Investigating the Effects of Legacy Bias

Beşevli, Ceylan; Buruk, Oğuz; Erkaya, Merve; Özcan, Oğuzhan

doi:10.1145/3197391.3205449

Cited by 14 publications

(9 citation statements)

References 7 publications

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“…Such an approach also helps shorten the time and effort required by professionals to learn the new ways of interaction (Köpsel and Bubalo, 2015). Our recommendation for the inclusion of both legacy and non-legacy knowledge in the interface is supported by evidence from recent gesture elicitation research, which found that legacy gestures were favored by participants for their familiarity and non-legacy gestures were favored for their affordances (Beşevli et al ., 2018). This suggests that both legacy and non-legacy knowledge is useful, based on user needs and context.…”

Section: Discussionmentioning

confidence: 99%

“…In speech analysis, the level of detail pertains to whether designers choose to speak succinctly, or whether they provide detail about sizes, locations, and relationships. It is especially relevant to investigate the level of detail in designers’ verbalizations for speech-based interfaces for CAD modeling, as it indicates how much detail designers prefer to incorporate in their instructions for conceptual design.Use of legacy knowledge: Legacy knowledge is based on users’ experience with prior interfaces and technologies (Morris et al ., 2014; Beşevli et al ., 2018). Research in elicitation studies has found that previous experience with desktop computing strongly influences users’ gestural responses (Morris, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Use of legacy knowledge: Legacy knowledge is based on users’ experience with prior interfaces and technologies (Morris et al ., 2014; Beşevli et al ., 2018). Research in elicitation studies has found that previous experience with desktop computing strongly influences users’ gestural responses (Morris, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Speech analysis for conceptual CAD modeling using multi-modal interfaces: An investigation into Architects’ and Engineers’ speech preferences

Khan

Tunçer

2019

AIEDAM

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Speech- and gesture-based interfaces for computer-aided design (CAD) modeling must employ vocabulary suitable for target professional groups. We conducted an experiment with 40 participants from architecture and engineering backgrounds to elicit their speech preferences for four CAD manipulation tasks: Scale, Rotate, Copy, and Move. We compiled speech command terms used by participants and analyzed verbalizations based on three analytic themes: the exactness of descriptions, the granularity of descriptions, and the use of CAD legacy terms. We found that participants from both groups used precise and vague expressions in their verbalizations and used a median of three parameters in their verbalizations. Architects used CAD legacy terms more than Engineers in the tasks Scale and Rotate. Based on these findings, we give recommendations for the design of speech- and gesture-based interface for conceptual CAD modeling.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Speech analysis for conceptual CAD modeling using multi-modal interfaces: An investigation into Architects’ and Engineers’ speech preferences

Khan

Tunçer

2019

AIEDAM

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“…Another point of common discussion is the legacy bias (which Morris et. al also focusses on) [79,106,22,15]. Legacy bias refers to the predisposed ideas of participants-especially pertaining to their previous technological experiences-that may bias them when they are asked to propose a gesture.…”

Section: Agreement Rate Evaluationsmentioning

confidence: 99%

“…Some elicitations also rely on some level of wizard-of-oz techniques in which the user believes the system to be controlled by them while the researcher controls it [15,67]. Some works specifically mention this in conjunction to removing the gulf of execution [67].…”

Section: Agreement Rate Evaluationsmentioning

confidence: 99%

Evaluating User Preferences for Augmented Reality Interactions with the Internet of Things

Chopra

Maurer

2020

Proceedings of the International Conference on Advanced Visual Interfaces

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This thesis investigates how users want to control IoT devices in their homes with headset AR. Gestural and voice controls are both suitable methods of input for headset AR. However, there is a lack of end-user input in the design of such gestures and voice commands: especially in comparative terms. An elicitation study is performed with 16 participants to gather their preferred voice commands and gestures for a set of referents. The contribution is an analysis of 784 inputs (392 gestures and 392 voice), a resulting gesture set and command types, comparative method preferences, a novel method to analyze voice command input (called voice command pattern template), as well as design recommendations based on observations and interviews. These recommendations serve as guides for future designers and implementors of such voice commands and gestures.

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