Abstract
Uneven workload distributions in teams can lead to suboptimal team performance. This paper therefore describes the design of a virtual assistant that supports workload harmonization in teams by measuring workload, informing team members about their own and other team members’ workload, and supporting team members in the redistribution of workload. The virtual assistant was developed in the context of train traffic control according to the situated Cognitive Engineering (sCE) methodology, which was extended to allow for a value sensitive design process. More specifically, the values ‘insight,’ ‘helping others’ and ‘privacy’ were explicitly accounted for throughout the design of the virtual assistant. A prototype of the virtual assistant was evaluated positively in a focus group. Thereby, the contribution of the paper is twofold. First, an improvement in a human-centered development methodology—sCE with values—is described and its use is demonstrated in an actual design case. Second, a novel, positively evaluated solution for workload harmonization in teams is presented.
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Notes
The clause “if permitted” was added to preserve privacy (see objective 3).
References
Anderson JR (2014) Rules of the mind. Psychology Press, Routledge
Bordini RH, Dastani M, El Seghrouchni AEF (2009) Multi-agent programming: languages, tools and applications. Springer, Berlin
Bratman M (1987). Intention, plans, and practical reason. Harvard University Press
Cain B (2007) A review of the mental workload literature. Defence Research And Development Toronto, Toronto
Carroll JM, Rosson MB, Convertino G, Ganoe CH (2006) Awareness and teamwork in computer-supported collaborations. Interact Comput 18(1):21–46
Cohen I, Brinkman WP, Neerincx MA (2015) Modelling environmental and cognitive factors to predict performance in a stressful training scenario on a naval ship simulator. Cogn Technol Work 17(4):503–519
Colin TR, Smets NJ, Mioch T, Neerincx MA (2014). Real time modeling of the cognitive load of an Urban Search And Rescue robot operator. In robot and human interactive communication, 2014 RO-MAN: The 23rd IEEE international symposium on (pp 874–879)
Czeskis A, Dermendjieva I, Yapit H, Borning A, Friedman B, Gill B, Kohno T (2010). Parenting from the pocket: value tensions and technical directions for secure and private parent-teen mobile safety. Proceedings of the sixth symposium on usable privacy and security, ACM, p 15
De Greef T (2012) Virtual assistants for dynamic task allocation and coordination. Delft University of Technology, TU Delft
De Greef T, Mohabir A, Van der Poel I, Neerincx M (2013) sCEthics: embedding ethical values in cognitive engineering. In Proceedings of the 31st European conference on cognitive ergonomics, ACM, p 4
De Regt A, Siegel AW, Schraagen JM (2016) Toward quantifying metrics for rail-system resilience: identification and analysis of performance weak resilience signals. Cogn Technol Work 18(2):319–331
Denning T, Borning A, Friedman B, Gill BT, Kohno T, Maisel WH (2010) Patients, pacemakers, and implantable defibrillators: human values and security for wireless implantable medical devices. Proceedings of the SIGCHI conference on human factors in computing systems, ACM, p 917–926
Eggemeier FT, Wilson GF (1991) Performance-based and subjective assessment of workload in multi-task environments. In: Damos D (ed) Multiple Task Performance, CRC Press, pp 217–278
Flanagan M, Howe D, Nissenbaum H (2008) Embodying values in technology: theory and practice. In: Van den Hoven J, Weckert J (eds) Information Technology and Moral Philosophy, Cambridge University Press, pp 322–353
Flemisch F, Heesen M, Hesse T, Kelsch J, Schieben A, Beller J (2012) Towards a dynamic balance between humans and automation: authority, ability, responsibility and control in shared and cooperative control situations. Cogn Technol Work 14(1):3–18
Friedman B, Hendry D (2012) The envisioning cards: a toolkit for catalyzing humanistic and technical imaginations. Proceedings of the 2012 ACM annual conference on human factors in computing systems, ACM, pp 1145–1148
