Proceedings of OZCHI 2005, Canberra, Australia. November 23 - 25, 2005. Copyright the author(s) and CHISIG
SUBJECTIVE UNDERSTANDING OF CONTEXT ATTRIBUTES: A CASE STUDY Kirsi-Maria Hiltunen1
Jonna Häkkilä1
1
Nokia Group Elektroniikkatie 3 90570 Oulu, Finland {kirsi-maria.hiltunen; jonna.hakkila}@nokia.com
Urpo Tuomela2 2
University of Oulu Linnanmaa 90570 Oulu, Finland
[email protected]
ABSTRACT Subjective understanding of context attributes may cause potential usability risks when designing context-aware mobile applications. This paper presents a survey-based study of people’s individual perceptions on defining and grouping context attributes. The findings demonstrate the problem areas that were found on several commonly used context attributes, such as temperature, time, noise level and movements.
KEYWORDS: Context-awareness, mobile devices, personalization
1.
INTRODUCTION
Research in context-aware mobile devices has gained increasing amounts of attention during the last few years. Although it seems to be commonly agreed that context-awareness has great potential to provide new types of applications (Kolari et al., 2004), enhance the efficiency of device usage (Schmidt et al., 2000), and help the user e.g. in navigation and time-scheduling (Davies et al, 2001). There also exists a consensus of opinions that the design of user interfaces for context-aware mobile devices is a challenging task. Several attributes used frequently in context-aware applications can be dependent on the person’s subjective perceptions of the meaning of the attribute. For instance when describing conditions such as dark, loud or morning. Although it seems to be implicitly known that user’s (and designer’s) subjective understanding of context may causes usability risks, the authors have not been able to find studies charting this phenomenon more closely. In this paper we present a survey-based study on the subjective understanding of context attributes, how context is perceived and how an intuitive context attribute categorization could be created. In the following, the research questions are referred to with letters A-H according to their order of appearance in the survey. The survey was conducted anonymously and the language used was Finnish. The survey included 15 participants, 5 male and 10 female. The participants were recruited from a lecture held at the University of Oulu, Finland, where the survey was distributed to 100 people present. The participants were between 18 and 34 of age, and represented different fields: humanities, educational sciences, and commercial sciences. Only three respondents claimed to be somewhat familiar with the term contextawareness. In the beginning of the survey, context-awareness was explained in brief, but with as minimal explanations and examples as possible in order to avoid influencing on the participants.
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[email protected]) 1 OZCHI 2005 Proceedings ISBN: 1-59593-222-4
2.
CONTEXT CATEGORIZATION AND CAPTURING CONTEXT
Question A. The objective of the question A was to chart the different perceptions in categorising diverse context attributes. Based on the earlier research and our own experiences, the attributes (25 in total) were selected to present the situations commonly occurring within context-aware mobile applications. The attributes were related to place, time, activity, gestures, device orientation and environmental properties, e.g.: 1) On vacation; 2) Tuesday; 3) The device is the display downwards; 4) It is afternoon; 5) A swing of hand;… 25) In a store. Participants were advised to group the attributes and name each group. An attribute could be used in more than one group, and the use of subgroups was possible. All of the 15 participants answered this question. Three types of grouping strategies were observed, and are shown along with the number of participants using the strategy and the overall number of different attribute groups: 1) 2) 3)
Personal grouping (3 participants; 11 attribute groups), Analytical grouping (11 participants; 50 groups), and Device-centered grouping (5 participants; 16 groups).
