Using the taxonomy of cognitive learning to model online searching

Jansen, Bernard J.; Booth, Danielle; Smith, Brian K.

doi:10.1016/j.ipm.2009.05.004

Cited by 113 publications

(96 citation statements)

References 62 publications

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“…Our results are consistent with results from Liu et al [13], who used three levels of objective task complexity, and consistent with Jansen et al [20], who used six levels of cognitive complexity (remember, understand, apply, analyze, evaluate, and create). Interestingly, Jansen et al [20] found that evaluate and create tasks required less search interaction than analyze tasks.…”

Section: Discussionsupporting

confidence: 92%

“…The third conceptualization of task complexity we use comes from Jansen et al [13] Table 2 in Section 3.3 for definitions). Although Jansen et al [13] did not situate this work in the context of task complexity, they observed a number of significant differences in the amount of interaction users exhibited when completing different task types, including session duration, number of queries, and number of pages viewed.…”

Section: Tasks and Task Complexitymentioning

confidence: 99%

“…We decided instead to construct tasks that we believed would require different amounts and diversity of information to complete and different amounts of search effort. In doing so, we turned to Anderson and Krathwohl's taxonomy of educational objectives [1] and the work of Jansen et al [13].…”

Section: Tasksmentioning

confidence: 99%

“…Six types of cognitive processes are identified: remember, understand, apply, analyze, evaluate and create, with each type requiring increasing amounts of cognition and effort. While this taxonomy is traditionally used to create educational materials such as exercises and exam questions, we used it to construct search tasks similar to Jansen et al [13]. We propose that the different dimensions in Table 2 reflect increasing levels of cognitive complexity.…”

Section: Tasksmentioning

confidence: 99%

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Task complexity, vertical display and user interaction in aggregated search

Arguello

Kelly

et al. 2012

Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Aggregated search is the task of blending results from specialized search services or verticals into the Web search results. While many studies have focused on aggregated search techniques, few studies have tried to better understand how users interact with aggregated search results. This study investigates how task complexity and vertical display (the blending of vertical results into the web results) affect the use of vertical content. Twenty-nine subjects completed six search tasks of varying levels of task complexity using two aggregated search interfaces: one that blended vertical results into the web results and one that only provided indirect vertical access. Our results show that more complex tasks required significantly more interaction and that subjects completing these tasks examined more vertical results. While the amount of interaction was the same between interfaces, subjects clicked on more vertical results when these were blended into the web results. Our results also show an interaction between task complexity and vertical display; subjects clicked on more verticals when completing the more complex tasks with the interface that blended vertical results. Subjects' evaluations of the two interfaces were nearly identical, but when analyzed with respect to their interface preferences, we found a positive relationship between system evaluations and individual preferences. Subjects justified their preference using similar rationales and their comments illustrate how the display itself can influence judgments of information quality, especially in cases when the vertical results might not be relevant to the search task. Categories and Subject Descriptors General TermsPerformance, Experimentation, Human Factors. KeywordsAggregated search interfaces, search behaviors, evaluation, user study, interaction, task complexity INTRODUCTIONIn addition to Web search, commercial search companies (e.g., Google, Bing, Yahoo!) provide access to a wide range of specialized services known as verticals (e.g., images, video, news Most published research in aggregated search has focused on automatic methods for predicting which verticals to present (vertical selection) [4,5,11,19] and where in the Web results to present them (vertical presentation) [2,3,23]. Evaluation of these systems has typically been conducted by using editorial vertical relevance judgements as the gold standard [2,3,4,5,19], or by using user-generated clicks on vertical results as a proxy for relevance [11,23]. While these studies have greatly advanced the state of the art in aggregated search techniques, because users are far removed from the evaluation, they have contributed little insight about how users' higher-level objectives influence their engagement with vertical search results.A few published studies have investigated user behavior with aggregated search interfaces [24,25,28]. Thus far, these studies show two major trends. First, when a vertical is relevant, users prefer to see its results towards to the top of the blended results [...

