an enhanced student learning experience in the design process within graphic design

Clancy, Pauline and McElveen, Richard (2016) Pixel Deep v Deep Learning: exploring an alternative design research approach as an intrinsic pedagogical model for an enhanced student learning experience in the design process within graphic design. In: Graphic Design Educators' Network. Exploring Territories: mapping the terrain of graphic design education, Cardiff School of Art and Design, Cardiff Metropolitan University, Wales. .. [Conference contribution]

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Abstract

Everyone is a Designer. It could be argued that graphic design software and technology has demystified and democratized the design process and outcomes. For students it can be enticing to create 'pixel deep’ work that ‘looks good’ but is void of solid underpinning or rigorous thought process. Limited observing, thinking or researching beyond the internet realm where ideas are dying out in favor of visual seduction. This paper suggests that within graphic design education, there is a need for a more rigorous learning and teaching approach to enable students to understand and develop a non-formulaic design process. An alternative approach where there is a deeper investigation of the subject, a more rigorous research methodology and introducing play and experimentation into the design process to create rich, exciting and meaningful outcomes. This paper will discuss this approach and demonstrate the effectiveness on the student learning experience and project outcomes.

Item Type:Conference contribution (Keynote)
Keywords:graphic design education, design process, design pedagogy, design detectives, pixel deep, deep learning
Faculties and Schools:Faculty of Art, Design and the Built Environment > Belfast School of Art
Faculty of Art, Design and the Built Environment
Research Institutes and Groups:Art and Design Research Institute > Space and Place
Art and Design Research Institute
ID Code:37293
Deposited By: Ms Pauline Clancy
Deposited On:25 Apr 2017 08:06
Last Modified:17 Oct 2017 16:28

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