Les expériences contrôlées

Plusieurs méthodes d’évaluation vues en LOG 350 … Sondages Évaluation heuristique Tests d’utilisabilité Expériences Etc.

Les expériences Une partie fondamentale de la méthode scientifique Permettent de trouver des relations causales entres des conditions et leurs effets En IHM, permettent de trouver si une interface A est plus rapide/cause moins d’erreurs/etc. qu’une interface B

Les expériences On varie (manipule) au moins une variable (exemple: l’interface à utiliser). C’est la variable indépendante. Chaqu’une de ses valeurs correspond à une condition. On mesure au moins une variable (exemples: le temps, le nombre d’erreurs, la satisfaction subjective). C’est la variable dépendante. On analyse les résultats pour voir s’il y a des différences significatives.

Exemple d’expérience Les « expanding targets » Référence: M. McGuffin, R. Balakrishnan (2002). Acquisition of Expanding Targets. Proceedings of ACM Conference on Human Factors in Computing Systems (CHI) 2002, pages 57-64, http://doi.acm.org/10.1145/503376.503388

Example: Mac OS X Does this really make acquisition easier ? This is a backup slide, in case the movie doesn’t work. Does this really make acquisition easier ?

Additional motivation There are also less recent examples of schemes where a widget or some portion of a widget expands in response to the user’s focus: From left to right we have a fisheye calendar, the perspective wall, and a fisheye menu. The common theme for all of these strategies is an attempt to make better use of available screen space by displaying more information when and where it is needed. However, this talk will focus on the effects that this kind of thing has on selection of targets. --------------------------- // Various widgets have been proposed that expand dynamically in response to the user’s focus. // to make better use of screen space, and display more information when and where it’s needed. // Here are some examples where the expansion is used to display more information: // Furnas’ calendar, etc. Furnas Generalized fisheye views CHI 1986 Mackinlay, Robertson, Card The Perspective Wall CHI 1991 Bederson Fisheye Menus UIST 2000

Fitts’ Law A Target Cursor W A good place for us to start is with Fitts’ Law. Fitts’ Law describes the average time required to select a target. … There are a few different formulations of Fitts’ Law; the one that is popular now is the Shannon formulation which looks like this … --------------------- // … to answer this question, a good place to start is Fitts’ Law. // … // This is the Shannon formulation of Fitts’ Law that is generally accepted in the HCI community. Target Cursor W

Fitts’ Law Target 1 Target 2 Same ID → Same Difficulty This part could be skipped over. -------------------------- So Fitts’ Law tells us that ID is scale invariant. How is this possible ? How is it that a target farther away takes the same time to acquire ? The answer is that, although the user has farther to travel to acquire the 2nd target, they also have more distance over which they can accelerate. Furthermore, because the target is bigger, the user doesn’t have to be as precise about when to stop. Target 1 Target 2 Same ID → Same Difficulty

Fitts’ Law Target 1 Target 2 Smaller ID → Easier ---------------------- Now, on the other hand, if the target somehow covers more than the cone, … Target 1 Target 2 Smaller ID → Easier

Fitts’ Law Target 1 Target 2 Larger ID → Harder ---------------------- Likewise, if the target is strictly within the cone, … Target 1 Target 2 Larger ID → Harder

Fitts’ Law W Open-loop Closed-loop Speed Overshoot Undershoot Distance --------------------- As a final point about Fitts’ Law, I would like to show you a velocity profile of a user’s movement toward a target. Imagine the user starting on the left and having to move onto the target. Ideally, … However, in practice, the user may for example not move quite far enough … … one or more small corrective movements … The average number of corrective movements increases as the target becomes smaller or harder to select. Now, 2 points: -The current prevailing model is that the initial movement is open-loop, while the corrective motions at the end are closed-loop. -If the initial movement really is open-loop, then the target size doesn’t matter initially, and we may be able to take advantage of this fact when designing targets that expand dynamically. (Actually, when I stated this at CHI 2002, someone pointed out that the width can be important, i.e. users may perform a shorter initial movement if they know that the width is large, since they won’t be expecting to have to move all the way to the centre of the target.) Undershoot Distance

Expanding Targets Basic Idea: Big targets can be acquired faster, but take up more screen space So: keep targets small until user heads toward them Click Me ! Well, what exactly do I mean by an “expanding target” ? The basic idea is that Fitts’ Law tells us bigger buttons are easier to select, however if we make all of our buttons big we run out of screen space. So, as a compromise, let’s try to keep buttons small until the user wants to select one of them; Somewhat like this … Unfortunately, Fitts’ Law does not tell us a priori that such a target would be easier to select, because the expansion occurs after the user has already started to move towards the target. So, as a first step, we wanted to establish that expanding targets are in fact easier to select. ---------------------- // Now since Fitts’ law tells us that bigger targets are faster to acquire, why not make all our buttons and widgets bigger ? // Well, because they would take up too much screen space. Etc. Okay Cancel

