[ACM Press the 2011 annual conference extended abstracts - Vancouver, BC, Canada...

Using the Keystroke-Level Model for Designing User Interface on Middle-Sized Touch Screens

Abstract The Keystroke-Level Model was developed to predict accurately task execution time for mouse-and-keyboard systems. Middle-sized touch screens are becoming much more popular so it is important to determine whether KLM can provide useful predictions for these interfaces as well. The KLMs were created using special software CogTool for three touch screen interfaces for integrated control systems and were compared to experimental data. The results showed that the KLM prediction error for middle-sized touch screens reached less than 5%. This conclusion is that KLM has

acceptable accuracy level in this environment for making predictions for the task execution times.

Keywords Keystroke-Level Model, Cognitive Modeling

ACM Classification Keywords H.1.2. Human information processing. H5.m. Information interfaces and presentation (e.g., HCI): Miscellaneous.

General Terms Experimentation

Introduction In the development of new technical devices with Human-Computer Interface (HCI) the touch screens are applying more and more often. As shown in iSuppli analysts’ reports, [7], [8], the touch screens production grows every year. The manufacturers pay special attention at middle-sized touch screen (7-17”) today. This type of touch screens are used in many kinds of devices – from popular (Apple iPad, X2 Computing iTablet and other tablet PCs, pay and information terminals) to industrial (medical, monitoring and

Copyright is held by the author/owner(s).

CHI 2011, May 7–12, 2011, Vancouver, BC, Canada.

ACM 978-1-4503-0268-5/11/05.

Evgeniy Abdulin

Institute of Informatics Problems

Russian Academy of Sciences

44 b.2 Vavilov st., 119333,

Moscow, Russia

[email protected]

CHI 2011 • Session: Text Entry & Typing May 7–12, 2011 • Vancouver, BC, Canada

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control equipment) segment. Touch screens are also used in such appliances as Intgrated Control Systems (ICSs), which are based on real-time operational systems. The ICSs are the systems for controlling audio-video technical complexes, engineering systems (lighting, conditioning etc.) and telecommunication devices in conference and assembly halls, auditoriums, and meeting rooms are the subject of this Case Study.

ICSs control real, not virtual, objects. Most of ICS operator’s actions are unable to be cancelled or returned to the original state. That is why ICSs have strict requirements to the structure of human-computer interaction (HCI) and its realization. Mistakes in HCI entail serious consequences.

For making well-grounding decisions in regard with HCI in ICSs, the GOMS (the models of Goals, Objects, Methods, and Selection rules) engineering models (KLM, CMN-GOMS, NGOMSL, CPM-GOMS) are used. These models are based on mathematical and linguistic methods. The Keystroke-Level Model (KLM), created by Card, Moran and Newell [1] is the most popular of them. However, KLM was designed for user interfaces which use a keyboard, a mouse, and, probably, a joystick, not a touch screen. The touch screen provides the next-generation type of interaction between the system and the user. The user performs the control process via touching directly virtual workspace, when the mouse and the keyboard are third-party devices between the user and the system’s virtual environment. So, there is no confidence that KLM will have equal prediction results for mouse-and-keyboard systems and systems with touch screens. The attempt to investigate KLM in regard to small-sized touch screens (up to 7”) was made by Luo and John [3] several years ago, but the aspects of middle-sized touch screens have not

been investigated yet. It is also important because the previous user experience involved devices with stylus only, but middle-sized touch screens oriented to direct touching by finger as well. The differences are in the physical feedback, in the area of screen which can be used for activating virtual elements of GUI and in the overall principles of virtual workspace organization. These differences provide the necessity of investigating applying of KLM separately for small-sized and middle-sized touch screens.

This Case Study contains experimental results of applying KLM for comparison of representative task execution times for three different graphical user interface (GUI) designs on touch screen for ICS. They are used as raw data in the process of developing the actual audio-video systems in system integrator company.

