User Characteristics

1. User Characteristics Yaji Sripada

2. In this lecture you learn • Implications of users and their tasks on application development • with the help of a few case studies • General HCI principles need to be contextualized to certain special user groups and their tasks Dept. of Computing Science, University of Aberdeen

3. Introduction • The starting point of this course: • Users want to understand information about something to make an informed decision • E.g users want to understand information about digital cameras before buying a digital camera (example from first lecture) • So far we learnt techniques that act as means to support users in comprehending information • data analysis (interpretation) and • visualization techniques • Real world application development involves using these techniques suitable to the application context with • Real users and • Real tasks • In this lecture we learn implications of users and their tasks on application design using case studies Dept. of Computing Science, University of Aberdeen

4. HCI Approach to user characterization • Users vary along several dimensions • Age (e.g children vs adults) • Personality (e.g. extrovert vs introvert) • Physical disabilities (e.g visual impairments) • Skills (e.g. expert vs novice) • Etc • Identify user groups among the complete set of users • Subsets of users with similar characteristics • Identify the tasks (goals) of user groups • Use this information to drive system design Dept. of Computing Science, University of Aberdeen

5. Implications of user characterization • System design in our case involves mainly designing two processes • Data analysis (interpretation) • Visualization • Data analysis (interpretation) techniques produce relevant information from raw input data • What is relevant information? • Information is relevant if it is useful for achieving user tasks (goals) • E.g for the user task of judging the safety of a scuba dive, information such as dive depth, dive duration and speed of ascent are relevant information Dept. of Computing Science, University of Aberdeen

6. Implications of user characterization (2) • How to design data analysis to extract relevant information? • User tasks (goals) determine values for parameters that regulate the runtime behaviour of data analysis methods • This means, design of data analysis is mainly influenced by user tasks • User group characteristics too have some influence on design of data analysis • Particularly with user groups with extreme characteristics • E.g. young children with poor numeric skills Dept. of Computing Science, University of Aberdeen

7. Implications of user characterization (3) • Visualization techniques present information using a suitable visualization • What is a suitable visualization? • Visualization is suitable if it enables users (with their characteristics) to understand the presented information • E.g. a learner scuba diver with poor graph reading skills might need visualizations that clearly mark dive depth and bottom time • Design of visualization is mainly influenced by user characteristics • The visualization technique used can vary with user characteristics • E.g. a doctor inspecting scuba dive data may like to view tissue saturation values and model predicted micro-bubble data Dept. of Computing Science, University of Aberdeen

8. Implications of user characterization (4) • We learnt that visualization involves mapping attributes of domain information to attributes of graphical display • E.g. scuba dive depth series is plotted as a line graph and rapid ascent information is mapped to red marks on the line graph • The mapping scheme used in the visualization can vary with user characteristics • E.g. for a user with red-green colour blindness to avoid using red for marking rapid ascent patterns on a green dive profile line graph • User tasks too have some influence on design of visualization • E.g. a researcher on diving safety requires visualizations that are lot more technical than a regular scuba diver Dept. of Computing Science, University of Aberdeen

9. Practical problem with the HCI Approach • Acquiring knowledge of user characteristics and user tasks is not easy • HCI recommends two approaches • explicit characterization – e.g. asking users directly for user characteristics • But users do not always know the required information • Implicit characterization – e.g. start with no explicit user information (cold start) but infer user characteristics from observable user behaviour • But user behaviour is not always rational Dept. of Computing Science, University of Aberdeen

10. Expert knows the implications of user groups and their tasks • One practical solution is to allow domain experts who regularly deal with different user groups and tasks to configure the system for different users • E.g, a weather forecaster may know how to analyse and present weather forecast information for more technically oriented oilrig staff Dept. of Computing Science, University of Aberdeen

