Modelling Learning of New Keyboard Layouts

CHI, pp. 4203-4215, 2017.

Cited by: 23|Bibtex|Views39|Links
EI
Keywords:
H.5reaction timecharacters per secondnew layoutlayout changeMore(13+)
Weibo:
History is replete with interfaces rejected on account of high perceived learning effort, such as attempts to tweak or more radically depart from the Qwerty layout

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 ...More

Code:

Data:

0
Introduction
  • Understanding learnability and learning of user interfaces (UIs) has long been a core objective in HCI research.
  • Encountering new, updated, or redesigned interfaces is characteristic of contemporary computer use.
  • For a user, this directs resources toward constant learning and relearning.
  • It is crucial to understand how users weigh the expected benefits of a UI against costs such as learning effort [62].
Highlights
  • Understanding learnability and learning of user interfaces (UIs) has long been a core objective in HCI research
  • History is replete with interfaces rejected on account of high perceived learning effort, such as attempts to tweak or more radically depart from the Qwerty layout [22, 20]
  • Participants: In total, N = 33 people were recruited for the experiment
  • The change in search times between blocks is displayed in Figure 3, which shows the model’s predictions with the best-fitting parameters (F = 1.06, f = 1.53, and σM = 0.60)
  • Participants: We recruited 10 people to participate in the study (4 M, 6 F, ages 21 to 36 years, mean age 26, SD = 4.4)
  • Since visual search tasks and typing differ in nature, the fit obtained speaks to the model’s value for analysing the effects of smaller changes in layout on typing performance
Methods
  • N = 33 people were recruited for the experiment.
  • Half of the participants (17) were older adults (61–71 years old, M = 68, SD = 3), and half (16) were young adults (19–35 years old, M = 25, SD = 6).
  • The authors recruited 10 people to participate in the study (4 M, 6 F, ages 21 to 36 years, mean age 26, SD = 4.4).
  • All were university students and experienced in smartphone use (3–4 hours/day) and text messaging in particular (1–2 hours/day) They rated themselves as being good or expert at touchscreen typing
Results
  • Search times: All participants except one demonstrably learned the layout of their assigned keyboards over the four days.
  • The change in search times between blocks is displayed in Figure 3, which shows the model’s predictions with the best-fitting parameters (F = 1.06, f = 1.53, and σM = 0.60).
  • Since visual search tasks and typing differ in nature, the fit obtained speaks to the model’s value for analysing the effects of smaller changes in layout on typing performance.
  • Figure 6 shows that the model acquires similar search times and projected learning curve with both Dvorak and Sath with about 15 hours of practice
Conclusion
  • The present work advances understanding of how users learn to locate keys on a keyboard.
  • The model was further fitted to predict experimental data related to performance changes when the user is relearning, typing with a slightly changed layout.
  • Without changes to the parameters obtained in these two studies, the model’s predictions proved an acceptable fit to data from an external study on the impact of a partially new layout on performance and relearning time.Today’s user interacts constantly with multiple and changing interfaces, and consistency is a critical challenge for UI design.
  • The model presented here provides a psychologically grounded and empirically tested tool for analysing this phenomenon, and helps the designers attain sufficient UI consistency when implementing new designs
Summary
  • Introduction:

    Understanding learnability and learning of user interfaces (UIs) has long been a core objective in HCI research.
  • Encountering new, updated, or redesigned interfaces is characteristic of contemporary computer use.
  • For a user, this directs resources toward constant learning and relearning.
  • It is crucial to understand how users weigh the expected benefits of a UI against costs such as learning effort [62].
  • Methods:

    N = 33 people were recruited for the experiment.
  • Half of the participants (17) were older adults (61–71 years old, M = 68, SD = 3), and half (16) were young adults (19–35 years old, M = 25, SD = 6).
  • The authors recruited 10 people to participate in the study (4 M, 6 F, ages 21 to 36 years, mean age 26, SD = 4.4).
  • All were university students and experienced in smartphone use (3–4 hours/day) and text messaging in particular (1–2 hours/day) They rated themselves as being good or expert at touchscreen typing
  • Results:

