People and Computers XIX - The Bigger Picture: Proceedings of HCI 2005 (BCS Conference) 184628192X, 9781846281921

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People and Computers XIX - The Bigger Picture

Tom McEwan, Jan Gulliksen and David Benyon (Eds)

People and Computers XIX The Bigger Picture Proceedings of HCI2005

Springer

Tom McEwan, MSc, PgCert, MBCS, CITP, Ceng, ILTM, SEDA-accredited teacher in HE School of Computing, Napier University, Edinburgh, UK Jan Gulliksen, MSc, PhD Department of Information Technology/HCI, Uppsala University, Uppsala, Sweden David Benyon, BSc, MSc, PhD School of Computing, Napier University, Edinburgh, UK

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British Library Cataloguing in Publication Data A catalogue record for this book is availablefromthe British Library ISBN-10; 1-84628-192-X ISBN-13:978-1-84628-192-1

Printed on acid-free paper

© Springer-Verlag London Limited 2006 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, diis publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. The use of registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made.

Printed in the United Kingdom by Athenaeum Press Ltd., Gateshead 987654321 Springer Science+Business Media springeronline.com

Contents Preface: The Bigger Picture

ix

H — HCI at the Human Scale

1

"Looking At the Computer but Doing It On Land": Children's Interactions in a Tangible Programming Space Ylva Fernaeus & Jakob Tholander

3

The Usability of Digital Ink Technologies for Children and Teenagers Janet C Read

19

PROTEUS: Artefact-driven Constructionist Assessment within Tablet PC-based Low-fidelity Prototyping Dean Mohamedally, Panayiotis Zaphiris & Helen Petrie

37

The Reader Creates a Personal Meaning: A Comparative Study of Scenarios and Human-centred Stories Georg Str0m

53

What Difference Do Guidelines Make? An Observational Study of Online-questionnaire Design Guidelines Put to Practical Use Jo Lumsden, Scott Flinn, Michelle Anderson & Wendy Morgan

69

Designing Interactive Systems in Context: From Prototype to Deployment Tim Clerckx, Kris Luyten 4nev>9(MXf9

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4.3 Sensitivity The sensitivity measure gives values between 0 and 1. A value -7. Ford, G. [2005], Researching the Effects of Culture on Usability, Master's thesis. University of South Africa. Ford, G. & Gelderblom, J. H. [2003], The Effects of Culture on Performance Achieved through the Use of Human-Computer Interaction, in J. Eloff, A. Engelbrecht, P. Kotz6 & M. Eloff (eds.). Proceedings of the 2003 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists on Enablement through Technology (SAICSIT03h ACM Press, pp.218-30. Ford, G. & Kotz6, P. [2005], Designing Usable Interfaces with Cultural Dimensions, in M. F Costabile & F Patemd (eds.), Human-Computer Interaction — INTERACT '05: Proceedings of the Tenth IFIP Conference on Human-Computer Interaction, Vol. 3585 of Lecture Notes in Computer Science, Springer. Ford, G., Kotz6, P. & Marcus, A. [2005], Cultural Dimension Models: Who is Stereotyping Whom?, in N. Aykin (ed.). Proceedings of the Ilth International Conference on HumanComputer Interaction (HCI International 2005), Lawrence Erlbaum Associates. Forer, D. & Ford, G. [2003], User Performance and User Interface Design: Usability Heuristics versus Cultural Dimensions, in J. Mende & I. Sanders (eds.), Proceedings of the South African Computer Lecturer's Association 2003 Conference, University of Witwatersrand. Hall, E. T. [1959], The Silent Language, Doubleday. Hall, P., Lawson, C. & Minocha, S. [2003], Design Patterns as a Guide to the Cultural Localisation of Software, in V. Evers, K. R5se, P. Honold, J. Coronado & D. L. Day (eds.), Proceedings of the 5th Annual International Workshop on Intemationalisation of Products and Systems (IWIPS 2003), Product & Systems Intemationalisation, Inc., pp.79-88. Hartley, J. [2002], Communication, Cultural and Media Studies: The Key Concepts, Routledge. Hofstede, G. [2001], Culture's Consequences, second edition. Sage Publications.

Researching Culture and Usability — A Conceptual Model of Usability

333

Hoft, N. L. [1996], Developing a Cultural Model, in E. M. del Galdo & J. Nielsen (eds.), International User Interfaces, John Wiley & Sons, pp.74-87. Honold, P. [2000], Culture and Context: An Empirical Study for the Development of a Framework for the Elicitation of Cultural Influence in Product Usage, International Journal of Human-Computer Interaction 12(3 & 4), 327-45. Kirakowski, J. & Cierlik, B. [1999], Context of Use: ftp://ftp.ucc.ie/hfrg/baseline/CoU20.rtf (retrieved 2004-09).