As a result, it is possible to exclude the GUI designs which have the worst task execution times, if the execution time is the only parameter for the task of making design judgment. In other cases, this value will be nevertheless one of the dominant parameters. Accordingly, it is another possibility – to take into consideration the designs with acceptable task execution times only. Previously the research of KLM’s accuracy level was done as applied to middle-sized touch screen. As shown by the results, the KLM prediction error for ICSs reached less than 5%.

Method The experiment involved researches of ICS as applied to its GUI. There were hypothesizes of experiment as follows:


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Hypothesis about model’s accuracy level (prediction error less than 5%) for KLM as applied to ICS and its interface devices (middle-sized touch screen 7-17”);

Hypothesis about GUI design with modal popup pages. As supposed, this design has significantly longer task execution time than designs without modal pages (with not modal popup pages or without popup pages at all).

The KLM with Fitts’ Law [1], [5] was used as engineering model of GUI. Models were created using CogTool v1.1.2 [9]. This software was developed in Carnegie-Mellon University, PA by Professor Bonnie John’s team [2], [3], [4].

The following hypothesis points were accepted for KLM construction to analyze representative task execution by users on GUI designs which were taken into consideration:

The main users’ actions are: preparing for pressing virtual button on touch screen’s operational surface and the button pressing presented by two operators in CogTool’s KLM realization: L (Look At) and PT (Push and Tap);

Users perform mental actions presented by M operator for two actions in representative task: making choice between possible actions on the main page of ICS and subsystems, and also making choice on a popup page (if it exists in certain GUI design);

All users’ actions are error-free and breaks-free for any other actions which are not connected with the task execution.

Participants The ICS staff in four Russian regions (Astrakhan, Ulyanovsk, Voronezh, and Tatarstan Republic) took part in the measurements as participants.

Totally sixteen participants were recruited for measurements, some of them took part in several independent observation rounds, and there were 10, 10 and 11 different participants in the first, second and third tests of designs accordingly (the total number of observation rounds is 31). The participants were recruited according to equal distribution of the participants’ age parameter. The youngest one was 25 years old and the oldest one was 58 years old in each group. Average ages are 37.1, 35.2 and 36 years old accordingly.

All the staff work on ICSs which were installed almost simultaneously. However they had different levels of experience with PCs, they worked with ICSs from 6 to 12 months.

Apparatus and materials As shown in Figure 1, the following equipment was used in the experiment:

ICS central controller with installed control software (CSW);

Interfacing device – touch screen (7 or 10 inches, resolution 800x480 pixels, size differences are not significant for the task and KLM) with GUI software;

LAN equipment (100 Mb/s);


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PC with installed CogTool and EventsAnalyzer software, and free software from ICS’ manufacturer.

Figure 1. Configuration of ICSs which took part in the study

and diagram of interaction with measuring instruments

Procedure The representative task that users performed for each GUI design is: preparing the technical complex controlled by ICS for videopresentation with recording (the videoswitching, cameras and HDD/DVD recorder are used). It contains several steps: videoswitching the signal to three different displaying devices (LCD panels, a projector, desk-top Board’s displays), setting one of the cameras to certain preset and switching on the recording session.

Task algorithms for three GUI designs are presented below:

Design 1 (with modal popup pages): The layout for this design shown in Figure 2. User starts from page 1.1. With the help of Videoswitching System virtual button he opens page 1.2. After that he chooses three displaying devices in consecutive order – LCD panels & a projector, Board’s displays and a HDD/DVD recorder by pressing virtual buttons accordingly. The modal popup page (page 1.3) is opened after each of these actions. User chooses three video sources – cameras number 1, 2 and 3 accordingly for each of the displaying device. After having chosen the choice (switching is done), modal popup page is closed automatically and the user returns to page 1.2. After the last switching the user presses Return button and goes to page 1.1.

The next stage starts by pressing Cameras System button on page 1.1 to pass to page 1.4. Then the user presses Presets button to open modal popup page (page 1.5) and activates preset number 1 by pressing p1 button. This one returns the user to page 1.4 automatically. After this, the user presses Return button and goes to page 1.1 again.