Experimental Setup W Target Start Position A … to do this, we reduced things to a 1-dimensional, single target selection task … ---------------------------------- First, we wanted to confirm that expanding targets were easier to acquire. We reduced the problem to a single target, 1-dimensional selection task, to eliminate confounding factors. In our experiments, we have each user do the following: …

Experimental Setup Expansion: How ? Animated Expansion -------------------- Now, for expanding targets, there were a few different parameters that we wanted to explore. First, …

Experimental Setup Expansion: How ? Fade-in Expansion Repeat diff between two, Point out that with fade-in expansion, the full target size is immediately available (in the motor domain); this is not the case with animated expansion. --------------------------

Experimental Setup Expansion: How ? When ? P = 0.25 P is confusing; say “expansion point P”.

Experimental Setup Expansion: How ? When ? P = 0.5

Experimental Setup Expansion: How ? When ? P = 0.75 Why do we care about p ? ----------------------------------------------

Pilot Study 7 conditions: No expansion (to establish a, b values) Expanding targets Either animated growth or fade-in P is one of 0.25, 0.5, 0.75 (Expansion was always by a factor of 2) Mention why a factor of 2 was used: because we thought it would be a reasonable value for designers to use in a real UI. --------------

Pilot Study 7 conditions x 16 (A,W) values x 5 repetitions x 2 blocks x 3 participants = 3360 trials Slower ---------------------

Pilot Study: Results Time (seconds) ID (index of difficulty)

Pilot Study: Results Time (seconds) ID (index of difficulty)

Pilot Study: Results Time (seconds) ID (index of difficulty) ----------------------------- I’ve shown you how long it took to select static targets. What about the expanding targets ? Well, before I show you that data, let’s try to predict what the results might look like. By doubling the size of a target, we reduce its ID by approximately 1. This is approximately the same as shifting the base line to the right by 1. So, at best, we should expect the time to select expanded targets to coincide with the dashed line. So the dashed line is a lower bound on performance with expanding targets. Now, what we actually expected was for the selection time to fall somewhere in between these two lines. We expected the expansion to yield some advantage, but not achieve the lower bound. To our surprise, … ID (index of difficulty)

Pilot Study: Results Time (seconds) P = 0.25 ID (index of difficulty) To do: find out if the measured red lines are for fade-in or animated expansion. ----------------------- There was a significant difference between the base condition and the expanding conditions. There was no significant difference between any of the expanding conditions (i.e. between animated growth and fade-in, and also between the 3 P values). ID (index of difficulty)

Pilot Study: Results Time (seconds) P = 0.5 ID (index of difficulty)

Pilot Study: Results Time (seconds) P = 0.75 ID (index of difficulty)

Implications Pilot Study suggests the advantage of expansion doesn’t depend on P So, set P = 0.9 and perform a more rigorous study -------------------------------- If any P value will do, let’s choose a value close to 1. From a designer’s perspective, a large P value is better (because it allows us to delay expansion until the very end of the trajectory). Mention that we performed a small 1-person study that confirmed there was still an effect with P=0.9.

Full Study 2 conditions: No expansion (to establish a, b values) Expanding targets, with Animated growth P = 0.9 Expansion factor of 2 Quickly mention again why the factor of 2. --------------

Full Study 2 conditions x 13 (A,W) values x 5 repetitions x 5 blocks x 12 participants = 7800 trials

Results Time (seconds) A, W values Statistically significant ---------------- For simplicity, refer to the x-axis as “different ID values”. A, W values

Results Time (seconds) ID (index of difficulty)

Results Time (seconds) ID (index of difficulty)

Results Time (seconds) ID (index of difficulty)

Results Time (seconds) P = 0.9 ID (index of difficulty) Since our measured MT approximately coincides with the lower bound, we have essentially shown that the advantage of expansion is about as good as you could possibly expect. And this is with an expansion point P of 0.9, so the expansion only happens at the very end of the trajectory. Note that we can therefore use the lower bound as a predictive tool. ID (index of difficulty)

Implications For single-target selection task, Expansion yields a significant advantage, even when P=0.9 What about multiple targets ? Expansion point p -------------------------

(Fin des diapos sur les « expanding targets »)

Les variables dans une expérience Variables indépendantes: celles qu’on manipule (on les appelle aussi les facteurs); correspondent aux conditions (ou traitements ou niveaux) Variables dépendantes: celles qu’on mesure Variables de contrôle: celles qu’on contrôle, c.-à-d. qu’on essaie de garder constantes entre les conditions Variables aléatoires: celles qu’on laisse varier, de manière le plus aléatoire possible. Exemples: âge, sexe, profil socio-économique, etc. Comment assurer une variation aléatoire entre les conditions ? Assignation aléatoire des participants aux conditions Désavantage: Ces variables vont introduire plus de variabilité dans nos résultats Avantage: Nos résultats seront plus généraux; nos conclusions vont s’appliquer à plus de situations Variables confondantes: celles qui varient de manière systématique entre les conditions. On veut éliminer ces variables!