Friedman B, Kahn Jr PH, Borning A (2013) Value sensitive design and information systems. In: Doorn N, Schuurbiers D, van de Poel I, Gorman ME (eds) Early engagement and new technologies: opening up the laboratory, Springer, Netherlands, pp 55–95
Galy E, Cariou M, Mélan C (2012) What is the relationship between mental workload factors and cognitive load types? Int J Psychophysiol 83(3):269–275
Gawron VJ (2008) Human performance, workload, and situational awareness measures handbook. CRC Press, Boca Raton
Hankins TC, Wilson GF (1998) A comparison of heart rate, eye activity, EEG and subjective measures of pilot mental workload during flight. Aviat Space Environ Med 69(4):360–367
Harbers M, Neerincx MA (2014) Value sensitive design of automated workload distribution support for traffic control teams. Proceedings of the international conference on human computer interaction
Harbers M, Van den Bosch K, Meyer J-J (2010) Design and evaluation of explainable BDI agents. In: Proceedings of International Conference on Web intelligence and intelligent agent technology, vol. 2, IEEE, pp 125–132
Harbers M, Aydogan R, Jonker CM, Neerincx MA (2014) Sharing information in teams: giving up privacy or compromising on team performance? In Proceedings of the 2014 international conference on autonomous agents and multi-agent systems, IFAAMAS, pp 413–420
Harbers M, Detweiler C, Neerincx MA (2015) Embedding stakeholder values in the requirements engineering process. Springer, Berlin, pp 318–332
Hindriks KV (2009) Programming rational agents in GOAL. In: El Fallah Seghrouchni A, Dix J, Dastani M, Bordini RH (eds) Multi-Agent Programming: Languages and Tools and Applications, Springer, pp 119–157
Hogervorst MA, Brouwer AM, van Erp JB (2014) Combining and comparing EEG, peripheral physiology and eye-related measures for the assessment of mental workload. Front Neurosci 8:322
Laird JE (2012) The Soar cognitive architecture. MIT Press, Cambridge
Lederer S, Mankoff J, Dey AK (2003) Who wants to know what when? Privacy preference determinants in ubiquitous computing. In CHI’03 extended abstracts on human factors in computing systems, ACM, pp 724–725
Levin S, Aronsky D, Hemphill R, Han J, Slagle J, France DJ (2007) Shifting toward balance: measuring the distribution of workload among emergency physician teams. Ann Emerg Med 50(4):419–423
Looije R, Neerincx MA, Hindriks KV (2016) Specifying and testing the design rationale of social robots for behavior change in children. Cognit Syst Res. doi:10.1016/j.cogsys.2016.07.002
Longo L (2015) A defeasible reasoning framework for human mental workload representation and assessment. Behav Inform Technol 34(8):758–786
Manders-Huits N (2011) What values in design? The challenge of incorporating moral values into design. Sci Eng Ethics 17(2):271–287
Mehrabian A (1996) Pleasure-arousal-dominance: a general framework for describing and measuring individual differences in temperament. Curr Psychol 14(4):261–292
Meijer S (2012) Gaming simulations for railways: lessons learned from modeling six games for the Dutch infrastructure management. In Infrastructure design, signaling and security in railway, pp 275–294
Meshkati N, Hancock PA (eds) (2011) Human mental workload. Elsevier, Amsterdam
Mesmer-Magnus JR, DeChurch LA (2009) Information sharing and team performance: a meta-analysis. J Appl Psychol 94(2):535
Miller JK, Friedman B, Jancke G (2007) Value tensions in design: the value sensitive design, development, and appropriation of a corporation’s groupware system. Proceedings of the 2007 international ACM conference on supporting group work, ACM, pp 281–290
Mioch T, Smets NJ, Neerincx MA (2012) Predicting performance and situation awareness of robot operators in complex situations by unit task tests. In ACHI 2012, The fifth international conference on advances in computer–human interactions, pp 241–246