Then the last stage needs to be started by using a HDD/DVD Recorder button which opens page 1.6. The user chooses Disc Control button and popup modal page (page 1.7) is opened. After the beginning of recording session with the help of the button with a special icon, page 1.6 is opened automatically. Then the user completes the task by Return button, and the start page 1.1 is opened.

Design 2 (with not modal popup pages): The layout for this design is shown in Figure 3. The user starts from page 2.1. By Videoswitching System virtual button he opens page 2.2. After that he chooses three displaying


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devices in consecutive order – LCD panels, a projector and a HDD/DVD recorder by pressing virtual buttons accordingly. The not modal popup page (page 2.3) is opened after the first of these actions. The user chooses three video sources – cameras number 1, 2 and 3 accordingly for each of the displaying devices. After the last switching the user presses Return button and goes to page 2.1.

The next stage starts by pressing Cameras System button on page 2.1 to pass to page 2.4. Then the user activates preset number 1 by pressing the button with number 1. After this, the user presses Return button and goes to page 2.1 again.

Then the last stage needs to be started by using a HDD/DVD Recorder button which opens page 2.5. After the start of the recording by button with a special icon the user completes the task by Return button, and start page 2.1 is opened.

Design 3 (without modal popup pages at all): The layout for this design is shown in Figure 4. The user

starts from page 3.1. By Videoswitching System virtual button he opens page 3.2. After that he chooses the video source – Cameras, then three displaying devices: Board’s displays, LCD panels & a projector and Videoconference – video channel by pressing virtual buttons accordingly. After that the user presses the Videoswitching procedure performing button. After the switching the user presses Return button and goes to page 3.1.

The next stage starts by pressing Cameras System button on page 3.1 to pass to page 3.3. Then the user activates preset number 1 by pressing p1 button. After this, the user presses Return button and goes to page 3.1 again.

Then the last stage needs to be started by using a HDD/DVD Recorder button which opens page 3.5. After the start of the recording by button with a special icon, the user completes the task by Return button, and start page 3.1 is opened.


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Figure 2. Layout for Design 1


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Results All hardware and software operated without any failure during the experiment. Participants carried out their tasks confidently, without someone’s assistance. There were only two persons in the experimental room – the participant who was performing the task and the instructor. Each participant was asked about his health, mood and about some other factors that can influence the results. No complaints were registered.

The analysis of ICSs’ events (users’ actions, control messages/signals generation and their receiving) during the measurements was realized via CSW function – automated message system. Each message was associated with one event, significant for the representative task, and corresponded to system timer (SYSTIMER) value, which generated peaks every 0.1 second. There is a log file’s fragment in Figure 5.

The difference between the two system timer values in the messages allows to obtain real time difference between any two events. These massages transmitted via TCP/IP from controller to PC with measurement software, then preliminary processing was performed by ICS manufacturer’s software, and finally they were

collected and systematized as statistical results. The EventsAnalyzer v1.1 application was made by the author and it was used to automate the processing of a vast volume of raw data.

Each participant executed the representative task and was involved in one or several observation rounds. 31 observation rounds were done totally, each of them included at least 15 measurements (usually 18-20 were performed, for redundancy, preparing for checks and rejecting certain results during these checks). Finally, at least 465 valid results of representative task execution were obtained.

Special training session was conducted for each user before every observation round; the task was explained verbally and demonstrated. After that the user was asked to do training tasks three times – one with the instructor’s assistance and two by himself. Then the observed task execution was performed without any additional learning. The formula 10+5 (repeating the task 10 times, then 10 minute pause and then repeating the task 5 times) was used to reduce automatism which can influence the final result.

Figure 5. The records in the log-file, that were received by automated message system


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When measurement results were obtained from a current participant, they were preliminary analyzed by EventsAnalyzer application, the procedural check for each log file was also performed. If any mistake was registered in any fragment, it was deleted from the log file. At least 15 correct results for each participant were confirmed in each observation round after all checks, verifications and preliminary processing. Then log files were processed by EventsAnalyzer to obtain data in MS Excel format. These .xls files were the raw data for statistical analysis.