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Section: Discussionsupporting

confidence: 92%

Section: Tasks and Task Complexitymentioning

confidence: 99%

Section: Tasksmentioning

confidence: 99%

Section: Tasksmentioning

confidence: 99%

See 2 more Smart Citations

Task complexity, vertical display and user interaction in aggregated search

Arguello

Kelly

et al. 2012

Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…Complementary research was conducted by Jansen et al who investigated search and search intent as decision making process (Jansen & McNeese, 2005) and consecutively as learning process (Jansen, Booth & Smith, 2009). In the latter, they analyzed the search process from a cognitive learning perspective.…”

Section: Search Intent Detection and Categorizationmentioning

confidence: 99%

Acquiring knowledge about human goals from Search Query Logs

Strohmaier

Kroll

2012

Information Processing & Management

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A better understanding of what motivates humans to perform certain actions is relevant for a range of research challenges including generating action sequences that implement goals (planning). A first step in this direction is the task of acquiring knowledge about human goals. In this work, we investigate whether Search Query Logs are a viable source for extracting expressions of human goals. For this purpose, we devise an algorithm that automatically identifies queries containing explicit goals such as find home to rent in Florida. Evaluation results of our algorithm achieve useful precision/recall values. We apply the classification algorithm to two large Search Query Logs, recorded by AOL and Microsoft Research in 2006, and obtain a set of ∼110.000 queries containing explicit goals. To study the nature of human goals in Search Query Logs, we conduct qualitative, quantitative and comparative analyses. Our findings suggest that Search Query Logs (i) represent a viable source for extracting human goals, (ii) contain a great variety of human goals and (iii) contain human goals that can be employed to complement existing commonsense knowledge bases. Finally, we illustrate the potential of goal knowledge for addressing following application scenario: to refine and extend commonsense knowledge with human goals from Search Query Logs. This work is relevant for (i) knowledge engineers interested in acquiring human goals from textual corpora and constructing knowledge bases of human goals (ii) researchers interested in studying characteristics of human goals in Search Query Logs.

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Doctors' online information needs, cognitive search strategies, and judgments of information quality and cognitive authority: How predictive judgments introduce bias into cognitive search models

Hughes

Wareham

Joshi³

2009

J. Am. Soc. Inf. Sci.

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Literature examining information judgments and Internet search behaviors notes a number of major research gaps, including how users actually make these judgments outside of experiments or researcher-defined tasks, and how search behavior is impacted by a user's judgment of online information. Using the medical setting, where doctors face real consequences in applying the information found, we examine how information judgments employed by doctors to mitigate risk impact their cognitive search. Diaries encompassing 444 real clinical information search incidents, combined with semistructured interviews across 35 doctors, were analyzed via thematic analysis. Results show that doctors, though aware of the need for information quality and cognitive authority, rarely make evaluative judgments. This is explained by navigational bias in information searches and via predictive judgments that favor known sites where doctors perceive levels of information quality and cognitive authority. Doctors' mental models of the Internet sites and Web experience relevant to the task type enable these predictive judgments. These results suggest a model connecting online cognitive search and information judgment literatures. Moreover, this implies a need to understand cognitive search through longitudinalor learning-based views for repeated search tasks, and Received June 13, 2009; revised August 12, 2009; accepted September 3, 2009 Additional Supporting Information may be found in the online version of this article. IntroductionInformation search is a process by which a person seeks knowledge about a problem or situation, constituting a major activity by the Internet's millions of users (Browne, Pitts, & Wetherbe, 2007). The Web is now a primary source of information for many people, driving a critical need to understand how users search or employ search engines (Jansen & Spink, 2006). Extensive literature examines not only behavioral models detailing the different moves or tactics during Internet search but also decision making or strategies described as cognitive search models (Navarro-Prieto, Scaife, & Rogers, 1999;Thatcher, 2006Thatcher, , 2008. The latter examines the cognitive aspects of the moves users employ to optimize their search performance, exploring elements such as expertnovice differences or judgments on when to terminate the search (e.g., Thatcher, 2006Thatcher, , 2008Cothey, 2002;Jaillet, 2003;Browne et al., 2007). This notion of judgment introduces a second stream of literature, Internet information judgments, where authors note that the use of predictive information judgments impacts decision making in search, based on an anticipation of a page's value before viewing it (Rieh, 2002;Griffiths & Brophy, 2005).Cognitive search models rarely explore the impact of predictive judgments. Most studies are based on tasks defined by researchers in experimental settings that are difficult to generalize to professional contexts or real use (Thatcher, 2006; information judgments during real instances of information search and...

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Using the taxonomy of cognitive learning to model online searching

Cited by 113 publications

References 62 publications

Task complexity, vertical display and user interaction in aggregated search

Task complexity, vertical display and user interaction in aggregated search

Acquiring knowledge about human goals from Search Query Logs

Doctors' online information needs, cognitive search strategies, and judgments of information quality and cognitive authority: How predictive judgments introduce bias into cognitive search models

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