11. ScubaText • ScubaText project analyses scuba dive computer data and presents the results of analysis • Graphically – annotated graph • Textually – summary of safety related information • It is assumed that learner divers may find the textual descriptions and their links to graphical displays useful for judging the safety of a dive • User group – learner divers • User task – judging the safety of a dive • Based on user characterization (learner diver) textual descriptions are included in the presentation • Real user evaluation showed that the simple user model did not work! Dept. of Computing Science, University of Aberdeen

12. Text+Annotated Graphics (D) Risky dive with some minor problems. Because your bottom time of 12.0min exceeds no-stop limit by 4.0min this dive is risky. But you performed the ascent well. Your buoyancy control in the bottom zone was poor as indicated by ‘saw tooth’ patterns marked ‘A’ on the depth-time profile. Dept. of Computing Science, University of Aberdeen

13. Experiment • Hypotheses • 1. Existing visual environments for exploring dive data are not used consistently • 2. Text+annotated graph is useful in judging the safety of a dive • 3. Current textual descriptions are appropriate for the given dive • Method • Questionnaire • 4 different forms of dive data presentation • A. the graph from the dive computer software • B. Text alone without links to graph • C. A + B • D text+annotated graph • No. of participants = 20 • Scuba divers affiliated to British Sub Aqua Club (BSAC) Dept. of Computing Science, University of Aberdeen

14. Diving is exciting; safety information is boring • Existing visualizations not used • Only 4 out of twenty participants use the visualizations consistently • View of Medical staff • Divers can deviate from recommended behaviour over long periods considerably • View of divers • Safety clashes with fun and adventure associated with diving • Exploring dive data (records of dive mistakes) are avoided completely Dept. of Computing Science, University of Aberdeen

15. Current textual descriptions are not appropriate • Most subjects felt the text was inappropriate, because • Judgemental • Data feature to dive feature mapping should be one to many • ‘Sawtooth’ pattern on the dive profile could be an artefact due to the surface of the seabed!!! • Our data interpretation module needs to be more sophisticated • More accurate domain knowledge • More sophisticated domain reasoning • Emotional content of the text is just as important (if not more) as the factual content • Emotionally tailored texts act as sugar coating on the bitter safety information Dept. of Computing Science, University of Aberdeen

16. Revised Text One of the subjects revised the output text as follows: Potentially risky dive with some minor problems. The bottom time of 12.0min exceeds no-stop limit by 4.0min requiring mandatory decompression stops. The ascent was at a constant rate within the recommended rate. The saw tooth patterns marked ‘A’ on the depth-time profile should be avoided if possible as this increases the chance of developing DCI even within the recommended decompression limits. The re-descent from 5m to 10m in the later stages of the dive should also be avoided for the same reason as saw-tooth profiles. • Revisions mostly aimed at neutralising the emotional content • But doctors who regularly treat divers with DCI prefer text with emotional content because of the direct impact it can have Dept. of Computing Science, University of Aberdeen

17. Discussion • To communicate the safety message effectively • Good understanding of user personality required • In the department we work on Affective NLG • Generating emotionally appropriate text • Elsewhere in NLG, emotional issues such as politeness are explored Dept. of Computing Science, University of Aberdeen

18. BabyTalk • BabyTalk is an ongoing research project in the department • Analyses patient related data from a neonatal intensive care unit (NICU) and presents the results of analysis • Graphically using currently available time series plots • Textually using automatically generated textual summaries of the data • There are several user groups • Doctors – need technical information to make treatment decisions • Nurses – need technical information to make shift plans • Family and Friends – need non-technical (semi-technical) information to support the parents • Designing systems for these different user groups is currently under investigation • Any ideas? Dept. of Computing Science, University of Aberdeen

19. Atlas • Another ongoing research project • Analyses geo-referenced data such as census data and presents the results of analysis • Textually which is read out by existing screen readers • The intended user group involves visually impaired users who want to access geo-referenced census data which is currently made available as thematic maps generated by GIS • User disability guides the design of both analysis and presentation of information Dept. of Computing Science, University of Aberdeen

20. Summary • Users and their tasks vary greatly • General HCI approach is useful • But need to address practical issues arising from application context Dept. of Computing Science, University of Aberdeen