    Search times: All participants except one demonstrably learned the layout of their assigned keyboards over the four days.
  • The change in search times between blocks is displayed in Figure 3, which shows the model’s predictions with the best-fitting parameters (F = 1.06, f = 1.53, and σM = 0.60).
  • Since visual search tasks and typing differ in nature, the fit obtained speaks to the model’s value for analysing the effects of smaller changes in layout on typing performance.
  • Figure 6 shows that the model acquires similar search times and projected learning curve with both Dvorak and Sath with about 15 hours of practice
  • Conclusion:

    The present work advances understanding of how users learn to locate keys on a keyboard.
  • The model was further fitted to predict experimental data related to performance changes when the user is relearning, typing with a slightly changed layout.
  • Without changes to the parameters obtained in these two studies, the model’s predictions proved an acceptable fit to data from an external study on the impact of a partially new layout on performance and relearning time.Today’s user interacts constantly with multiple and changing interfaces, and consistency is a critical challenge for UI design.
  • The model presented here provides a psychologically grounded and empirically tested tool for analysing this phenomenon, and helps the designers attain sufficient UI consistency when implementing new designs
Tables
  • Table1: Parameters of the model and their descriptions, where values adopted from the literature are denoted with ‘a’, and empirically fitted values are in boldface (LTM parameters were fitted in study 1, and controller parameters in study 2)
  • Table2: The experimental design in study 1, with motor-calibration tasks (M), visual search with a random keyboard (R) and an assigned keyboard (A), and transcription tasks with a touchscreen mobile phone (T) (each block, apart from M, was 15 minutes long)
Download tables as Excel
Related work
  • RELATED WORK AND GOALS

    Whilst there is extensive work on modelling of learning in skilled activities [1, 3, 5, 4, 45, 61, 29] and visual search [31, 55, 34], models directly applicable to the domain of keyboards are lacking. Here we present a review of empirical findings on the effects of changing a keyboard, conducted to compile a list of phenomena that were taken as goals for the modelling effort and for discussing how existing models address these.

    Empirical Studies of Layout Learning Prior work has established that learning accounts for a large proportion of user performance in typing. In a study where the keyboard was randomised after each keypress, the attained performance (5.5 words per minute; WPM) was inferior to that in a condition wherein an unchanged Qwerty layout was used (20.5 WPM) [42]. This finding suggests that efforts to model visual search are important for understanding the development of typing performance.

