Feature Engineering for Design Thinking Assessment

Ryan Arlitt; Sumbul Khan; Lucienne Blessing

doi:10.1017/dsi.2019.39

Feature Engineering for Design Thinking Assessment

Ryan Arlitt^*
, Sumbul Khan
, Lucienne Blessing

^*Corresponding author for this work

Singapore University of Technology and Design

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

271 Downloads (Orbit)

Abstract

As design and design thinking become increasingly important competencies for a modern workforce, the burden of assessing these fuzzy skills creates a scalability bottleneck. Toward addressing this need, this paper presents an exploratory study into a scalable computational approach for design thinking assessment. In this study, student responses to a variety of contextualized design questions – gathered both before and after participation in a design thinking training course – are analyzed. Specifically, a variety of text features are engineered, tested, and interpreted within a design thinking framework in order to identify specific markers of design thinking skill acquisition. Key findings of this work include
identification of text features that may enable scalable measurement of (1) user-centric language and (2) design thinking concept acquisition. These results contribute toward the creation of computational tools to ease the burden of providing feedback about design thinking skills to a wide audience.

Original language	English
Title of host publication	Proceedings of the Design Society: 22nd International Conference on Engineering Design (ICED19)
Editors	Sandro Wartzack, Benjamin Schleich
Publisher	Cambridge University Press
Publication date	2019
Pages	3891-3900
DOIs	https://doi.org/10.1017/dsi.2019.39
Publication status	Published - 2019
Event	22nd International Conference on Engineering Design (ICED19) - Delft, Netherlands Duration: 5 Aug 2019 → 8 Aug 2019

Conference

Conference	22nd International Conference on Engineering Design (ICED19)
Country/Territory	Netherlands
City	Delft
Period	05/08/2019 → 08/08/2019

Series	Proceedings of the International Conference on Engineering Design
Number	DS94
ISSN	2220-4342

Bibliographical note

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.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 8 Decent Work and Economic Growth
SDG 12 Responsible Consumption and Production

Keywords

Circular economy
Sustainability
Business models and considerations
Process model

Access to Document

10.1017/dsi.2019.39

FulltextFinal published version, 656 KB

OpenUrl availability

Check availability in DTU Findit

Cite this

Arlitt, Ryan ; Khan, Sumbul ; Blessing, Lucienne. / Feature Engineering for Design Thinking Assessment. Proceedings of the Design Society: 22nd International Conference on Engineering Design (ICED19). editor / Sandro Wartzack ; Benjamin Schleich. Cambridge University Press, 2019. pp. 3891-3900 (Proceedings of the International Conference on Engineering Design; No. DS94).

@inproceedings{e3ae8f530f9143a189c5c64a2dced31c,

title = "Feature Engineering for Design Thinking Assessment",

abstract = "As design and design thinking become increasingly important competencies for a modern workforce, the burden of assessing these fuzzy skills creates a scalability bottleneck. Toward addressing this need, this paper presents an exploratory study into a scalable computational approach for design thinking assessment. In this study, student responses to a variety of contextualized design questions – gathered both before and after participation in a design thinking training course – are analyzed. Specifically, a variety of text features are engineered, tested, and interpreted within a design thinking framework in order to identify specific markers of design thinking skill acquisition. Key findings of this work include identification of text features that may enable scalable measurement of (1) user-centric language and (2) design thinking concept acquisition. These results contribute toward the creation of computational tools to ease the burden of providing feedback about design thinking skills to a wide audience.",

keywords = "Circular economy, Sustainability, Business models and considerations, Process model",

author = "Ryan Arlitt and Sumbul Khan and Lucienne Blessing",

note = "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.; 22nd International Conference on Engineering Design (ICED19) ; Conference date: 05-08-2019 Through 08-08-2019",

year = "2019",

doi = "10.1017/dsi.2019.39",

language = "English",

series = "Proceedings of the International Conference on Engineering Design",

publisher = "Cambridge University Press",

number = "DS94",

pages = "3891--3900",

editor = "Sandro Wartzack and Benjamin Schleich",

booktitle = "Proceedings of the Design Society: 22nd International Conference on Engineering Design (ICED19)",

address = "United Kingdom",

}

Arlitt, R, Khan, S & Blessing, L 2019, Feature Engineering for Design Thinking Assessment. in S Wartzack & B Schleich (eds), Proceedings of the Design Society: 22nd International Conference on Engineering Design (ICED19). Cambridge University Press, Proceedings of the International Conference on Engineering Design, no. DS94, pp. 3891-3900, 22nd International Conference on Engineering Design (ICED19), Delft, Netherlands, 05/08/2019. https://doi.org/10.1017/dsi.2019.39

Feature Engineering for Design Thinking Assessment. / Arlitt, Ryan; Khan, Sumbul; Blessing, Lucienne.
Proceedings of the Design Society: 22nd International Conference on Engineering Design (ICED19). ed. / Sandro Wartzack; Benjamin Schleich. Cambridge University Press, 2019. p. 3891-3900 (Proceedings of the International Conference on Engineering Design; No. DS94).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - Feature Engineering for Design Thinking Assessment

AU - Arlitt, Ryan

AU - Khan, Sumbul

AU - Blessing, Lucienne

N1 - 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.

PY - 2019

Y1 - 2019

N2 - As design and design thinking become increasingly important competencies for a modern workforce, the burden of assessing these fuzzy skills creates a scalability bottleneck. Toward addressing this need, this paper presents an exploratory study into a scalable computational approach for design thinking assessment. In this study, student responses to a variety of contextualized design questions – gathered both before and after participation in a design thinking training course – are analyzed. Specifically, a variety of text features are engineered, tested, and interpreted within a design thinking framework in order to identify specific markers of design thinking skill acquisition. Key findings of this work include identification of text features that may enable scalable measurement of (1) user-centric language and (2) design thinking concept acquisition. These results contribute toward the creation of computational tools to ease the burden of providing feedback about design thinking skills to a wide audience.

AB - As design and design thinking become increasingly important competencies for a modern workforce, the burden of assessing these fuzzy skills creates a scalability bottleneck. Toward addressing this need, this paper presents an exploratory study into a scalable computational approach for design thinking assessment. In this study, student responses to a variety of contextualized design questions – gathered both before and after participation in a design thinking training course – are analyzed. Specifically, a variety of text features are engineered, tested, and interpreted within a design thinking framework in order to identify specific markers of design thinking skill acquisition. Key findings of this work include identification of text features that may enable scalable measurement of (1) user-centric language and (2) design thinking concept acquisition. These results contribute toward the creation of computational tools to ease the burden of providing feedback about design thinking skills to a wide audience.

KW - Circular economy

KW - Sustainability

KW - Business models and considerations

KW - Process model

U2 - 10.1017/dsi.2019.39

DO - 10.1017/dsi.2019.39

M3 - Article in proceedings

T3 - Proceedings of the International Conference on Engineering Design

SP - 3891

EP - 3900

BT - Proceedings of the Design Society: 22nd International Conference on Engineering Design (ICED19)

A2 - Wartzack, Sandro

A2 - Schleich, Benjamin

PB - Cambridge University Press

T2 - 22nd International Conference on Engineering Design (ICED19)

Y2 - 5 August 2019 through 8 August 2019

ER -

Feature Engineering for Design Thinking Assessment

Abstract

Conference

Bibliographical note

UN SDGs

Keywords

Access to Document

OpenUrl availability

Fingerprint

Cite this