Hostname: page-component-8448b6f56d-42gr6 Total loading time: 0 Render date: 2024-04-23T21:39:56.607Z Has data issue: false hasContentIssue false
  • English
  • Français

Extracting and Analysing Design Process Data from Log Files of ICT Supported Co-Creative Sessions

Part of: Collaborative Design

Published online by Cambridge University Press:  26 July 2019

Niccolo' Becattini ,
Gaetano Cascini ,
Jamie Alexander O'Hare ,
Federico Morosi  and
Jean-Francois Boujut
Niccolo' Becattini*
Affiliation:
Politecnico di Milano;
Gaetano Cascini
Affiliation:
Politecnico di Milano;
Jamie Alexander O'Hare
Affiliation:
University of Bath;
Federico Morosi
Affiliation:
Politecnico di Milano;
Jean-Francois Boujut
Affiliation:
Grenoble Institute of Technology
*
Contact: Becattini, Niccolo, Politecnico di Milano, Mechanical Engineering, Italy, niccolo.becattini@polimi.it

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The observation of designers' behaviour in collaborative design activities and the analysis of protocols improved the understanding of how novel ideas emerge, what occurs among designers and, indirectly, what methods have a good impact on the outcomes. Yet, protocol analysis requires recording the design sessions, often in a simulated environment, thus introducing a bias in the observation. Moreover, the analysis takes up to 1000 times the duration of the observed design session. These limitations definitely hinder the scalability of this practice to large experiments in real operational environments.

This paper investigates the possibility to use the data collected in log files, automatically recorded during collaborative design sessions assisted by an ICT design support tool, as a means to extract relevant information about the design process and ultimately to infer insights about co-designers' cognition during the session. In this perspective, the paper proposes a set of metrics tailored to an Augmented Reality-based collaborative design tool. The study has been carried about by processing the data collected in 5 real case studies conducted in three different design companies.

Keywords

Human behaviour in designCollaborative designBig dataDesign informaticsDesign Protocol Analysis
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 129 - 138
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

Bannert, M., Sonnenberg, C., Mengelkamp, C. and Pieger, E. (2015), “Short-and long-term effects of students’ self-directed metacognitive prompts on navigation behavior and learning performance”. Computers in Human Behavior, Vol. 52, pp. 293–306.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
Becattini, N., Cascini, G. and Rotini, F. (2015), “An OTSM-TRIZ based framework towards the computer-aided identification of cognitive processes in design protocols”. In Design Computing and Cognition’14 (pp. 99–117). Springer, Cham.Google Scholar
Becattini, N., Borgianni, Y., Cascini, G. and Rotini, F. (2012), “Experiencing protocol analysis of computer-guided design tasks”. In DS 70: Proceedings of DESIGN 2012, the 12th International Design Conference, Dubrovnik, Croatia (pp. 1821–1830).Google Scholar
Bouabid, A., Lepreux, S. and Kolski, C. (2018), “Study on generic tangible objects used to collaborate remotely on RFID tabletops”. Journal on Multimodal User Interfaces, pp. 1–20.Google Scholar
Blessing, L. T. and Chakrabarti, A. (2009). DRM, a design research methodology. Springer Science & Business Media.Google Scholar
Carmel, E., Crawford, S. and Chen, H. (1992), “Browsing in hypertext: A cognitive study.” IEEE transactions on Systems, Man, and Cybernetics, Vol. 22, No. 5, pp. 865–884.Google Scholar
Catledge, L.D. and Pitkow, J.E., 1995. Characterizing browsing behaviors on the world-wide web. Georgia Institute of Technology.Google Scholar
Ferre, X., Villalba, E., Julio, H. and Zhu, H. (2017), September. “Extending Mobile App Analytics for Usability Test Logging”, In IFIP Conference on Human-Computer Interaction (pp. 114–131). Springer, Cham.Google Scholar
Greiff, S., Wüstenberg, S. and Avvisati, F. (2015), “Computer-generated log-file analyses as a window into students’ minds? A showcase study based on the PISA 2012 assessment of problem solving.” Computers & Education, Vol. 91, pp. 92–105.Google Scholar
Jiang, H. and Yen, C.-C. (2009), “Protocol analysis in design research: a review”. In Rigor and Relevance in Design: IASDR 2009, Seoul, Korea October 18–22, Seoul, pp. 147–156. International Association of Societies of Design Research.Google Scholar
Kan, J.W. and Gero, J.S. (2009), “Using the FBS ontology to capture semantic design information in design protocol studies”. In About: Designing. Analysing Design Meetings (pp. 213–229). CRC Press.Google Scholar
Kafai, Y. and Fields, D. (2012), “Connecting play: Understanding multimodal participation in virtual worlds”. In Proceedings of the 14th ACM international conference on Multimodal interaction (pp. 265–272). ACM.Google Scholar
Kornmann, J., Kammerer, Y., Anjewierden, A., Zettler, I., Trautwein, U. and Gerjets, P. (2016), “How children navigate a multiperspective hypermedia environment: The role of spatial working memory capacity”. Computers in human behavior, Vol. 55, pp. 145–158.Google Scholar
Krieter, P. and Breiter, A. (2018), “Analyzing mobile application usage: generating log files from mobile screen recordings”. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services (p. 9). ACM.Google Scholar
Landauer, M., Wurzenberger, M., Skopik, F., Settanni, G. and Filzmoser, P. (2018), “Dynamic log file analysis: An unsupervised cluster evolution approach for anomaly detection, computers & security, 79, pp. 94–116.Google Scholar
Lee, G., Eastman, C.M. and Zimring, C. (2003), “Avoiding design errors: a case study of redesigning an architectural studio”. Design Studies, Vol. 24 No. 5, pp. 411–435.Google Scholar
Lee, Y., 2018. “Using Self-Organizing Map and Clustering to Investigate Problem-Solving Patterns in the Massive Open Online Course: An Exploratory Study”. Journal of Educational Computing Research, p. 0735633117753364.Google Scholar
Patil, D.N. and Meshram, B.B. (2019), “Web Browser Analysis for Detecting User Activities”. In Recent Findings in Intelligent Computing Techniques (pp. 279–291). Springer, Singapore.Google Scholar
Peruzzini, M., Grandi, F. and Pellicciari, M. (2018), “How to analyse the workers’ experience in integrated product-process design”. Journal of Industrial Information Integration, Vol. 12, pp. 31–46.Google Scholar
Rahimian, F.P. and Ibrahim, R. (2011), “Impacts of VR 3D sketching on novice designers’ spatial cognition in collaborative conceptual architectural design”. Design Studies, Vol. 32, No. 3, pp. 255–291.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
Someren, M. V., Barnard, Y. F. and Sandberg, J. A. ((1994), The think aloud method: a practical approach to modelling cognitive processes. Academic Press.Google Scholar
Schulz, A.H. and Breiter, A., 2012, August. “Monitoring user patterns in school information systems using logfile analysis”. In IFIP Conference on Information Technology in Educational Management (pp. 94–103). Springer, Berlin, Heidelberg.Google Scholar
Snider, C., Škec, S., Gopsill, J.A. and Hicks, B.J. (2017), The characterisation of engineering activity through email communication and content dynamics, for support of engineering project management. Design Science, 3.Google Scholar
Theobald, M., Bellhäuser, H. and Imhof, M. (2018), “Identifying individual differences using log-file analysis: Distributed learning as mediator between conscientiousness and exam grades”. Learning and Individual Differences, Vol. 65, pp. 112–122.Google Scholar