Evaluation techniques Part 2 Lecture 14 Evaluation techniques Part 2

Today’s Topics We will cover today Evaluation through user participation Styles of evaluation Empirical methods: experimental evaluation Experimental Design Analysis of data Experimental studies on groups

Today’s Topics Observational Methods Query Techniques Psychological Methods Selecting appropriate evaluation technique

Evaluating through user Participation User participation in evaluation tends to occur in the later stages of development when there is at least a working prototype of the system in place. This may range from a simulation of the system’s interactive capabilities, without its underlying functionality.

Styles of evaluation Two distinct styles of evaluation are those performed under laboratory conditions and those conducted in the work environment or ‘in the field’

Laboratory studies Advantages: Disadvantages: Appropriate specialist equipment available uninterrupted environment Disadvantages: lack of context difficult to observe several users cooperating Appropriate if system location is dangerous or impractical for constrained single user systems to allow controlled manipulation of use

Field Studies Advantages: Disadvantages: Appropriate natural environment context retained (though observation may alter it) longitudinal studies possible Disadvantages: distractions noise Appropriate where context is crucial for longitudinal studies

Empirical methods: experimental evaluation One of the most powerful methods of evaluating a design or an aspect of a design is to use a controlled experiment. This provides empirical evidence to support a particular claim or hypothesis. It can be used to study a wide range of different issues at different levels of detail.

Empirical methods: experimental evaluation The evaluator chooses a hypothesis to test, which can be determined by measuring some attribute of participant behavior. A number of experimental conditions are considered which differ only in the values of certain controlled variables.

Empirical methods: experimental evaluation Factors to be considered for experiment design mainly includes, Participants chosen Variables tested and manipulated Hypothesis tested Experimental Design

Participants In evaluation experiments, participants should be chosen to match the expected user population as closely as possible. This will involve experimental testing with the actual users but this is not always possible. If participants are not actual users, they should be chosen to be of a similar age and level of education as the intended user group.

Participants Their experience with computers in general, and with systems related to that being tested, should be similar, as should their experience or knowledge of the task domain.

Participants It is no good testing an interface designed to be used by the general public on a participant set made up of computer science undergraduates: they are simply not representative of the intended user population

Participants Choose enough sample size of population to test all aspects of the system. Nielsen and Landauer suggest that usability testing with a single participant will find about a third of the usability problems, and that there is little to be gained from testing with more than five. Always test using large number of participants i.e.; double of five.

Variables Experiments manipulate and measure variables under controlled conditions, in order to test the hypothesis. There are two main types of variable: those that are ‘manipulated’ or changed (known as the independent variables) and those that are measured (the dependent variables).

Variables independent variable (IV) dependent variable (DV) characteristic changed to produce different conditions e.g. interface style, number of menu items dependent variable (DV) characteristics measured in the experiment e.g. time taken, number of errors.

Hypothesis A hypothesis is a prediction of the outcome of an experiment. It is framed in terms of the independent and dependent variables, stating that a variation in the independent variable will cause a difference in the dependent variable. The aim of the experiment is to show that this prediction is correct.

Hypothesis This is done by disproving the null hypothesis, which states that there is no difference in the dependent variable between the levels of the independent variable. If a result is significant at the given level of certainty, that the differences measured would not have occurred by chance (that is, that the null hypothesis is incorrect).

Hypothesis prediction of outcome null hypothesis: framed in terms of IV and DV For example “error rate will increase as font size decreases” null hypothesis: states no difference between conditions aim is to disprove this e.g. null hyp. = “no change with font size”

Experimental Design There are two main methods: between-subjects and within-subjects.

Experimental design within groups design between groups design each subject performs experiment under each condition. transfer of learning possible less costly and less likely to suffer from user variation. between groups design each subject performs under only one condition no transfer of learning more users required variation can bias results.

Between group design The advantage of a between-subjects design is that any learning effect resulting from the user performing in one condition and then the other is controlled: each user performs under only one condition. The disadvantages are that a greater number of participants are required, and that significant variation between the groups can negate any results.

Within-group Design Each user performs under each different condition. This design can suffer from transfer of learning effects, but this can be lessened if the order in which the conditions are tackled is varied between users.

Analysis of data Before you start to do any statistics: look at data save original data Choice of statistical technique depends on type of data information required Type of data discrete - finite number of values continuous - any value

Analysis - types of test parametric assume normal distribution robust powerful non-parametric do not assume normal distribution less powerful more reliable contingency table classify data by discrete attributes count number of data items in each group

Analysis of data (cont.) What information is required? is there a difference? how big is the difference? how accurate is the estimate? Parametric and non-parametric tests mainly address first of these

Experimental studies on groups Experimental studies of groups and of groupware are more difficult than the corresponding single-user experiments already considered.

Experimental studies on groups Problems with: subject groups choice of task data gathering analysis

Experimental studies on groups To organize, say, 10 experiments of a single-user system requires 10 participants. For an experiment involving groups of three, we will, of course, need 30 participants for the same number of experiments.

Experimental Studies on groups In addition, experiments in group working are often longer than the single-user equivalents as we must allow time for the group to ‘settle down’ and some rapport to develop. This all means more disruption for participants and possibly more expense payments.

Subject groups larger number of subjects  more expensive longer time to `settle down’ … even more variation! difficult to timetable so … often only three or four groups

The Task Choosing a suitable task is also difficult. We may want to test a variety of different task types: creative, structured, information passing, and so on.