The first grouping strategy, personal grouping, refers to strategy where grouping the attributes has been done from the viewpoint of the individual’s personal life. Here, the groups were so unique that there was almost no resemblance in the groups formulated by different people. This is illustrated by participant #13, who used the group Free time that included attributes like "At home", "+30oC" and "It's dim”. The second strategy, analytical grouping, showed users grouping attributes more analytically and not from the personal point of view. For example, participant #9 used four groups: a) Time and date, which included attributes like "Tuesday" and "9 o'clock"; b) Place, including "At the store" and "I'm at work"; c) Environmental conditions and temperature, including "The device is the display downwards", "It's dim" and "Outside"; and d) Movement, including "I'm running", "The device is moving" and "A swing of hand". Although the grouping was made more analytically, the groups differed from each other. For example, some subjects included attributes like "I'm at work" (#3) and "The device is in the bag" (#11) to the place group, and things like "I'm on vacation" (#2, #11) and "It's dim" (#2) to the time group. The third category, device-centred grouping, refers to observing the context attributes from the viewpoint of the device. For example, participant #10 used categories: a) What the device 'senses', including attributes like "A swing of hand", "Outside", "It's quiet" and "The device is in the bag"; b) What am I doing, including "Walking down the stairs" and "A swing of hand"; c) What the device 'knows', including "It's afternoon" and "9 o'clock"; and d) Condition set to the device, including "I'm on vacation", "At home", and "The device is in the bag". Question B. In question B, the subjects were asked to give examples of situations and name context attributes they would like to capture in the selected cases. Only ten of the fifteen participants answered this question. Altogether, the participants gave only two situations where they would capture more than one context attribute. The first example was setting the mobile phone automatically to the silent profile in the workday evenings, and the other was reactivating a wake-up alarm automatically if the phone had not been touched within 15 minutes after turning off the first alarm. The other examples included capturing only one context attribute. The most used attribute was some kind of place (used 8 times), e.g. a library. Three of these answers used place as a method of gathering information about events when leaving a location. Time was the second most popular attribute (identified by3 respondents). Other answers included health measures, air quality (in terms of pollution), and temperature.
3.
CATEGORIZING A SELECTED CONTEXT ATTRIBUTE
In questions C-G, the subjects were asked to categorize a specific context attribute. Question C: Time of Day. All 15 participants answered question C giving altogether 88 categories for the time of day. From these, ten individual categorisations arose: early morning, morning, forenoon, day (or midday, lunchtime, daytime), afternoon, early evening (or soap operas), evening, before midnight, night, and the small hours (or the end of the night). The most common numbers of groups was five (given by five participants), and seven (given by four participants). Basically, the results demonstrated very different 2
rhythms of life. For example, morning was seen as being in between 6am and noon (participants #5, #7, #8, #9), 6am and 9am (#2), and 8am and 11am (#6). Furthermore, night was seen as being in between 1am and 6am (#10), midnight and 2am (#4), and midnight and 7am (#12, #14). The different opinions regarding the start and end time of a certain category, e.g. morning, varied from two to six different values. Question D: Temperature. All 15 participants answered this question. Altogether they gave 90 categories, from which ten were identified as being different from one another: very cold (or e.g. freezing), colder (or shivering), cold (or frost, fresh winter), cool (or e.g. around zero, not so cold), tepid (or mild, thaw), warmish, mild (or suitable, May Day), warm, warmer (or getting hotter), and hot (or too hot or heat). The most popular numbers of groups were four and seven. One participant used as many as ten groups. In this question, the answers given by the participants varied a lot. For example, two subjects could give a temperature category with the same name but with totally different temperatures, or vice versa. In addition, the number of categories varied remarkably. For example, one participant named categories cold (between -45 °C and -25 °C), shivering (between -24 °C and -15 °C), frost (between -15 °C and -6 °C), while another referred to cold (below -15 °C), and cool (between -15 °C and 10 °C). When analysing the results, it was very difficult to match the groups into a category, as every group had to be thought in respect to the categorisation used by each subject. It that all the participants were from Finland, a northern country, and that it was wintertime when the inquiry was made. For comparison, it would be interesting to do the same research in Finland in summer, as well as in another country and culture, as people would most probably name quite different categories. Question E: Volume. Question E focused on volume categorisation and included a sub-question related to decibels. All participants answered the question about their understanding of volume in decibels. Two of 15 participants indicated they understood the volume level if it was given to them in decibels, seven answered they could do it to some extend, and the remaining six responded negatively. Only ten participants out of the fifteen categorised volume. Altogether, participants gave 52 categories, from which eight major categories emerged: no sound, quiet (or unnoticeable), faint sound, normal sound (or not bothering), louder (or strong sound), loud (or annoying), unhealthy sound, and hurting sound (or bursting eardrums). Answers ranged from three to seven, with the most common response being five. Three of the ten participants categorising volume used decibels in their categorisation, whereas the rest gave examples for the categories they used. Question F: Lightness. Question F concerned the brilliance of light and included a sub-question related to its illuminance measured in lux. All fifteen participants answered the question about their perceptions of illuminance. Two participants indicated they understood the illuminance in lux to some extent, whereas 12 participants answered they could not distinguish the illuminance level measured in lux at all. Only nine participants out of the total fifteen categorised lightness, one of these categorisations not being understandable – presumably this participant misunderstood the question. This resulted only eight acceptable answers to the question F. Altogether, the eight participants gave 37 groups, from which seven major categories emerged. The most popular number of categories for lightness was four (used by six participants), with other respondents using five and seen categories. The results support the understanding that presenting lightness (or illumination) in a context-aware application is very challenging, as the users perceptions of illumination levels measured in lux tends to be very poor. Question G: Movement. Question G examined the categorisation of context attributes for movements, which could be used in triggering a function in a mobile phone. The device movements could also be considered. Nine participants categorised movement, four did not, and two indicated they would not use movement as a context information source at all. Participants gave 30 movement categories, each participant giving 1-5 different ones, from which 14 were identified for being different from one another. Participants used two different kinds of strategies for categorising movement. Some identified different kinds of movements according to the pace of the movement. These groups include e.g. slow, still and fast movement. Others divided groups according to how one moves, e.g. walking, riding a bike, driving, this being the most popular method of categorisation.
