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Sensemaking (information science)

Sensemaking (information science) is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Sensemaking (information science) rather than just read about it. In short: While sensemaking has been studied by other disciplines under other names for centuries, in information science and computer science the term "sensemaking" has primarily marked two distinct but related topics. Sensemaking was introduced as a methodology by Brenda Dervin in the 1980s and to human–computer interaction by PARC researchers Daniel M.

Key takeaways

  • Sensemaking (information science) belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Sensemaking (information science) to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Sensemaking (information science) from memory before moving on to harder problems.

Reference excerpt

While sensemaking has been studied by other disciplines under other names for centuries, in information science and computer science the term "sensemaking" has primarily marked two distinct but related topics. Sensemaking was introduced as a methodology by Brenda Dervin in the 1980s and to human–computer interaction by PARC researchers Daniel M. Russell, Mark Stefik, Peter Pirolli, and Stuart Card in 1993. In information science, the term is often written as "sense-making". In both cases, the concept has been used to bring together insights drawn from philosophy, sociology, and cognitive science (especially social psychology). Sensemaking research is therefore often presented as an interdisciplinary research programme.

As a process Given a body of data, sensemaking can be described as the process of developing a representation and encoding data in that representation to answer questions specific to a task, such as decision-making and problem-solving (Russell et al., 1993). Gary A. Klein and colleagues (Klein et al. 2006b) conceptualize sensemaking as a set of processes that is initiated when an individual or organization recognizes the inadequacy of their current understanding of events. Sensemaking is an active two-way process of fitting data into a frame (mental model) and fitting a frame around the data. Neither data nor frame comes first; data evoke frames and frames select and connect data. When there is no adequate fit, the data may be reconsidered or an existing frame may be revised. This description resembles the recognition-metacognition model (Cohen et al., 1996), which describes the metacognitive processes that are used by individuals to build, verify, and modify working models (or "stories") in situational awareness to account for an unrecognised situation. Such notions also echo the processes of assimilation and accommodation in Jean Piaget's theory of cognitive development (e.g., Piaget, 1972, 1977).

As methodology Brenda Dervin (Dervin, 1983, 1992, 1996) has investigated individual sensemaking, developing theories about the "cognitive gap" that individuals experience when attempting to make sense of observed data. Because much of this applied psychological research is grounded within the context of systems engineering and human factors, it aims to answer the need for concepts and performance to be measurable and for theories to be testable. Accordingly, sensemaking and situational awareness are viewed as working concepts that enable researchers to investigate and improve the interaction between people and information technology. This perspective emphasizes that humans play a significant role in adapting and responding to unexpected or unknown situations, as well as recognized situations. Dervin's work has largely focused on developing philosophical guidance for method, including methods of substantive theorizing and conducting research (Naumer, C. et al., 2008).

In human–computer interaction After a seminal paper on sensemaking in the human–computer interaction (HCI) field was published in 1993 (Russell et al., 1993), there was a great deal of activity around the understanding of how to design interactive systems for sensemaking, and workshops on sensemaking were held at prominent HCI conferences (e.g., Russell et al., 2009).

See also Augmented cognition

References (information science)

Worked examples

Example 1 — a first encounter with Sensemaking (information science)

Start with the simplest possible case. Write down what Sensemaking (information science) claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Sensemaking (information science) before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Sensemaking (information science) ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Sensemaking (information science)

In research
Sensemaking (information science) appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Sensemaking (information science) in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Sensemaking (information science) is common in secondary-school and first-year university syllabi. It links to neighbouring topics Information science, so understanding it makes those chapters shorter.
In everyday life
Look for Sensemaking (information science) outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Sensemaking (information science) in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Sensemaking (information science) means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Sensemaking (information science) out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Sensemaking (information science) in simple terms?

While sensemaking has been studied by other disciplines under other names for centuries, in information science and computer science the term "sensemaking" has primarily marked two distinct but related topics. Sensemaking was introduced as a methodology by Brenda Dervin in the 1980s and to human–co…

Why does Sensemaking (information science) matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Sensemaking (information science)?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Sensemaking (information science).

Tags

  • Information science

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