Introductory Notes,

Lederer, A., Maupin, D., Sena, M. & Zhuang, Y. [2000], The Technology Acceptance Model and the World Wide Web, Decision Support Systems 29(3), 269-82. Mayhew, D. J. [1992], Principles and Guidelines in Software and User Interface Design, Prentice-Hall. Smith, A. & Chang, Y. [2003], Quantifying Hofstede and Developing Cultural Fingerprints for Website Acceptability, in V. Evers, K. ROse, P. Honold, J. Coronado & D. L. Day (eds.), Proceedings of the 5th Annual International Workshop on Intemationalisation of Products and Systems (IWIPS 2003), Product & Systems Intemationalisation, Inc., pp.89-104. Succi, M. J. & Walter, Z. D. [1999], Theory of User Acceptance of Information Technologies: An Examination of Health Professionals, in Proceedings of the 32nd Hawaii International Conference on System Sciences, IEEE Computer Society Press. http://csdl.computer.org/comp/proceedings/hicss/1999/0001/04/0001toc.htm (retrieved 2004-09). Trompenaars, F. [1993], Riding the Waves ofCultur, Nicholas Brealey Publishing. Venkatesh, V. [2000], Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation and Emotion into the Technology Acceptance Model, Information Science Research 11(4), 342-65. Victor, D. [1992], International Business Communications, Harper Collins. Wordnet [2003], Wordnet 2.0, http://dictionary.reference.com/search?q=nation (retrieved 2004-10-11). Princeton University.

HCI Down at the Interface

Distinguishing Vibrotactile Effects with Tactile Mouse and Trackball Jukka Raisamo, Roope Raisamo & Katri Kosonen Tampere Unit for Computer-Human Interaction, Department of Computer Sciences, FINS3014 University of Tampere, Finland Email:

{jr,rr,katri}@cs,uta,fi

Vibration is used for drawing users' attention to notifications in electronic devices, such as mobile phones. However, it has not been studied how much information vibrotactile effects are capable of conveying. To begin this work we studied the detection thresholds for differences in frequency and magnitude of vibration with a mouse and a trackball in the frequency range from 10 to 40Hz. Twelve participants completed 30 trials with both devices. The task in each trial was to sort five effects in a descending order based on either their magnitude (16 trials) or frequency (14 trials). The results showed that magnitude was easier to distinguish than frequency. Moreover, the participants distinguished the differences between the effects better with the mouse than with the trackball. Keywords: vibrotactile feedback, effect discrimination, haptics, tactile mouse, tactile trackball.

1 Introduction Visual and auditory modalities are commonly used in various desktop user interfaces while knowledge on other modalities and their applicability is just emerging. We believe that the haptic modality involving the sense of touch can help to create more natural and informative interfaces. This has been utilized, for example, in designing interfaces for visually impaired users [e.g. Patomaki et al. 2004]. The term haptic refers to the sense of touch and relates to virtually all the information a human perceives via physical contact. Touch is a cutaneous sense, meaning that tactile information is perceived via skin. The glabrous (hairless) skin found in palms and fingertips is especially sensitive because it has the finest resolution of the receptive fields of tactile mechanoreceptors conveying the stimuli to the brain [Goldstein 1999, pp.416-8].

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Jukka Raisamo, Roope Raisamo & Katri Kosonen

Goldstein [1999, pp.423-4] describes that touch can be used for active exploration and for passive sensing. Active touching and exploring is efficient in identifying the physical properties of objects while passive sensing is better in identifying the fine surface details [Goldstein 1999, pp.423-4]. Lederman & Klatzky [1993] explain that this is why we tend to use different ways of touching depending on what information we are looking for, both intentionally and unintentionally. In the user interfaces haptic feedback is often used in two ways. The first is to imitate the physical laws and objects of the real world. The second is to give the user contextual information about the events and actions of a system. In the first case, the feedback has a crucial role and thus its quality has to correspond to the sensations familiar from the real world. The devices and models needed for the second case are much simpler. This makes haptic feedback a realistic option for enhancing humancomputer interaction in the desktop environment. Haptic interfaces are intrinsically bi-directional, since touching always involves haptic feedback. In desktop applications, touch can be utilized to provide an additional input and output channel. A major problem with the haptic devices is that most of them are either special research prototypes or their prices are too high for an average computer user. However, there are also low-cost tactile feedback devices are available. Most of them use the TouchSense technology developed by Immersion Corporation (http://www.immersion.com) to provide vibrotactile feedback for the user. Raisamo et al. [2004] suggest that even though the quality of the feedback of these devices is far from that of the more sophisticated devices, they can still be used to add extra value to the current user interfaces. As using vibrating alerts in mobile phones and pagers has become a common practice, it can be assumed that the use of the tactile modality will grow fast in the near future; both as supportive information channel providing better accessibility for people with special needs and as enriching the mobile contents for all users. This emergence is more than expected because the tactile modality has several interesting characteristics: it is always ready to receive information, it attracts one's attention effectively, and it is private. Thus, it is not a surprise that tactile feedback has recently been applied to areas that are present in our everyday life. Research has been done, for example, to add vibrotactile feedback to strengthen meaning and expression in instant messaging [Rovers & van Essen 2004], to reduce drivers' navigational workload in automobiles [van Erp & van Veen 2001], as well as to enhance the interaction in hand-held mobile devices [O'Modhrain 2004]. In these application areas the contribution of vibrotactile feedback varies but it still brings a new dimension compared to current interfaces. As a part of this progress, Brewster & Brown [2004] introduced a new interaction paradigm called tactile icon, or Tacton. Tactons are defined as "efficient language to represent concepts at the user interface. Tactons encode information by manipulating the parameters of cutaneous perception" [Brewster & Brown 2004]. As tactile feedback is being applied to new application areas there is room for research on various domains. There are some recent studies on detection thresholds for subtle haptic effects with more advanced haptic devices [Dosher et al. 2001]. Although there are some general guidelines available for using vibrotactile feedback in various

