Making Sense of Sensemaking 2: A Macrocognitive Model

Klein, Gary; Moon, Brian; Hoffman, Robert R.

doi:10.1109/mis.2006.100

Cited by 410 publications

(342 citation statements)

References 10 publications

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“…The data-frame theory of sensemaking (Klein et al 2006;Rankin et al 2016) postulates that elements are explained when they are fitted into a structure that links them to other elements. The term frame is used to denote an explanatory structure that defines entities by describing their relationship to other entities (Klein et al 2007, p. 118).…”

Section: Data-framingmentioning

confidence: 99%

Team reflection makes resilience-related knowledge explicit through collaborative sensemaking: observation study at a rail post

Siegel

Schraagen

2017

Cogn Tech Work

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Resilience is defined as the ability to adaptively deal with system boundaries in the face of the unexpected and unforeseen (Branlat and Woods in AAAI Fall Sympoisum, 2010. http://www.aaai.org/ocs/index.php/FSS/ FSS10/paper/viewPaper/2238). We hypothesize that drawing upon resilience-related knowledge is a prerequisite for such adaptivity. This paper proposes team reflection (Ellis et al. in Curr Dir Psychol Sci 23(1):67-72, 2014) as a macrocognitive function to make the resilience-related knowledge explicit. This knowledge is implicitly available with individual team members active at the sharp end but is never explicitly shared due to invisibility of goal-relevant constraints. To overcome this invisibility, we suggest an application that makes changes in the current rail sociotechnical system visible in terms of the three system boundaries, a variation of the originally proposed by Rasmussen (Saf Sci 27(2/3):183-213, 1997): safety, performance and workload. This allows a team of rail signallers to analyse movements towards system boundaries and share knowledge on these movements. An observational study at a rail control post was conducted to assess the value of team reflection in making resilience-related knowledge explicit. For this purpose, we developed a first prototype of the application concerning the performance boundary only. Using naturalistic observations of a team during a week, we observed how they reflected at the end of their shift on salient system changes. A global content analysis was used to show the relevance of the content to resilience and to test the increase in the resilience-related knowledge throughout the observation period. A specific case of a human approaching the rail tracks, as a potential suicide, was analysed in detail. The results show the value of team reflection on system movements towards their boundaries, thus making goal-relevant constrained knowledge explicit within the operational rail environment.

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Section: Data-framingmentioning

confidence: 99%

Team reflection makes resilience-related knowledge explicit through collaborative sensemaking: observation study at a rail post

Siegel

Schraagen

2017

Cogn Tech Work

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“…The Data-Frame Model (Klein et al 2006a, b) fits within the RPD Model (Baber and McMaster 2016). According to Klein et al (2006a, b), frames constitute conceptual constructs that can be explanatory structures accounting for the data (where data can be interpreted as events and stimuli such as maps, stories, diagrams).…”

Section: Sensemaking Processesmentioning

confidence: 99%

“…According to Klein et al (2006a, b), frames constitute conceptual constructs that can be explanatory structures accounting for the data (where data can be interpreted as events and stimuli such as maps, stories, diagrams). Frames can also shape the data within a closed-loop transition sequence between mental models (representing explanatory backward-looking processes) and mental simulation (representing anticipatory forward-looking processes) (Fig.…”

Section: Sensemaking Processesmentioning

confidence: 99%

“…We note the similarities between sensemaking and situation awareness (Endsley 1995), in that both are concerned with interpretation of the world and, particularly for asset maintenance, the prediction of future states. Much work in Situation Awareness emphasises the role of a knowledge product, or state of awareness, while sensemaking (Baber and McMaster 2016;Klein 1993) is more concerned with the ongoing process of framing and reframing information (Klein et al 2006a). However, alternative accounts (see Stanton et al 2017) emphasise the dynamic nature of situation awareness that is both 'framed' by prior expectation, and is distributed between people as individuals and teams, and artefacts.…”

Section: Sensemaking Processesmentioning

confidence: 99%

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Identifying rail asset maintenance processes: a human-centric and sensemaking approach

2018

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Efficient asset maintenance is key for delivering services such as transport. Current rail maintenance processes have been mostly reactive with a recent shift towards exploring proactive modes. The introduction of new ubiquitous technologies and advanced data analytics facilitates the embedding of a 'predict-and-prevent' approach to managing assets. Successful, user-centred integration of such technology is still, however, a sparsely understood area. This study reports results from a set of interviews, based on critical decision method, with rail asset maintenance and management experts regarding current procedural aspects of asset management and maintenance. We analyse and present the results from a human-centric sensemaking timeline perspective. We found that within a complex socio-technical environment such as rail transport, asset maintenance processes apply not only just at local levels, but also at broader, strategic levels that involve different stakeholders and necessitate different levels of expertise. This is a particularly interesting aspect within maintenance that has not been discussed as of yet within a process-based and timeline-based models of asset maintenance. We argue that it is important to consider asset maintenance activities within both micro (local)-and macro (broader)-levels to ensure reliability and stability in transport services. We also propose that the traditionally distinct notions of individual, collaborative and artefact-based sensemaking are in fact all in evidence in this sensemaking context, and argue that a more holistic view of sensemaking is therefore appropriate by placing these results within an amended recognition-primed decision-making model.

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“…Russell et al [25] studied cost structure of sensemaking and modelled sensemaking as cyclic processes of searching for external representations and encoding information into these representations to reduce the cost of tasks to be performed. Klein et al [13] defines sensemaking as a motivated, continuous effort to understand connections (which can be among people, places, and events) in order to anticipate their trajectories and act effectively.…”

Section: Individual Sensemakingmentioning

confidence: 99%

Requirements for Big Data Analytics Supporting Decision Making: A Sensemaking Perspective

Lau

Yang-Turner

Karacapilidis

2014

Studies in Big Data

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Big data analytics requires technologies to efficiently process large quantities of data. Moreover, especially in decision making, it not only requires individual intellectual capabilities in the analytical activities but also collective knowledge. Very often, people with diverse expert knowledge need to work together towards a meaningful interpretation of the associated results for new insight. Thus, a big data analysis infrastructure must both support technical innovation and effectively accommodate input from multiple human experts. In this chapter, we aim to advance our understanding on the synergy between human and machine intelligence in tackling big data analysis. Sensemaking models for big data analysis were explored and used to inform the development of a generic conceptual architecture as a means to frame the requirements of such an analysis and to position the role of both technology and human in this synergetic relationship. Two contrasting real-world use case studies were undertaken to test the applicability of the proposed architecture for the development of a supporting platform for big data analysis. Reflection on this outcome has further advanced our understanding on the complexity and the potential of individual and collaborative sensemaking models for big data analytics.

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Making Sense of Sensemaking 2: A Macrocognitive Model

Cited by 410 publications

References 10 publications

Team reflection makes resilience-related knowledge explicit through collaborative sensemaking: observation study at a rail post

Team reflection makes resilience-related knowledge explicit through collaborative sensemaking: observation study at a rail post

Identifying rail asset maintenance processes: a human-centric and sensemaking approach

Requirements for Big Data Analytics Supporting Decision Making: A Sensemaking Perspective

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