    The effect of changing a keyboard has constituted a major issue in the development of optimised layouts. Results are mixed. When words are learnt repetitively, an optimised keyboard may perform better than Qwerty [11, 10]. However, if full phrases are used, relearning takes longer and it may not even surpass Qwerty in the course of an experiment. Dunlop et al [21] reported that Sath – a layout optimised for familiarity and word disambiguation – did not outperform the Qwerty layout in learning during their study. Upon change, speed dropped from 21 WPM to 13 WPM, though it rose to 18 WPM after about four hours. Such results highlight the importance of understanding how relearning is conditioned to the previous layout and how quickly a set desired level can be reached.
Funding
  • This work has received funding from the joint JST–AoF project "User Interface Design for the Ageing Population" (AoF grant 291556) as an activity of FY2014 Strategic International Collaborative Research Program (SICORP), and from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement 637991)
  • Dongcai Wen supported designing the text entry interface used in the experiments
Reference
  • John R Anderson. 1976. Language, memory and thought Hillsdale. NJ: LEA (1976).
    Google ScholarFindings
  • John R Anderson. 1995. Learning and memory. Wiley, New York.
    Google ScholarFindings
  • John R Anderson. 2007. How Can the Human Mind Occur in the Physical Universe? Oxford University Press, New York.
    Google ScholarFindings
  • John R Anderson. 2013. The architecture of cognition. Psychology Press.
    Google ScholarFindings
  • John R Anderson, D Bothell, C Lebiere, and M Matessa. 1998. An integrated theory of list memory. Journal Of Memory And Language 38, 4 (1998), 341–380. DOI: http://dx.doi.org/10.1006/jmla.1997.2553
    Locate open access versionFindings
  • Alan Baddeley. 2012. Working memory: theories, models, and controversies. Annual review of psychology 63 (2012), 1–29.
    Google ScholarLocate open access versionFindings
  • Gilles Bailly, Antti Oulasvirta, Duncan P Brumby, and Andrew Howes. 2014. Model of visual search and selection time in linear menus. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 3865–3874. DOI: http://dx.doi.org/10.1145/2556288.2557093
    Locate open access versionFindings
  • Gilles Bailly, Antti Oulasvirta, Timo Kötzing, and Sabrina Hoppe. 2013. Menuoptimizer: Interactive optimization of menu systems. In Proceedings of the ACM symposium on User interface software and technology. ACM, 331–342. DOI: http://dx.doi.org/10.1145/2501988.2502024
    Locate open access versionFindings
  • Douglas Bates, Martin Mächler, Ben Bolker, and Steve Walker. 2015. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software 67, 1 (2015), 1–48. DOI:http://dx.doi.org/10.18637/jss.v067.i01
    Locate open access versionFindings
  • Xiaojun Bi, Barton A. Smith, and Shumin Zhai. 2010. Quasi-qwerty Soft Keyboard Optimization. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 283–286. DOI: http://dx.doi.org/1753326.1753367
    Locate open access versionFindings
  • Xiaojun Bi and Shumin Zhai. 2016. IJQwerty: What Difference Does One Key Change Make? Gesture Typing Keyboard Optimization Bounded by One Key Position Change from Qwerty. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’16). ACM, New York, NY, USA, 49–58. DOI: http://dx.doi.org/10.1145/2858036.2858421
    Locate open access versionFindings
  • Michael D Byrne. 2001. ACT-R/PM and menu selection: Applying a cognitive architecture to HCI. International Journal of Human-Computer Studies 55, 1 (2001), 41–84.
    Google ScholarLocate open access versionFindings
  • Stuart K Card, Thomas P Moran, and Allen Newell. 1980. Computer text-editing: An information-processing analysis of a routine cognitive skill. Cognitive psychology 12, 1 (1980), 32–74.
    Google ScholarLocate open access versionFindings
  • Stuart K Card, Allen Newell, and Thomas P Moran. 1983. The psychology of human-computer interaction. (1983).
    Google ScholarFindings
