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Touch-level model (TLM): evolving KLM-GOMS for touchscreen and mobile devices

Published: 28 March 2014 Publication History

Abstract

In the early days of personal computing, a model called "Goals, Operators, Methods, and Selection Rules" (GOMS) provided a method with which to quantify user interaction with a system. [2] Soon after, the Keystroke Level Model (KLM) allowed decomposition of complex tasks into atomic, benchmarked 'operators' that could generalize and estimate interactions on any keyboard interface. However, this the model is dated and unsuitable for today's modern devices.
These touchscreen interfaces are now pervasive [4], but the HCI implications of this shift are not fully researched; an imperative exists for research in human-computer interaction to study these interfaces and the interactions of the people using them. [3]
In this paper, we introduce new operators and other modifications to KLM-GOMS to accommodate modern touchscreen interfaces. We call these additions, together with updates to the existing KLM operators, the Touch Level Model (TLM). We propose that this model can be employed to model human task performance on a constrained-input touchscreen device and, with proper benchmarking, accurately predict actual user performance.
Our goal is to provide a means for quantitative analysis of touchscreen interfaces for HCI research and to enable description of interactions with such interfaces in terms of these operators, thus predicting user performance with less need to create prototypes or perform user studies.

References

[1]
Stuart K. Card, Thomas P. Moran, and Allen Newell. The keystroke-level model for user performance time with interactive systems. Commun. ACM, 23(7):396--410, July 1980.
[2]
Stuart K. Card, Allen Newell, and Thomas P. Moran. The Psychology of Human-Computer Interaction. L. Erlbaum Associates Inc., Hillsdale, NJ, USA, 2000.
[3]
International Data Corporation. Worldwide smart connected device market crossed 1 billion shipments in 2012, apple pulls near samsung in fourth quarter.
[4]
Benedict Evans. The end of the beginning. mobile blows past pcs: so what?
[5]
Paul Holleis, Friederike Otto, Heinrich Hussmann, and Albrecht Schmidt. Keystroke-level model for advanced mobile phone interaction. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '07, pages 1505--1514, New York, NY, USA, 2007. ACM.
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Paul Holleis, Maximilian Scherr, and Gregor Broll. A revised mobile klm for interaction with multiple nfc-tags. In Proceedings of the 13th IFIP TC 13 International Conference on Human-computer Interaction - Volume Part IV, INTERACT'11, pages 204--221, Berlin, Heidelberg, 2011. Springer-Verlag.
[7]
Bonnie E. John and David E. Kieras. The goms family of user interface analysis techniques: Comparison and contrast. ACM Trans. Comput.-Hum. Interact., 3(4):320--351, December 1996.
[8]
Franklin Tamborello Kristen Greene. ACT-Touch Reference Manual, Working Draft. National Institute of Standards and Technology, August 2013.
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Hui Li, Ying Liu, Jun Liu, Xia Wang, Yujiang Li, and Pei-Luen Patrick Rau. Extended klm for mobile phone interaction: A user study result. In CHI '10 Extended Abstracts on Human Factors in Computing Systems, CHI EA '10, pages 3517--3522, New York, NY, USA, 2010. ACM.
[10]
Lu Luo. Designing Energy and User Efficient Interactions with Mobile Systems. PhD thesis, Pittsburgh, PA, USA, 2008. AAI3305814.
[11]
Lu Luo and Bonnie E. John. Predicting task execution time on handheld devices using the keystroke-level model. In CHI '05 Extended Abstracts on Human Factors in Computing Systems, CHI EA '05, pages 1605--1608, New York, NY, USA, 2005. ACM.
[12]
Trenton Schulz. Using the keystroke-level model to evaluate mobile phones. Dissertation Univerity of Oslo, (1983), 2008.
[13]
Leonghwee Teo and Bonnie E. John. Comparisons of keystroke-level model predictions to observed data. In CHI '06 Extended Abstracts on Human Factors in Computing Systems, CHI EA '06, pages 1421--1426, New York, NY, USA, 2006. ACM.

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cover image ACM Other conferences
ACMSE '14: Proceedings of the 2014 ACM Southeast Conference
March 2014
265 pages
ISBN:9781450329231
DOI:10.1145/2638404
  • Conference Chair:
  • Ken Hoganson,
  • Program Chair:
  • Selena He
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

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Published: 28 March 2014

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ACM SE '14
ACM SE '14: ACM Southeast Regional Conference 2014
March 28 - 29, 2014
Georgia, Kennesaw

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Overall Acceptance Rate 502 of 1,023 submissions, 49%

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  • (2024)Task-based methodology to characterise immersive user experience with multivariate data2024 IEEE Conference Virtual Reality and 3D User Interfaces (VR)10.1109/VR58804.2024.00092(722-731)Online publication date: 16-Mar-2024
  • (2024)Performance Evaluation using Human Computer Interaction Models2024 8th International Conference on Information Technology (InCIT)10.1109/InCIT63192.2024.10810632(57-61)Online publication date: 14-Nov-2024
  • (2024)The Guided Evaluation MethodInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2024.103317190:COnline publication date: 1-Oct-2024
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  • (2023)MARLMUI: Multi-Agent Reinforcement Learning Approach in Mobile Adaptive User Interface2023 5th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)10.1109/REEPE57272.2023.10086785(1-5)Online publication date: 16-Mar-2023
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