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USING LIKELIHOOD RATIO TABLE AND NAÏVE BAYES CLASSIFIER METHOD TO HOLISTICALLY ASSESS CODESIGN PROGRAMMES AND METHODS

Published online by Cambridge University Press:  19 June 2023

William Siew,
Arlindo Silva and
Bina Rai
William Siew*
Affiliation:
Singapore University of Technology and Design;
Arlindo Silva
Affiliation:
Singapore University of Technology and Design;
Bina Rai
Affiliation:
National University of Singapore
*
Siew, William, Singapore University of Technology and Design, Singapore, william_siew@mymail.sutd.edu.sg

Abstract

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The concept of inclusivity involved an understanding of people, programmes and places, embedded with complex issues. 21 student designers took part in a first-of-its-kind five-day codesign programme to develop solutions for inclusive and engaged communities with residents. This quasi-experimental study aimed to develop a value-based approach using likelihood ratio table and a Naïve Bayes classifier method to assess the success of a codesign programme, in comparison to past programmes with different design challenges. Methodology proposed a systematic investigation to evaluate this programme holistically. Students discussed with stakeholders to uncover the complexities of human and environmental factors in design at early stage of ideation, and semi-structured participants’ observation tasks were considered instead of researcher's observations in the method of assessment. Selected teams were introduced to two new design methods to empathise better with seniors, i.e., Care Circle and See and Shoot. Findings revealed that these teams showed greater levels of critical inquiry when overcoming three key challenges, i.e., (1) identifying key personas, (2) examining potential use environment, and (3) access to market.

Keywords

Bayesian approachComplexityDesign educationDesign methodsInclusivity

Information

Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023, pp. 3751 - 3760
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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