  • Andy Cockburn, Carl Gutwin, and Saul Greenberg. 2007. A predictive model of menu performance. In Proceedings of the SIGCHI conference on Human factors in computing systems. ACM, 627–636. DOI: http://dx.doi.org/10.1145/1240624.1240723
    Locate open access versionFindings
  • Andy Cockburn, Carl Gutwin, Joey Scarr, and Sylvain Malacria. 2015. Supporting novice to expert transitions in user interfaces. ACM Computing Surveys (CSUR) 47, 2 (2015), 31:1–31:36.
    Google ScholarLocate open access versionFindings
  • Nelson Cowan. 2012. Working memory capacity. Psychology press.
    Google ScholarFindings
  • Justin Cuaresma and I Scott MacKenzie. 2013. A study of variations of Qwerty soft keyboards for mobile phones. In Proceedings of the International Conference on Multimedia and Human-Computer Interaction-MHCI. 126.1–126.8.
    Google ScholarLocate open access versionFindings
  • Arindam Das and Wolfgang Stuerzlinger. 2012. Comparing Cognitive Effort in Spatial Learning of Text Entry Keyboards and ShapeWriters. In Proceedings of the International Working Conference on Advanced Visual Interfaces. ACM, New York, NY, USA, 649–652. DOI: http://dx.doi.org/10.1145/2254556.2254676
    Locate open access versionFindings
  • Paul A David. 1985. Clio and the Economics of QWERTY. The American economic review 75, 2 (1985), 332–337.
    Google ScholarLocate open access versionFindings
  • Mark Dunlop and John Levine. 2012. Multidimensional pareto optimization of touchscreen keyboards for speed, familiarity and improved spell checking. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 2669–2678. DOI: http://dx.doi.org/10.1145/2207676.2208659
    Locate open access versionFindings
  • August Dvorak. 1943. There is a better typewriter keyboard. National Business Education Quarterly 11, 51-58 (1943), 66.
    Google ScholarLocate open access versionFindings
  • Brian D Ehret. 2002. Learning where to look: Location learning in graphical user interfaces. In Proceedings of the SIGCHI conference on Human factors in computing systems. ACM, 211–218. DOI: http://dx.doi.org/10.1145/503376.503414
    Locate open access versionFindings
  • Janine F. Hay and Larry L. Jacoby. 1999. Separating habit and recollection in young and older adults: effects of elaborative processing and distinctiveness. Psychology and aging 14, 1 (1999), 122–134. DOI: http://dx.doi.org/10.1037/0882-7974.14.1.122
    Locate open access versionFindings
  • William E Hick. 1952. On the rate of gain of information. Quarterly Journal of Experimental Psychology 4, 1 (1952), 11–26.
    Google ScholarLocate open access versionFindings
  • Joop Hox. 2010. Multilevel analysis: Techniques and Applications (2nd ed.). Routledge, Hove.
    Google ScholarFindings
  • Johan Hulleman. 2009. No need for inhibitory tagging of locations in visual search. Psychonomic Bulletin & Review 16, 1 (2009), 116–120.
    Google ScholarLocate open access versionFindings
  • Ray Hyman. 1953. Stimulus information as a determinant of reaction time. Journal of experimental psychology 45, 3 (1953), 188–196.
    Google ScholarLocate open access versionFindings
  • Christian P. Janssen and Wayne D. Gray. 2012. When, What, and How Much to Reward in Reinforcement Learning-Based Models of Cognition. Cognitive Science 36, 2 (2012), 333–358. DOI: http://dx.doi.org/10.1111/j.1551-6709.2011.01222.x
    Locate open access versionFindings
  • Bonnie E John, Konstantine Prevas, Dario D Salvucci, and Ken Koedinger. 2004. Predictive human performance modeling made easy. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 455–462. DOI: http://dx.doi.org/10.1145/985692.985750
    Locate open access versionFindings
  • Marcel A Just and Patricia A Carpenter. 1980. A theory of reading: from eye fixations to comprehension. Psychological review 87, 4 (1980), 329–354.
    Google ScholarLocate open access versionFindings
  • S. W. Keele. 1986. Motor Control. In Handbook of perception and human performance, & J. P. Thomas K. R. Boff, L. Kaufman (Ed.). Wiley, New York, 30.1–30.60.
    Google ScholarFindings
  • Nina Keith and K Anders Ericsson. 2007. A deliberate practice account of typing proficiency in everyday typists. Journal of Experimental Psychology: Applied 13, 3 (2007), 135–145.