Distinguishing Yibwtactile Effects with Tactile Mouse and Trackball domains [ETSI 2002; van Erp 2002], there is still no clear picture how its various parameters are perceived by the user. This kind of basic research is especially essential when looking for the best way to use low-quality feedback devices in user interfaces. In this paper, we describe an experiment that was conducted tofindout how well different feedback magnitudes and frequencies can be distinguished with two tactile feedback devices, a mouse and a trackball. The main contribution of this study is to provide basic knowledge of the effect of magnitude and frequency in distinguishing vibrotactile effects that can be used in various domains.

2 Previous Work When using a mouse in a standard graphical user interface, for example, the act of picking up and dragging an icon is moderately intuitive and is associated with the real-life action of physically moving objects. The standard computer mouse, however, only provides passive haptic feedback; we obtain both visual and kinaesthetic information about the movement and position of the mouse. Furthermore, the button clicks can be perceived as tactile and auditory feedback and dragging causes a light feel of passive force feedback. However, these outputs are more or less sporadic and cannot be directly controlled by the application. Even though touch input devices, such as different kinds of touchscreens, tablets and touchpads, have been available for years, haptic feedback devices are not widely spread. Some promising results have been achieved with various prototypes of tactile mice in single interaction events, for example in target selection [Akamatsu et al. 1995; Gobel et al. 1995] and movement times [Akamatsu & MacKenzie 1996]. In addition to previous ones, Hughes & Forrest [1996] have conducted a study on the use of touch in the perceptualization of data. However, the use of tactile feedback in more complex and realistic interaction tasks has not been widely studied, neither has the detection of tactile effect properties on existing low-cost tactile devices. The studies mentioned above show that tactile devices can be used effectively at least in basic interaction tasks with visual feedback. Because of the low quality of the tactile feedback that these devices produce, they are at their best in providing an additional feedback channel that can be used, for example, to inform the user of events and to indicate the states of ongoing processes. However, the tactile channel has potential for much more than giving simple one-bit information. Tactile feedback similar to what we tested has previously been used in a relatively small role to support visual feedback [Raisamo et al. 2004]. The vibrotactile feedback devices used in this study, the Logitech iFeel mouse (see http://www.logitech.com) and the Kensington OrbitSD trackball (see http://www.kensington.com), are based on the TouchSense technology developed by Immersion Corporation. Both the devices are inexpensive and they have a small motor inside them to produce the vibrations. Earlier work done with the iFeel mouse suggested that some users find both the feel and the sound of the tactile mouse rather unpleasant [Raisamo et al. 2004]. This evokes the question of adequate but comfortable detection thresholds for the frequency and magnitude to be able to take the full advantage of such low-frequency tactile feedback devices.

339

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Jukka Raisamo, Roope Raisamo & Katri Kosonen

Figure 1: The Logitech iFeel mouse (left), a DC motor inside the devices (middle) and the Kensington Technologies OrbitSD trackball (right).

Some research concerning the tactile threshold has been done but most of it relates to either direct skin contact or high quality haptic devices. Thus, these results cannot directly be applied to the devices used in this study. For example, for frequency, a minimum detection threshold of 20 percent has been suggested by van Erp [2002]. Van Erp also states that the minimum threshold is not enough if there is a need to code more than a simple message and that there is major individual differences in perceiving stimulus intensity.

3 Experiment The experiment was carried out in a usability laboratory. The participants wore hearing protectors during the test to mute most of the noise caused by the vibrating devices. Before the session, the participants were asked to fill in a questionnaire on their background and previous experience in using tactile and force feedback devices. During the test, the participants evaluated their subjective performance individually for each task and marked it on the paper. At the end, they filled in a questionnaire about their opinions on the devices and they were briefly interviewed to get more detailed comments. The program saved experimental data for empirical analysis.

