Finding Information on a Menu: Linking Menu Organization to the User's Goals

Mehlenbacher, Brad; Duffy, Thomas M.; Palmer, J.

doi:10.1207/s15327051hci0403_3

Cited by 31 publications

(24 citation statements)

References 19 publications

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“…While uniform distribution does not reflect menu usage in real worlds [8,9,10,12], it has been used in many menu studies [2,4,5,16,20] because it is easy to experimentally control. In contrast, Zipfian distribution is more difficult to control in spite of better modelling item frequency distribution in common applications [6,8,9].…”

Section: The Uniform and Zipfian Distributionsmentioning

confidence: 99%

“…While it is well known [6,8,9], the consequences of the distribution for behavior have not been systematically investigated. Indeed, many previous studies rely either on a Uniform distribution [2,4,5,16,20] when comparing selection time, or a variant of a Zipfian distribution [1,11] when focusing on the average menu selection time. However, it is unclear if these observations hold when given a different, and more ecologically plausible, frequency distribution.…”

Section: Introductionmentioning

confidence: 99%

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Effects of Frequency Distribution on Linear Menu Performance

Liu

Bailly

Howes

2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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While it is well known that menu usage follows a Zipfian distribution, there has been little interest in the impact of menu item frequency distribution on user's behavior. In this note, we explore the effects of frequency distribution on average menu performance as well as individual item performance. We compare three frequency distributions of menu item usage: Uniform; Zipfian with s=1 and Zipfian with s=2. The results show that (1) user's behavior is sensitive to different frequency distributions at both menu and item level; (2) individual item selection time depends on, not only its frequency, but also the frequency of other items in the menu. Finally, we discuss how these findings might have impacts on menu design, empirical studies and menu modelling.

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Section: The Uniform and Zipfian Distributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effects of Frequency Distribution on Linear Menu Performance

Liu

Bailly

Howes

2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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“…Of course, other categories could arise as indicated by some other participants, such as the division between important and less important metadata elements or based on their frequency of use for relevance judgment. These types of categorisation have been proposed by researchers in human computer interaction and menu design research who argued that items should be grouped either in terms of similarity, importance or frequency of use (Mehlenbacher, 1989;Shneiderman & Plaisant, 2004;Liu, et al, 2002).…”

Section: Structure Of Metadata Elementsmentioning

confidence: 99%

Design factors affecting relevance judgment behaviour in the context of metadata surrogates

Balatsoukas

O’Brien

Morris

2010

Journal of Information Science

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For the full text of this licence, please go to: http://creativecommons.org/licenses/by-nc-nd/2.5/ Design factors affecting user interaction and relevance judgment behaviour with metadata surrogates: the case of structure, highlighting and quantity of information AbstractThis paper reports on the findings of a user study which investigated the effects of structure, highlighting and quantity of information on university students' interaction with metadata surrogates of learning object repositories (LORs) during the relevance judgment process. The literature review revealed a lack of studies investigating how metadata surrogates should be designed to meet users' needs and improve the effectiveness of relevance judgment. In order to address this issue, different versions of a prototype system, called META-LOR 2, were developed and evaluated in terms of the time needed for users to find relevant information, the accuracy of their relevance judgments and their satisfaction. To evaluate the prototype, a user study was conducted where test subjects were asked to complete a set of tasks and fill in a satisfaction questionnaire. The findings showed that participants performed better and were more satisfied with those versions of the prototype which highlighted metadata elements that included the query terms, used clusters or categories to organize metadata elements in the surrogates and included metadata elements which were relevant to the query or task at hand. Participants performed less efficiently and were less satisfied with metadata surrogates which lacked the use of a clear structure, did not highlight the query terms and contained a large number of metadata elements. It is anticipated that the findings of this research will enhance understanding of users' interaction with metadata surrogates during the relevance judgment process and improve the design of the search result interfaces of educational information retrieval systems, such as LORs.

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“…It is perhaps not surprising then that since the inception of HCI there has been a sustained interest in studying visual search [3,4,6,7,[9][10][11]13,15].…”

Section: Introductionmentioning

confidence: 99%

“…These tasks have been extensively studied in the literature, but separately -there have been surprisingly few attempts to compare how people perform these different kinds of search tasks. What few studies there have been have found that known-item searches are faster and more accurate than semantic searches [12,13]. However, these early studies did not make use of eye-tracking techniques to investigate underlying changes in search strategy.…”

Section: Introductionmentioning

confidence: 99%

How does knowing what you are looking for change visual search behavior?

Brumby

Cox

Chung

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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When searching a display, users sometimes know what the target is but sometimes do not. It has generally been assumed that for this latter case people must engage in a deeper semantic evaluation of items during the search process. This idea is central to Information Foraging theory. But do people actually spend longer assessing items when engaged in a semantically demanding search task? We investigate this by having participants locate target items in 16-item menus. Participants were either told exactly what to look for (known-item search) or they were told the category that the target belonged to (semantic search). Participants were faster and more accurate at known-item searches. Eyemovement data show that this was because participants were more likely to skip over items when performing known-item searches. Contrary to expectation, we found limited empirical evidence to support the idea that deeper semantic evaluations of items lead to longer gaze durations (this occurred only when items were arranged very close together). This finding is important because it reveals how people adopt different eye gaze strategies depending on the kind of search activity they are engaged in.

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Finding Information on a Menu: Linking Menu Organization to the User's Goals

Cited by 31 publications

References 19 publications

Effects of Frequency Distribution on Linear Menu Performance

Effects of Frequency Distribution on Linear Menu Performance

Design factors affecting relevance judgment behaviour in the context of metadata surrogates

How does knowing what you are looking for change visual search behavior?

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