Nathan LP, Klasnja PV, Friedman B (2007) Value scenarios: a technique for envisioning systemic effects of new technologies. In CHI’07 extended abstracts on Human factors in computing systems, ACM, pp 2585–2590
Neerincx MA (2003) Cognitive task load design: model, methods and examples. In: Hollnagel E (ed) Proceedings of the International Conference on Handbook of cognitive task design, CRC Press, pp 283–305
Neerincx MA (2007) Modelling cognitive and affective load for the design of human–machine collaboration. Proceedings of the International Conference on Engineering Psychology and Cognitive Ergonomics, Springer, Berlin, pp 568–574
Neerincx MA (2011) Situated cognitive engineering for crew support in space. Pers Ubiquit Comput 15(5):445–456
Neerincx MA, de Greef HP (1998) Cognitive support: extending human knowledge and processing capacities. Hum Comput Interact 13:73–106
Neerincx MA, Lindenberg J (2008) Situated cognitive engineering for complex task environments. In: Schraagen JM, Militello L, Ormerod T, Lipshitz R (eds) Naturalistic decision making and macrocognition, Ashgate Publishing Limited, Aldershot, pp 373–390
Neerincx MA, Harbers M, Lim D, Van der Tas V (2014) Automatic feedback on cognitive load and emotional state of traffic controllers. Proceedings of the international conference on human computer interaction
Nissenbaum H (2009) Privacy in context: technology, policy, and the integrity of social life. Stanford University Press, Palo Alto
Peeters MM, Harbers M, Neerincx MA (2016) Designing a personal music assistant that enhances the social, cognitive, and affective experiences of people with dementia. Comput Hum Behav 63:727–737
Poel I (2013) Translating values into design requirements. In: Michelfelder DP, Mc-Carthy N, Goldberg DE (eds) Philosophy and engineering: reflections on practice, principles and process, philosophy of engineering and technology. Springer, Berlin, pp 253–266
Porter CO, Hollenbeck JR, Ilgen DR, Ellis AP, West BJ, Moon H (2003) Backing up behaviors in teams: the role of personality and legitimacy of need. J Appl Psychol 88(3):391
Reid GB, Nygren TE (1988) The subjective workload assessment technique: a scaling procedure for measuring mental workload. Adv Psychol 52:185–218
Salas E, Cooke NJ, Rosen MA (2008) On teams, teamwork, and team performance: discoveries and developments. Hum Factors: J Hum Factors Ergon Soc 50(3):540–547
Siegel AW, Schraagen JMC (2014) Measuring workload weak resilience signals at a rail control post. IIE Trans Occup Ergon Hum Factors 2(3–4):179–193
Siegel AW, Schraagen JM (2017) Team reflection makes resilience-related knowledge explicit through collaborative sensemaking: observation study at a rail post. Cogn Technol Work 19(1):127–142
Spiekman ME, Haazebroek P, Neerincx MA (2011) Requirements and platforms for social agents that alarm and support elderly living alone. In: Mutlu B, Bartneck C, Ham J, Evers V, Kanda T (eds) Social robotics, Springer, Berlin, pp 226–235
Sycara K, Lewis M (2004) Integrating intelligent agents into human teams. In Team cognition: understanding the factors that drive process and performance, Washington, DC, APA, pp 203–231
Van Broekhoven R, Siegel AW, Schraagen JM, Noordzij ML (2016) Comparison of real-time relative workload measurements in rail signalers. In Proceedings of the 2nd German conference on rail human factors, pp 30–40
Van den Hoven J (2007) ICT and value sensitive design. In The information society: Innovation, legitimacy, ethics and democracy in honor of Professor Jacques Berleur SJ, Springer, pp 67–72
Acknowledgements
The authors thank the members of the focus group for their valuable input. This research is part of the RAILROAD project and is supported by ProRail and the Netherlands organization for scientific research (NWO) (Under Grant 438-12-306).
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Harbers, M., Neerincx, M.A. Value sensitive design of a virtual assistant for workload harmonization in teams. Cogn Tech Work 19, 329–343 (2017). https://doi.org/10.1007/s10111-017-0408-4
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DOI: https://doi.org/10.1007/s10111-017-0408-4