The task must encourage cooperation perhaps involve multiple channels options: creative task e.g. ‘write a short report on …’ decision games e.g. desert survival task control task e.g. ARKola bottling plant

Data gathering several video cameras + direct logging of application problems: synchronisation sheer volume! one solution: record from each perspective

Analysis In true experimental tradition, we would like to see statistical differences between experimental conditions. We saw earlier that individual differences made this difficult in single-user experiments. If anything, group variation is more extreme. Given randomly mixed groups, one group will act in a democratic fashion. Thus dominating others.

Analysis Vast variation between groups solutions: within groups experiments micro-analysis (e.g., gaps in speech) anecdotal and qualitative analysis look at interactions between group and media controlled experiments may `waste' resources! anecdotal =subjectivee

Field studies Experiments dominated by group formation Field studies more realistic: distributed cognition  work studied in context real action is situated action physical and social environment both crucial Contrast: psychology – controlled experiment sociology and anthropology – open study and rich data

Observational Methods A popular way to gather information about actual use of a system is to observe users interacting with it. Techniques include Think Aloud Cooperative evaluation Protocol analysis Automated analysis Post-task walkthroughs

Think Aloud user observed performing task user asked to describe what he is doing and why, what he thinks is happening etc. Advantages simplicity - requires little expertise can provide useful insight can show how system is actually use Disadvantages subjective selective act of describing may alter task performance

Cooperative evaluation variation on think aloud user collaborates in evaluation both user and evaluator can ask each other questions throughout Additional advantages less constrained and easier to use user is encouraged to criticize system clarification possible

Protocol analysis paper and pencil – cheap, limited to writing speed audio – good for think aloud, difficult to match with other protocols video – accurate and realistic, needs special equipment, obtrusive computer logging – automatic and unobtrusive, large amounts of data difficult to analyze user notebooks – coarse and subjective, useful insights, good for longitudinal studies Mixed use in practice. audio/video transcription difficult and requires skill. Some automatic support tools available

Automated Analysis – EVA Workplace project Post task walkthrough user reacts on action after the event used to fill in intention Advantages analyst has time to focus on relevant incidents avoid excessive interruption of task Disadvantages lack of freshness may be post-hoc interpretation of events

post-task walkthroughs transcript played back to participant for comment immediately  fresh in mind delayed  evaluator has time to identify questions useful to identify reasons for actions and alternatives considered necessary in cases where think aloud is not possible

Query Techniques Relies on asking the user about the interface directly. Query techniques can be useful in eliciting detail of the user’s view of a system. Types of Query Techniques are Interviews Questionnaires

Interviews analyst questions user on one-to -one basis usually based on prepared questions informal, subjective and relatively cheap Advantages can be varied to suit context issues can be explored more fully can elicit user views and identify unanticipated problems Disadvantages very subjective time consuming

Questionnaires Set of fixed questions given to users Advantages quick and reaches large user group can be analyzed more rigorously Disadvantages less flexible less probing

Questionnaires (ctd) Need careful design Styles of question what information is required? how are answers to be analyzed? Styles of question general open-ended scalar multi-choice ranked

Psychological Methods One of the problems with most evaluation techniques is that we are reliant on observation and the users telling us what they are doing and how they are feeling. What if we were able to measure these things directly?

Psychological Methods Interest has grown recently in the use of what is sometimes called objective usability testing, ways of monitoring physiological aspects of computer use. Potentially this will allow us not only to see more clearly exactly what users do when they interact with computers, but also to measure how they feel.

Physiological methods Areas receiving more attention are, Eye tracking Physiological measurement

Eye tracking Head or desk mounted equipment tracks the position of the eye Eye movement reflects the amount of cognitive processing a display requires measurements include fixations: eye maintains stable position. Number and duration indicate level of difficulty with display saccades: rapid eye movement from one point of interest to another scan paths: moving straight to a target with a short fixation at the target is optimal

Eye Tracking Equipment

Calibrating Eye Tracking

Eye Tracking Animation

Physiological measurements Emotional response linked to physical changes These may help determine a user’s reaction to an interface Measurements include: heart activity, including blood pressure, volume and pulse. activity of sweat glands: Galvanic Skin Response (GSR) electrical activity in muscle: electromyogram (EMG) electrical activity in brain: electroencephalogram (EEG) Some difficulty in interpreting these physiological responses - more research needed

Selecting an Evaluation Method We have to decide which method is most appropriate for our needs. There is no hard and fast rule to select any method. However, there are a number of factors that should be taken into account when selecting evaluation techniques.

Factors distinguishing evaluation techniques The stage in the cycle at which the evaluation is carried out The style of evaluation The level of subjectivity or objectivity of the technique The type of measures provided The information provided

Factors distinguishing evaluation techniques The immediacy of the response The level of interference implied The resources required.

Choosing an Evaluation Method when in process: design vs. implementation style of evaluation: laboratory vs. field how objective: subjective vs. objective type of measures: qualitative vs. quantitative level of information: high level vs. low level level of interference: obtrusive vs. unobtrusive resources available: time, subjects, equipment, expertise

Summary Evaluation is an integral part of the design process and should take place throughout the design life cycle. Its aim is to test the functionality and usability of the design and to identify and rectify any problems. It can also try to determine the user’s attitude and response to the system.

Summary It can take place in a specialist laboratory or in the user’s workplace, and may or may not involve active participation on the part of the user. Experts can reduce time of evaluation by considering previous experimental results. The choice of evaluation method is largely dependent on what is required of the evaluation.