Separating the knowledge layers: Cognitive analysis of search knowledge through hierarchical goal decompositions

Bhavnani, Suresh K.; Bates, Marcia J.

doi:10.1002/meet.1450390122

Cited by 21 publications

(12 citation statements)

References 47 publications

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“…Discovery of differences in cognitive load for different task stages should not be surprising in view of previous research that examined complexity and the associated cognitive demands at the level of information display (e.g., Rosenholtz et al, 2007;Harper et al, 2009, presented in this section), and given our understanding of differences between task-stages (Dennis et al, 2002;Kim & Rieh, 2005). More generally, elements of search tactics and strategies involved in interactive information search (e.g., Bates, 1979;Belkin, Cool, Stein, & Thiel, 1995;Marchionini, 1995;Bhavnani & Bates, 2002;Xie, 2002) imply differences in cognitive effort as different tactics and strategies are employed; for example, query reformulation versus monitoring content for changes. It may be surprising, though, that relatively little attention has been given to the study of cognitive load at units shorter than a task.…”

Section: Cognitive Load On Information Tasksmentioning

confidence: 68%

Distribution of cognitive load in Web search

Gwizdka

2010

J. Am. Soc. Inf. Sci.

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The search task and the system both affect the demand on cognitive resources during information search. In some situations the demands may become too high for a person. This article has a three-fold goal. First, it presents and critiques methods to measure cognitive load. Second, it explores the distribution of load across search task stages. Finally, it seeks to improve our understanding of factors affecting cognitive load levels in information search. To this end, a controlled Web search experiment with 48 participants was conducted. Interaction logs were used to segment search tasks semiautomatically into task stages. Cognitive load was assessed using a new variant of the dual-task method. Average cognitive load was found to vary by search task stages. It was significantly higher during query formulation and user description of a relevant document as compared to examining search results and viewing individual documents. Semantic information shown next to the search results lists in one of the studied interfaces was found to decrease mental demands during query formulation and examination of the search results list.These findings demonstrate that changes in dynamic cognitive load can be detected within search tasks. Dynamic assessment of cognitive load is of core interest to information science because it enriches our understanding of cognitive demands imposed on people engaged in the search process by a task and the interactive information retrieval system employed.

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Section: Cognitive Load On Information Tasksmentioning

confidence: 68%

Distribution of cognitive load in Web search

Gwizdka

2010

J. Am. Soc. Inf. Sci.

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“…This formalization helps reveal why some users do not transition to use strategic knowledge. The cause of this is argued to be the qualitative difference of acquiring the strategic knowledge in the intermediate layer, where the user chooses what combination of commands to use to complete a task (Bhavnani & Bates, 2002;Bhavnani & John, 2000). Bhavnani's distinction of strategic knowledge is similar to Anderson's model of procedural knowledge.…”

Section: Acquisition Of Expertisementioning

confidence: 99%

An Investigation of Metrics for the In Situ Detection of Software Expertise

Grossman

Fitzmaurice

2014

Human–Computer Interaction

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Task-based analysis is a common and effective way to measure expertise levels of software users. However, such assessments typically require inperson laboratory studies and inherently require knowledge of the user's task. Today, there is no accepted method for assessing a user's expertise levels outside of a lab, during a user's own home or work environment activities. In this article, we explore the feasibility of software applications automatically inferring a user's expertise levels, based on the user's in situ usage patterns. We outline the potential usage metrics that may be indicative of expertise levels and then perform a study, where we capture such metrics, by installing logging software in the participants' own workplace environments. We then invite those participants into a laboratory study and perform a more traditional task-based assessment of expertise. Our analysis of the study examines if metrics captured in situ, without any task knowledge, can be indicative of user expertise levels. The results show the existence of significant correlations between metrics calculated from in situ usage logs, and task-based user expertise assessments from our laboratory study. We discuss the implications of the results and how future software applications may be able to measure and leverage knowledge of the expertise of its users.

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“…The results revealed “the existence of domain‐specific search knowledge” (p. 611). In another study, Bhavnani and Bates (2002) used hierarchical goal decomposition to better understand the knowledge required to complete particular search tasks. Because each of these studies included only a small sample, many of the findings could as appropriately be attributed to individual differences as to level of domain knowledge.…”

Section: Introductionmentioning

confidence: 99%

The effects of domain knowledge on search tactic formulation

Wildemuth

2003

J. Am. Soc. Inf. Sci.

282

228

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A search tactic is a set of search moves that are temporally and semantically related. The current study examined the tactics of medical students searching a factual database in microbiology. The students answered problems and searched the database on three occasions over a 9-month period. Their search moves were analyzed in terms of the changes in search terms used from one cycle to the next, using two different analysis methods.Common patterns were found in the students' search tactics; the most common approach was the specification of a concept, followed by the addition of one or more concepts, gradually narrowing the retrieved set before it was displayed. It was also found that the search tactics changed over time as the students' domain knowledge changed. These results have important implications for designers in developing systems that will support users' preferred ways of formulating searches. In addition, the research methods used (the coding scheme and the two data analysis methods-zero-order state transition matrices and maximal repeating patterns [MRP] analysis) are discussed in terms of their validity in future studies of search tactics. IntroductionWhen conducting an online search, a searcher enters one or more terms, gets a response from the system, and iteratively modifies the terms until satisfied with the results. Each iteration in the search formulation and reformulation process can be considered a search move. One way to examine these moves is to focus on the search terms selected in each iteration, as suggested by Shute and Smith (1993). For example, one move might consist of the substitution of a narrower term representing the same concept as that searched in the prior move (e.g., the first move might be a search on the term "reference works" and the second move might be a search on the term "encyclopedias").A set of moves that are temporally and semantically related can be a search tactic. While significant work has examined the individual moves that searchers make (Bates, 1979(Bates, , 1987Fidel, 1985), it is equally important to examine the sequences of moves made by searchers in order to understand the cognitive processes they use in formulating and reformulating their searches. The current paper attacks this problem by examining the tactics of medical students searching a factual database in microbiology, focusing on changes in students' tactics as their domain knowledge (i.e., their knowledge in the domain of microbiology) changed over time. In addition, it makes a methodological contribution by comparing two different methods for analyzing sequences of search moves.

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Separating the knowledge layers: Cognitive analysis of search knowledge through hierarchical goal decompositions

Cited by 21 publications

References 47 publications

Distribution of cognitive load in Web search

Distribution of cognitive load in Web search

An Investigation of Metrics for the In Situ Detection of Software Expertise

The effects of domain knowledge on search tactic formulation

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