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3.1. Expectations of Usage Situations Question H charted the possibilities and potential needs that the participants perceived of having for using a context-aware application. Seven participants did not answer this question at all. Altogether, participants gave 32 places, resulting in 20 different categories. Some participants also mentioned action or features they would like to automate in a selected place. These included shutting off a phone (e.g. in an airplane), changing the profile of the phone (e.g. into silent in library), providing reminders (e.g. when leaving home), and setting the phone to a hands free mode (e.g. in a car). As the answers were collected during a university lecture, the answers included many places important to students, including lecture halls or classes, the library, and the university.
4.
DISCUSSION AND CONCLUSIONS
For successful application design, the developers of context-aware applications should have an understanding of users’ perceptions related to the topic. This includes issues of how a user distinguishes contextual information sources and their categorisation, and the meanings and needs people propose for context-aware applications. In this paper, we have investigated the views people have on defining and grouping some context attributes. Because of the small number of participants, the results do not claim to suggest solutions or recommendations to the design problems. However, the results reveal several significant trends that should be considered when designing context-aware applications, and provide interesting information that we believe is useful when planning further studies. The results suggest that people have very different perceptions of how to group context attributes and what the values or subgroups within a category should be. Although this finding agrees with common understanding on the field, the authors were still surprised by the strength of the trend. The results show that categorising on context attributes into a commonly agreed ontology is very difficult. In addition, the results indicated that some attributes easily have cultural or geographical dependency. Thus, context attributes should be researched with people from different cultures and countries. The results also suggest that attributes that can be measured but are hardly ever discussed in exact terms in everyday life, e.g. lux and decibels, would be difficult for the user to set. The study was restricted by three things. Firstly, only fifteen participants returned the inquiry. This was a disappointment considering the fact that there were 100 inquiry sheets delivered in a lecture containing 300 students. Secondly, as previously mentioned, all participants were from Finland. This may have affected the given answers, in particular the categorisation of temperature and possibly lightness. Thirdly, the participants consisted of university students. This may also have affected the answers as can be seen from the answers to question H, relating to specific places. Although the findings of the study are generally aligned with the common consensus regarding the problems of interaction design for contextaware applications, there is hardly any previously published evidence of the phenomena. Thus, the authors believe the paper contributes to the discussion by providing some experimental data on the issue, and demonstrates the issues for possible risks in user interface development for context-aware applications. This study shows that context sensitivity is very difficult subject to study with people unfamiliar with the concept. Given the problems specified above, we conclude that there is clearly a need for further studies. In the future, the authors intend to continue their research with the topic and aim to apply the findings to design guidelines of context-aware applications.
5.
REFERENCES
Davies, N., Cheverst, K., Mitchell, K., and Efrat, A. (2001). Using and Determining Location in a Context-Sensitive Tour Guide. IEEE Computer 34, 8, 35-41 Kolari, J., Laakko, T., Hiltunen, T, Ikonen, V, Kulju, M, Suihkonen R, Toivonen , S., and Virtanen, T. (2004). Context-Aware Services for Mobile Users. VTT Publication 539, Espoo, Finland, 2004. 178 p. Schmidt, A., Takaluoma, A., and Mäntyjärvi, J. Context-Aware Telephony Over WAP. (2000). Personal and Ubiquitous Computing 4, 4, 225-229. 4