Régression linéaire Y X Sortie: pente, intersection, et coéfficient de corrélation de Pearson r qui est dans l’intervalle [-1,1]

Un lien causal … Dans une expérience bien contrôlée, s’il n’y a pas de variables confondantes, et on trouve que les variable dépendantes changent lorsqu’on change les variables indépendantes, on peut conclure qu’il y a un lien causal: le changements dans les variables indépendantes cause le changement dans les variables dépendantes. Dans ce cas, une corrélation impliquerait un lien causal.

… versus une corrélation simple Par contre, si on ne fait qu’observer une corrélation entre deux variables X et Y, sans contrôler les conditions, cela n’implique pas un lien causal entre eux. Il se pourrait que X a un effet sur Y Y a un effet sur X Une troisième variable, Z, a un effet sur X et Y C’est pour ça qu’on essaie d’éliminer les variables confondantes dans les expériences

Exemple Des chercheurs voulait savoir quelle variable pourrait prédire les chances qu’un conducteur de motocyclette ait un accident de moto. Ils ont cherché des corrélations entre le nombre d’accidents, et l’âge, le niveau socio-économique, etc. Ils ont trouvé que la plus forte corrélation était avec le nombre de tatous du conducteur. Évidemment, les tatous ne causent pas les accidents, ni l’inverse.

Examples of Questions to Answer in an Experiment Of 3 interfaces, A, B, C, which enables fastest performance at a given task? Does prozac have an effect on performance at tying shoe laces? How does frequency of advertisements on television affect voting behaivour? Can casting a spell on a pair of dice affect what numbers appear on them?

Elements of an Experiment Population Set of all possible subjects / observations Sample Subset of the population chosen for study; a set of subjects / observations Subjects People/users under study. The more politically correct term within HCI is “participants”. Observations / Dependent variable(s) Individual data points that are measured/collected/recorded E.g. time to complete a task, errors, etc. Condition / Treatment / Independent variables(s) Something done to the samples that distinguishes them (e.g. giving a drug vs placebo, or using interface A vs B) Goal of experiment is often to determine whether the conditions have an effect on observations, and what the effect is

Tasks to Design and Run an Experiment Choose independent variables Choose dependent variables Develop hypothesis Choose design paradigm Choose control procedures Choose a sample size Pilot experiment Often more exploratory, varying a greater number of variables to get a “feel” for where the effect(s) might be Run experiment Focuses in on the suspected effect; tries to gather lots of data under key or optimal conditions to result in a strong conclusion Analyze data Using statistical tests such as ANOVA Interpret results

Hypothesis Statement, to be tested, of relationship between independent and dependent variables The null hypothesis is that the independent variables have no effect on the dependent variables

Experimental Design Paradigms Between subjects or within subjects manipulation (entre participants vs à travers tous les participants) Example: designs with one independent variable Between subjects design One independent variable with 2 or more levels Subjects randomly assigned to groups Each subject tested under only 1 condition Within subject design Each subject tested under all conditions Order of conditions randomized or counterbalanced (why?)

What To Control Subject characteristics Task variables Gender, handedness, etc. Ability Experience Task variables Instructions Materials used Environmental variables Setting Noise, light, etc. Order effects Practice Fatigue

How to Control for Order Effects Counterbalancing Factorial Design Latin Square

Data Analysis and Hypothesis Testing Describe data Descriptive statistics (means, medians, standard deviations) Graphs and tables Perform statistical analysis of results Are results due to chance? (That is, with what probability)

ANOVA “Analysis of Variance” A statistical test that compares the distributions of multiple samples, and determines the probability that differences in the distributions are due to chance In other words, it determines the probability that the null hypothesis is correct If probability is below 0.05 (i.e. 5 %), then we reject the null hypothesis, and we say that we have a (statistically) significant result Why 0.05 ? Dangers of using this value ?

Techniques for Making Experiment more “Powerful” (i. e Techniques for Making Experiment more “Powerful” (i.e. able to detect effects) Reduce noise (i.e. reduce variance) Increase sample size Control for confounding variables E.g. psychologists often use in-bred rats for experiments ! Increase the magnitude of the effect E.g. give a larger dosage of the drug

Uses of Controlled Experiments within HCI Evaluate or compare existing systems/features/interfaces Discover and test useful scientific principles Examples ? Establish benchmarks/standards/guidelines

Exemple d’un plan d’expérience … Pour chaque participant … Pour chaque condition majeure ... * On fait des essais de réchauffement On a un certain nombre de blocs, séparés par des pauses Pour chaque bloc … On répète chaque condition mineure un certain nombre de fois * * Comment ordonner ces choses ?