Modelling Learning of New Keyboard Layouts

Jussi PP Jokinen; Sayan Sarcar; Antti Oulasvirta; Chaklam Silpasuwanchai; Zhenxin Wang; Xiangshi Ren

doi:10.1145/3025453.3025580

Modelling Learning of New Keyboard Layouts

Jussi PP Jokinen, Sayan Sarcar, Antti Oulasvirta, Chaklam Silpasuwanchai, Zhenxin Wang, Xiangshi Ren

Kochi University of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

47 Citations (Scopus)

343 Downloads (Pure)

Abstract

Predicting how users learn new or changed interfaces is a long-standing objective in HCI research. This paper contributes to understanding of visual search and learning in text entry. With a goal of explaining variance in novices' typing performance that is attributable to visual search, a model was designed to predict how users learn to locate keys on a keyboard: initially relying on visual short-term memory but then transitioning to recall-based search. This allows predicting search times and visual search patterns for completely and partially new layouts. The model complements models of motor performance and learning in text entry by predicting change in visual search patterns over time. Practitioners can use it for estimating how long it takes to reach the desired level of performance with a given layout.

Original language	English
Title of host publication	Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems
Publisher	ACM
Pages	4203-4215
Number of pages	13
ISBN (Electronic)	978-1-4503-4655-9
DOIs	https://doi.org/10.1145/3025453.3025580
Publication status	Published - 2017
MoE publication type	A4 Conference publication
Event	ACM SIGCHI Annual Conference on Human Factors in Computing Systems - Colorado Convention Center, Denver, United States Duration: 6 May 2017 → 11 May 2017 Conference number: 35 https://chi2017.acm.org/

Conference

Conference	ACM SIGCHI Annual Conference on Human Factors in Computing Systems
Abbreviated title	ACM CHI
Country/Territory	United States
City	Denver
Period	06/05/2017 → 11/05/2017
Internet address	https://chi2017.acm.org/

Keywords

visual search
keyboard layouts
models of learning

Access to Document

10.1145/3025453.3025580Licence: CC BY

p4203-jokinen
© 2017 The Authors
Final published version, 1.19 MBLicence: CC BY

CHI Best Paper Award x 2
Oulasvirta, Antti (Recipient), 2017
Prize: Award or honor granted for a specific work

Cite this

@inproceedings{d282713499c84c1991f66328e6b46479,

title = "Modelling Learning of New Keyboard Layouts",

abstract = "Predicting how users learn new or changed interfaces is a long-standing objective in HCI research. This paper contributes to understanding of visual search and learning in text entry. With a goal of explaining variance in novices' typing performance that is attributable to visual search, a model was designed to predict how users learn to locate keys on a keyboard: initially relying on visual short-term memory but then transitioning to recall-based search. This allows predicting search times and visual search patterns for completely and partially new layouts. The model complements models of motor performance and learning in text entry by predicting change in visual search patterns over time. Practitioners can use it for estimating how long it takes to reach the desired level of performance with a given layout.",

keywords = "visual search, keyboard layouts, models of learning",

author = "Jokinen, {Jussi PP} and Sayan Sarcar and Antti Oulasvirta and Chaklam Silpasuwanchai and Zhenxin Wang and Xiangshi Ren",

note = "| openaire: EC/H2020/637991/EU//COMPUTED; ACM SIGCHI Annual Conference on Human Factors in Computing Systems, ACM CHI ; Conference date: 06-05-2017 Through 11-05-2017",

year = "2017",

doi = "10.1145/3025453.3025580",

language = "English",

pages = "4203--4215",

booktitle = "Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems",

publisher = "ACM",

address = "United States",

url = "https://chi2017.acm.org/",

}

Jokinen, JPP, Sarcar, S, Oulasvirta, A, Silpasuwanchai, C, Wang, Z & Ren, X 2017, Modelling Learning of New Keyboard Layouts. in Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, pp. 4203-4215, ACM SIGCHI Annual Conference on Human Factors in Computing Systems, Denver, Colorado, United States, 06/05/2017. https://doi.org/10.1145/3025453.3025580

TY - GEN

T1 - Modelling Learning of New Keyboard Layouts

AU - Jokinen, Jussi PP

AU - Sarcar, Sayan

AU - Oulasvirta, Antti

AU - Silpasuwanchai, Chaklam

AU - Wang, Zhenxin

AU - Ren, Xiangshi

N1 - Conference code: 35

PY - 2017

Y1 - 2017

N2 - Predicting how users learn new or changed interfaces is a long-standing objective in HCI research. This paper contributes to understanding of visual search and learning in text entry. With a goal of explaining variance in novices' typing performance that is attributable to visual search, a model was designed to predict how users learn to locate keys on a keyboard: initially relying on visual short-term memory but then transitioning to recall-based search. This allows predicting search times and visual search patterns for completely and partially new layouts. The model complements models of motor performance and learning in text entry by predicting change in visual search patterns over time. Practitioners can use it for estimating how long it takes to reach the desired level of performance with a given layout.

AB - Predicting how users learn new or changed interfaces is a long-standing objective in HCI research. This paper contributes to understanding of visual search and learning in text entry. With a goal of explaining variance in novices' typing performance that is attributable to visual search, a model was designed to predict how users learn to locate keys on a keyboard: initially relying on visual short-term memory but then transitioning to recall-based search. This allows predicting search times and visual search patterns for completely and partially new layouts. The model complements models of motor performance and learning in text entry by predicting change in visual search patterns over time. Practitioners can use it for estimating how long it takes to reach the desired level of performance with a given layout.

KW - visual search

KW - keyboard layouts

KW - models of learning

U2 - 10.1145/3025453.3025580

DO - 10.1145/3025453.3025580

M3 - Conference article in proceedings

SP - 4203

EP - 4215

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

PB - ACM

T2 - ACM SIGCHI Annual Conference on Human Factors in Computing Systems

Y2 - 6 May 2017 through 11 May 2017

ER -

Modelling Learning of New Keyboard Layouts

Abstract

Conference

Keywords

Access to Document

Fingerprint

Prizes

CHI Best Paper Award x 2

Cite this