3.1 Participants Twelve volunteered participants, eight students and four employees of the Department of Computer Sciences participated in the experiment. Five of them were female and seven were male, and their age was between 21 and 33 years (mean 25.5, SD = 3.1). All participants were experienced computer and mouse users, but only two of them had used a trackball before. Five of the participants had previously tried the tactile mouse, but none of them had tried the tactile trackball. None of the participants used either of the tested devices on the regular basis and only three of them were frequent users of game controllers that provide vibrotactile feedback.

3.2 Apparatus The tactile feedback devices used in the experiment are shown in Figure 1 (Logitech iFeel Mouse on the left and the Kensington Orbit3D trackball on the right). The vibrotactile feedback of the devices is generated with a small electrical partialrotation direct current (DC) motor (Figure 1, in the middle). A similar type of motor

Distinguishing Vibrotactile Effects with Tactile Mouse and Trackball

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is linked to the body of both the devices with a nylon cam that can only rotate a few degrees per rotation cycle. The frequency of the vibration is based on the speed the cam completes a cycle, and the magnitude on the angle of rotation. A Java application (Figure 2) with the Immersion TouchSense SDK for Java was used in the experiment to provide the tactile effects. The application presented the participants arranging tasks that had five similar rectangles called tactile objects shown on the screen. The application provided vibrotactile feedback when the mouse was moved over them. The feedback lasted as long as the mouse cursor was kept over the object. The objects were placed on the screen in a curve so that also the furthermost objects would be easier to compare (see Figure 2). There was a combo box under each tactile object to assign indexes for the objects.

3.3 Tasks The participants were asked to arrange the feedback in a descending order based on the property being varied. The participant needed to select an appropriate index (1..5) from the combo box for each tactile object where one meant the effect with the highest magnitude or frequency and five the effect with the smallest. By default all the effects were given an empty index and at least one object had to be indexed before the participant could proceed to the next task. Also, each index could be assigned for only one tactile object. Each participant ran the test twice, once with both devices. In the test, the participants were given two rounds of tasks that contained sixteen (magnitude) or fourteen (frequency) quintets of tactile objects (as in Figure 2). All the tactile objects had one constant property, the frequency (25Hz) or the magnitude (8000, in scale 0.. 10000), but the other property varied according to the designed pattern. The constant values for both magnitude and frequency were decided on the basis of a pilot test conducted to roughly estimate the limits of the devices.

Jukka Raisamo, Roope Raisamo & Katri Kosonen

342 Threshold category

(%)

Number of tactile quintets (magnitude)

Number of tactile quintets (frequency)

10 15 20 25 30 40 50

3 3 3 2 2 2 1

4 3 2 2 2 1

Table 1: The distribution of detection threshold categories used in the experiment.

In the magnitude round, the effect magnitude was varied from 2000 to 10000 units (in scale 0.. 10000) with a constant frequency of 25Hz. The detection threshold categories for this round were 10, 15, 20, 25, 30, 40, and 50 percent, meaning the difference in the intensity between two consecutive effects. In the frequency round, the frequencies of the effects varied from 10 to 40Hz with a constant magnitude of 8000 units. The detection threshold categories for the frequency round were 10, 15, 20, 25, 30, and 40 percent, respectively. The tasks in both rounds were designed to cover the whole range within each detection threshold category. In practice, this means that the categories with lower threshold have more tasks to cover the range than categories with the higher ones. For magnitude tasks the number of categories varied from 3 to 1 and for frequency tasks from 4 to 1. More detailed data on the distribution of threshold categories can be found in Table 1. The order of the devices and the rounds was counter-balanced between participants. The order of the tasks inside rounds and the order of the tactile objects inside each task were randomized within a test to avoid learning effects. Also, the threshold categories had no effect to the order of the tasks but were used only in the analysis of the results. Average task completion times, number of touches, touch durations, and number of right answers were calculated from all tasks separately. The right answers were counted per object so that in one task there could be a maximum of five of those.

4 Results A repeated measures ANOVA between the means calculated for the detection threshold categories revealed significant main effects in the magnitude tasks for threshold (Fi,6 = 24.86, p < 0.001) as well as in the frequency tasks (Fi,5 = 3.90, p < 0.05). There were also significant effects for device in the magnitude tasks (Fi,6 = 55.49, p < 0.001) and in the frequency tasks (Fi,5 = 4.96, p < 0.05). Figure 3 shows the average number of right answers for both task types with the mouse and the trackball as a function of detection threshold category. Results of a paired samples r-test for the number of right answers showed that the mouse was significantly more accurate in distinguishing both the magnitude (f6 = 8.5, p < 0.001) and the frequency (^5 = 4.0, p < 0.05) of the effects. Furthermore,

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343

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