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2021 | OriginalPaper | Buchkapitel

Comparing Performance Models for Bivariate Pointing Through a Crowdsourced Experiment

verfasst von : Shota Yamanaka

Erschienen in: Human-Computer Interaction – INTERACT 2021

Verlag: Springer International Publishing

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Abstract

Evaluation of a novel user-performance model’s fitness requires comparison with baseline models, yet it is often time consuming and involves much effort by researchers to collect data from many participants. Crowdsourcing has recently been used for evaluating novel interaction techniques, but its potential for model comparison studies has not been investigated in detail. In this study, we evaluated four existing Fitts’ law models for rectangular targets, as though one of them was a proposed novel model. We recruited 210 crowd workers, who performed 94,080 clicks in total, and confirmed that the result for the best-fit model was consistent with previous studies. We also analyzed whether this conclusion would change depending on the sample size, but even when we randomly sampled data from five workers for 10,000 iterations, the best-fit model changed only once (0.01%). We have thus demonstrated a case in which crowdsourcing is beneficial for comparing performance models.

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Vorheriges Kapitel BubbleVideo: Supporting Small Group Interactions in Online Conferences

Nächstes Kapitel Older Adults’ Motivation and Engagement with Diverse Crowdsourcing Citizen Science Tasks

We found this previous work as part of a Ph.D. thesis by one of these authors (Faridani) [11]. He defined this Fitts’ law study as a crowdsourced task, and thus we introduce it here.

https://crowdsourcing.yahoo.co.jp.

The simulation included data from the outlier worker detected in the analysis of the main experiment, because that worker’s status as an outlier depends on the other sampled workers’ results.

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Titel: Comparing Performance Models for Bivariate Pointing Through a Crowdsourced Experiment
verfasst von: Shota Yamanaka
Verlag: Springer International Publishing
Buch: Human-Computer Interaction – INTERACT 2021
Print ISBN: 978-3-030-85615-1

Electronic ISBN: 978-3-030-85616-8

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-85616-8_6

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