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

Pauline Clancy, Richard McElveen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherUnknown Publisher
Number of pages0
Publication statusAccepted/In press - 8 Sept 2016
EventGraphic Design Educators' Network. Exploring Territories: mapping the terrain of graphic design education - Cardiff School of Art and Design, Cardiff Metropolitan University, Wales
Duration: 8 Sept 2016 → …
http://www.graphicdesigneducators.network/exploring-territories/

Conference

ConferenceGraphic Design Educators' Network. Exploring Territories: mapping the terrain of graphic design education
Period8/09/16 → …
Internet address

Keywords

  • graphic design education
  • design process
  • design pedagogy
  • design detectives
  • pixel deep
  • deep learning

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