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Impact of Inventive Design Education through the Correlation between Students’ Grades and Individual Talent

Part of: Design Education

Published online by Cambridge University Press:  26 July 2019

Niccolo Becattini  and
Gaetano Cascini

Abstract

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This paper aims at assessing the impact of inventive design education on students attending a class on Methods and Tools for Systematic Innovation. The study stems from the difficulty to understand how much personal inventive talent influences the final evaluation, especially in a context where students are asked to solve open problems, as conceptual design ones. To overcome the potential bias due to the individual talent, the authors propose to determine the impact of their teaching activity by means of an ex-ante/ex-post correlation analysis. Several cohorts of students along the years have been asked to solve some design problems at the beginning of the course, when no topics have been thought yet. An adapted creativity metrics enriched to map course contents measures the students' performance at the beginning ot the class (ex-ante). These results get correlated to the students' final grades (ex-post) in order to highlight areas where teaching has a stronger impact and those where talent remains predominant.

Keywords

Design educationCreativityConceptual design
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 529 - 538
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) 2019

References

Becattini, N., Borgianni, Y., Cascini, G. and Rotini, F. (2012), “Model and algorithm for computer-aided inventive problem analysis”. Computer-Aided Design, Vol. 44 No. 10, pp. 961–986.Google Scholar
Becattini, N. and Cascini, G. (2016), “Improving self-efficacy in solving inventive problems with TRIZ”. In Multidisciplinary Contributions to the Science of Creative Thinking, (pp. 195–213). Springer, Singapore.Google Scholar
Becattini, N., Cascini, G., O'Hare, J.A. and Masclet, C. (2018), “Coding schemes for the analysis of ICT supported co-creative design sessions”. In DS92: Proceedings of the DESIGN 2018 15th International Design Conference, (pp. 533–544).Google Scholar
Dorst, K. and Cross, N. (2001), “Creativity in the design process: co-evolution of problem–solution”. Design studies, Vol. 22 No. 5, pp. 425–437.Google Scholar
Fleiss, J.L. (1971), “Measuring nominal scale agreement among many raters”. Psychological bulletin, Vol. 76 No. 5, p. 378.Google Scholar
Fu, K., Cagan, J. and Kotovsky, K. (2010), “Design team convergence: the influence of example solution quality”. Journal of Mechanical Design, Vol. 132 No. 11, p. 111005.Google Scholar
Gero, J.S. and Kannengiesser, U. (2004), “The situated function–behaviour–structure framework”. Design studies, Vol. 25 No. 4, pp. 373–391.Google Scholar
Jia, L., Becattini, N., Cascini, G. and Tan, R. (2018), “Testing design stimuli for design-byanalogy on a large set of designers”. In DS 89: Proceedings of The Fifth International Conference on Design Creativity (ICDC 2018), University of Bath, Bath, UK, (pp. 39–46).Google Scholar
Kim, K.H. (2006), “Can we trust creativity tests? A review of the Torrance Tests of Creative Thinking (TTCT)”. Creativity research journal, Vol. 18 No. 1, pp. 3–14.Google Scholar
Landis, J.R. and Koch, G.G. (1977), “The measurement of observer agreement for categorical data”. Biometrics, pp. 159–174.Google Scholar
McCaffrey, T. and Krishnamurty, S. (2015), “The obscure features hypothesis in design innovation”. International Journal of Design Creativity and Innovation, Vol. 3 No. 1, pp. 1–28.Google Scholar
Saliminamin, S., Becattini, N. and Cascini, G. (2018), “Sources of creativity stimulation for designing the next generation of technical systems: correlations with R&D designers’ performance”. Research in Engineering Design, pp. 1–21.Google Scholar
Shah, J.J., Smith, S.M. and Vargas-Hernandez, N. (2003), “Metrics for measuring ideation effectiveness”. Design studies, Vol. 24 No. 2, pp. 111–134.Google Scholar
Srinivasan, V. and Chakrabarti, A. (2010), “Investigating novelty–outcome relationships in engineering design”. AI EDAM, Vol. 24 No. 2, pp. 161–178.Google Scholar