    Google ScholarLocate open access versionFindings
  • David Kieras. 2011. The persistent visual store as the locus of fixation memory in visual search tasks. Cognitive Systems Research 12, 2 (2011), 102–112.
    Google ScholarLocate open access versionFindings
  • David E Kieras and Anthony J Hornof. 2014. Towards accurate and practical predictive models of active-vision-based visual search. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 3875–3884. DOI: http://dx.doi.org/10.1145/2556288.2557324
    Locate open access versionFindings
  • David E Kieras and David E Meyer. 1997. An overview of the EPIC architecture for cognition and performance with application to human-computer interaction. Human-computer interaction 12, 4 (1997), 391–438.
    Google ScholarLocate open access versionFindings
  • Tuomo Kujala and Dario D Salvucci. 2015. Modeling visual sampling on in-car displays: The challenge of predicting safety-critical lapses of control. International Journal of Human-Computer Studies 79 (2015), 66–78.
    Google ScholarLocate open access versionFindings
  • Paul Ung-Joon Lee and Shumin Zhai. 2004. Top-down learning strategies: can they facilitate stylus keyboard learning? International journal of human-computer studies 60, 5 (2004), 585–598.
    Google ScholarLocate open access versionFindings
  • Blake MacDonald, Pritam Ranjan, Hugh Chipman, and others. 2015. GPfit: An R package for fitting a gaussian process model to deterministic simulator outputs. Journal of Statistical Software 64, 12 (2015), 1–23.
    Google ScholarLocate open access versionFindings
  • I. Scott Mackenzie. 2002. A note on calculating text entry speed. (2002). Unpublished work. Available online at http://www.yorku.ca/mack/RN-TextEntrySpeed.html.
    Locate open access versionFindings
  • I. Scott MacKenzie and Shawn X. Zhang. 1999. The design and evaluation of a high-performance soft keyboard. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 25–31. DOI: http://dx.doi.org/302979.302983
    Locate open access versionFindings
  • I. Scott MacKenzie and Shawn X Zhang. 2001. An empirical investigation of the novice experience with soft keyboards. Behaviour & Information Technology 20, 6 (2001), 411–418.
    Google ScholarLocate open access versionFindings
  • Laurent Magnien, Jean Leon Bouraoui, and Nadine Vigouroux. 2004. Mobile Devices: Soft Keyboard Text-entry Enhanced by Visual Cues. In Proceedings of the 1st French-speaking Conference on Mobility and Ubiquity Computing. ACM, New York, NY, USA, 158–165. DOI: http://dx.doi.org/10.1145/1050873.1050908
    Locate open access versionFindings
  • Akira Miyake and Priti Shah. 1999. Models of working memory: Mechanisms of active maintenance and executive control. Cambridge University Press.
    Google ScholarFindings
  • Allen Newell. 1990. Unified theories of cognition. Harvard University Press, Cambridge, Mass.
    Google ScholarFindings
  • Allen Newell and Paul S Rosenbloom. 1981. Mechanisms of skill acquisition and the law of practice. Cognitive skills and their acquisition 1 (1981), 1–55.
    Google ScholarFindings
  • Marketta Niemelä and Pertti Saariluoma. 2003. Layout attributes and recall. Behaviour & information technology 22, 5 (2003), 353–363.
    Google ScholarLocate open access versionFindings
  • Antti Oulasvirta, Lari Kärkkäinen, and Jari Laarni. 2005. Expectations and memory in link search. Computers in Human Behavior 21, 5 (2005), 773–789.
    Google ScholarLocate open access versionFindings
  • Antti Oulasvirta, Anna Reichel, Wenbin Li, Yan Zhang, and Myroslav Bachynskyi. 2013. Improving Two Thumb Text Entry on Touchscreen Devices. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2013), 2765–2774. http://10.1145/2470654.2481383
    Locate open access versionFindings
  • Andriy Pavlovych and Wolfgang Stuerzlinger. 2004. Model for non-expert text entry speed on 12-button phone keypads. In Proceedings of the Conference on Human Factors in Computing Systems. 351–358. DOI: http://dx.doi.org/985692.985737
    Locate open access versionFindings