Each of the considered design had possibilities for executing the representative task. The differences among task realizations (kind of video sources, display devices etc.) were parametrical and did not influence the observations as only procedural (videoswitching for three different display devices), not parametrical aspects were actual for the experiment.

Only insignificant differences were implemented (e.g., videoswitching from cameras 1 and 2 is absolutely equal in user’s realization to videoswitching from camera 1 only for designs 1 and 2). These differences were implemented to simplify the software analysis for large volume of data. Also all the three designs provide equal feedback for the user. Every pressing a virtual button was followed by a sound signal. Design 1 shows the monitor picture as the PIP on touch screen surface and ICSs with designs 2 and 3 have special separate monitors close to the touch screen.

Designs 1, 2 and 3 were created in accordance with the requirements of three different customers. That is why they have some differences in the function hierarchy, grouping and screen layout. These differences were

accepted as insignificant because they influenced the P operator’s value only in the final result. This conclusion is based on the fact that there are not any differences in the original KLM at all (when P=1.1 sec. constantly). The KLM with Fitts’ Law was used in the experiment and its P operator’s value significantly lower than original P (from 0.2 to 0.4 sec.). The differences in 40% (as maximum) among these Ps for equal pages and unequal ones (which were involved in the experiment) give less than 2% in final result. So, these differences are insignificant for presented task.

Means for task execution times for a certain GUI design were computed at the beginning of the statistical analysis. They were done for each observation round. Then, these means were compared with KLM predictions. CogTool software was used for automated KLM computing.

Descriptive statistics for GUI designs is presented in Table 1.


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Design 1 2 3Number of participants 10 10 11Mean age of participants 37.1 35.2 36Mean task execution time 22.52 12.16 10.75Standard Deviation 3.15 1.36 2.295% confidence interval for actual Mean (±Δ) 2.25 0.97 1.48From 20.27 11.19 9.27Through 24.77 13.13 12.23High 28.19 14.55 13.61Low 19.12 10.11 7.2Median 21.57 12.05 9.85Average Absolute Deviation form Median 2.51 1.03 1.87

Table 1. Experimental data and descriptive statistics for Designs 1, 2 and 3 accordingly

Design 1 2 3Measurements result 22.52 12.16 10.75CogTool prediction 22.72 12.26 10.60CogTool error % 0.009 0.008 0.014

Table 2. The comparison of CogTool predictions and experimental results for considered designs


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CogTool evaluations for integrated control systems touch panel interfaces

10.598

22.718

12.255

10.74712.157

22.522

0.000

5.000

10.000

15.000

20.000

25.000

30.000

1 2 3Designs

Tim

e, se

cond

s

CogTool prediction Observations results

Figure 6. KLM (CogTool) evaluations for integrated control systems touch panel interfaces

Preliminary discussion

Hypothesis about model’s accuracy level (prediction error less than 5%) for KLM applied to ICS and its interface devices (middle-sized touch screen 7-17”). The hypothesis was tested by measuring the representative task execution time and comparing the obtained results with KLM predictions. The results of this testing (Table 2 and Figure 6) show

that KLM prediction error for ICS GUI on touch screen is less than 5% (in the present work less than 2%);

Hypothesis about GUI design with modal popup pages. As expected, this design has a significantly longer task execution time than designs without modal pages (with not modal popup pages or without popup pages at all). The hypothesis was


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tested by comparing measured representative task execution time for three different GUI designs (GUI with modal popup pages, with not modal popup pages or without popup pages at all accordingly). The test showed that the hypothesis is valid (Table 4 and Figure 6). ANOVA showed the statistically-significant difference for three groups: F(2,28)=76,57, p<0,001.

Discussion As a result of this research work, engineering interface model KLM can be applied for modeling GUI on middle-format touch screens, for ICSs, in particular. However the KLM prediction error in measured values was less than 2%, approximately 5% is declared for more complex tasks. The results are in agreement with previous ones have obtained by Luo and John [3] for small-sized touch screens with additional note that lower prediction error have reached for KLM.

In the conclusion, based on the results, GUI design with modal popup pages offers significantly longer task execution time than GUI without realization of this approach. The cause of this fact is in additional time required for popup page’s flipping (procedure of opening/closing the page) and additional mental actions for new information reading, interpretation and adaptation to new interface environment. Modal page also limits users free of choice by borders made by the programmer. Modal page, as definition, makes the mode in GUI, which is undesirable [6]. This restrict in real tasks causes impossibility to realize actions that are necessary for the user while he will have to complete work with this modal page or window. They are useful and convenient for the programmer because GUI’s structure looks clear from his point of view. In

addition, the programmer avoids limits of place on the screen for GUI elements placing – if he cannot place something on the main page, he can do it in additional popup one. However, in this case the user spends significantly longer time for his tasks execution, so modal popup pages/windows make worse software’s main parameters when the user tries to use it for its intended purpose. Modelessness GUI designing, avoiding popup pages is more complex for the programmer but software designed with this approach increases working and cost efficiency.

The problem of the task execution times for GUI design with not modal popup pages and another one without popup pages at all, as expected is, has perspective for studying. The results showed some differences in time between these two GUI designs – 13% approximately. I intend to study whether these approaches have significant differences for task execution times or not. The result of this study will be useful for software developers to find the most appropriate realization for the projects with effective GUI.

As a result, the analysis of the considered KLMs, the conclusion is as follows: although the representative task with input data that was specified before the execution, a large part of the HCI time (50-60%, depends on GUI design) is spent for user’s mental actions. These actions are the longest in CHI. It is supposed to one of the branches for GUI development will be searching for the way of reducing the number of mental actions in CHI. The result of this future work will be presented as the user’s time optimization for work with GUI and increasing the efficiency of implemented systems, ICS particularly.


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According to presented subject, the aspects of text-editing, system response (R operator in KLM) were not considered in this work. It should be also noted that the aspects of user learning for ICSs and its GUI on touch screens were not studied for the same reason either. The research of the ICS operator’s work in environment when the flow of input signals is received by ICS and the operator needs to do several actions simultaneously (it is necessary to respond to a new signal while he is responding to the previous one) is also beyond the considered framework. The studying of these aspects will be certainly useful for HCI research and system parameters improvement, particularly for ICSs. The research in finding conditions for the most efficient control system operator’s reaction for external signals receiving in normal and extreme environment will be also useful. It is planned to study these aspects in future.

Acknowledgements The author is very grateful to Professor David Kieras (University of Michigan, MI, USA) and Professor Bonnie John (Carnegie Mellon University, PA, USA) for useful advising.

References [1] Card, S. K., Moran, T. P., Newell, A. The psychology of human-computer interaction. Hillsdale, NJ: Lawrence Erlbaum Associates, 1983.

[2] Teo, L., John, B. E. Comparisons of Keystroke-Level Model predictions to observed data. Extended Abstracts of CHI 2006.

[3] Luo Lu, John B.E. Predicting Task Execution Time on Handheld Devices Using the Keystroke-Level Model, Conference on Human Factors in Computing Systems (CHI '05) extended abstracts on Human factors in computing systems, Portland, OR, April 2005.

[4] John B., Prevas K., Salvucci D., Koedinger K. Predictive Human Performance Modeling Made Easy Paper of CHI 2004

[5] Fitts, P.M.. The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 47, p. 381-391. 1954

[6] Raskin, J., The Humane Interface, Addison-Wesley, 2000.

[7] iPad and Competitors Spur Touch Screen Boom - Pave Way for Wider PC Usage . http://www.isuppli.com/Display-Materials-and-Systems/News/Pages/iPad-and-Competitors-Spur-Touch-Screen-Boom-Pave-Way-for-Wider-PC-Usage.aspx.

[8] Touch-Screen Interfaces Continue to Drive Growth in Signage and Professional Applications iSuppli 4Q-2009 market research report

[9] CogTool website http://www.cogtool.org/download.html


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