  • David Peebles and Corinna Jones. A model of object location memory. In Proceedings of the Annual Conference of the Cognitive Science Society.
    Google ScholarLocate open access versionFindings
  • Robert A Rescorla and Allan R Wagner. 1972. A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. Classical conditioning II: Current research and theory 2 (1972), 64–99.
    Google ScholarFindings
  • Ladislao Salmerón, José J Cañas, and Inmaculada Fajardo. 2005. Are expert users always better searchers? Interaction of expertise and semantic grouping in hypertext search tasks. Behaviour & information technology 24, 6 (2005), 471–475.
    Google ScholarLocate open access versionFindings
  • Timothy A Salthouse. 1984. Effects of age and skill in typing. Journal of Experimental Psychology: General 113, 3 (1984), 345–371.
    Google ScholarLocate open access versionFindings
  • Dario D Salvucci. 2001. An integrated model of eye movements and visual encoding. Cognitive Systems Research 1, 4 (2001), 201–220. DOI: http://dx.doi.org/10.1016/S1389-0417(00)00015-2
    Locate open access versionFindings
  • Dario D Salvucci and Kristen L Macuga. 2002. Predicting the Effects of Cell-Phone Dialing on Driver Performance. Cognitive Systems Research 3, 1 (2002), 95–102. DOI: http://dx.doi.org/10.1016/S1389-0417(01)00048-1
    Locate open access versionFindings
  • Andrew Sears, Julie A Jacko, Josey Chu, and Francisco Moro. 2001. The role of visual search in the design of effective soft keyboards. Behaviour & Information Technology 20, 3 (2001), 159–166.
    Google ScholarLocate open access versionFindings
  • Amanda L. Smith and B. S. Chaparro. 2015. Smartphone Text Input Method Performance, Usability, and Preference With Younger and Older Adults. Human Factors: The Journal of the Human Factors and Ergonomics Society 57, 6 (2015), 1015–1028. DOI: http://dx.doi.org/10.1177/0018720815575644
    Locate open access versionFindings
  • Leong-hwee Teo, Bonnie E John, and Marilyn Hughes Blackmon. 2012. CogTool-Explorer: A Model of Goal-Directed User Exploration that Considers Information Layout. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2012), 2479–2488. DOI: http://dx.doi.org/10.1145/2207676.2208414
    Locate open access versionFindings
  • Kashyap Todi, Daryl Weir, and Antti Oulasvirta. 2016. Sketchplore: Sketch and Explore with a Layout Optimiser. In Proceedings of the ACM Conference on Designing Interactive Systems. ACM, 543–555. DOI: http://dx.doi.org/2901790.2901817
    Locate open access versionFindings
  • Vladislav D. Veksler, Christopher W. Myers, and Kevin A. Gluck. 2014. SAwSu: An Integrated Model of Associative and Reinforcement Learning. Cognitive Science 38, 3 (2014), 580–598. DOI: http://dx.doi.org/10.1111/cogs.12103
    Locate open access versionFindings
  • Viswanath Venkatesh, James YL Thong, and Xin Xu. 2016. Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead. Journal of the Association for Information Systems 17, 5 (2016), 328.
    Google ScholarLocate open access versionFindings
  • R William Soukoreff and I. Scott Scott Mackenzie. 1995. Theoretical upper and lower bounds on typing speed using a stylus and a soft keyboard. Behaviour & Information Technology 14, 6 (1995), 370–379.
    Google ScholarLocate open access versionFindings
  • Eldad Yechiam, Ido Erev, Vered Yehene, and Daniel Gopher. 2003. Melioration and the transition from touch-typing training to everyday use. Human Factors: The Journal of the Human Factors and Ergonomics Society 45, 4 (2003), 671–684.
    Google ScholarLocate open access versionFindings
  • Shumin Zhai, Michael Hunter, and Barton A Smith. 2002a. Performance optimization of virtual keyboards. Human–Computer Interaction 17, 2-3 (2002), 229–269.
    Google ScholarLocate open access versionFindings
  • Shumin Zhai, Alison Sue, and Johnny Accot. 2002b. Movement model, hits distribution and learning in virtual keyboarding. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2002), 17–24. DOI:http://dx.doi.org/503380.503381
    Locate open access versionFindings
Your rating :
0

 

Best Paper
Best Paper of CHI, 2017
Tags
Comments