HCI (Human Computer Interaction ) Unit 3 of Rgpv Syllabus

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Unit 3

Precis

GOMS is an acronym that stands for GOALS, OPERATORS, METHODS, and SELECTION RULES. A GOMS model is composed of METHODS that are used to achieve specific GOALS. The METHODS are then composed of OPERATORS at the lowest level. The OPERATORS are specific steps that a user performs and are assigned a specific execution time. If a GOAL can be achieved by more than one METHOD, then SELECTION RULES are used to determine the proper METHOD

Scientific Foundation

Since 1983, with the publication of the Card, Moran, and Newell book, GOMS has provided a framework for analyzing routine human computer interactions. GOMS has it's scientific foundation in cognitive psychology and is an improvement on earlier human factors modeling.

Methods and Information

There are basically four different GOMS models. The four models are the Keystroke-Level Model (KLM), CMN-GOMS, NGOMSL, and CPM-GOMS. These four models vary in complexity and are used to model different activities. There are different ways to construct GOMS models depending on which flavor of GOMS that is applied. For an example of constructing a GOMS model using the CMN-GOMS method see the Goms Watch Analysis. Goms Watch Analysis

Our in-class experiment used a simple activity so that everyone would be an expert in that problem domain. The experiment was to set a watch and develop models of GOMS analysis for this task. We briefly described the experiment and directions as follows:

Experiment Goal: Advance minutes by two.

Initial Condition: Watch as picture above with colon flashing.

How to operate Watch:

The watch has three buttons:


Display button Light button Set button(advances the mode)

The Set minutes:

Push Set Button until Colon/Minutes is displayed Correct the minutes by pushing the command button Press Set to make update complete

Then we formed groups to design a GOMS Task Analysis.

GOMS Task Analysis (CMN - GOMS)

Model 1

Goal: Advance minutes by two

o Goal: Enter proper set mode Press/Click Set Button .34 Press/Click Set Button .34 Press/Click Set Button .34 Press/Click Set Button .34 Press/Click Set Button .34 Verify Minutes Set 1.35

o Goal: Advance minutes by two Press/Click Command Button .34 Press/Click Command Button .34 Verify Advance 1.35

o Goal: Lock in changes Press/Click Set button .34 Press/Click Command button .34 Verify Colon Flashing 1.35

Total Time for activity = 7.11 secs (based on KLM timings)

Model 2 -

This models operations are the same as model 1, but their timings for a key press were derived from their observations with this particular apparatus.

Goal: Advance minutes by two


o Goal: Enter Set mode Press Set Button .26 Press Set Button .26 Press Set Button .26 Press Set Button .26 Press Set Button .26 Verify Set 1.35

o Goal: Advance minutes by two Press/Click Command Button .26 Press/Click Command Button .26 Verify Advance 1.35

o Goal: Lock in changes Press/Click Set button .26 Press/Click Command button .26 Verify Colon Flashing 1.35

Total Time for activity = 6.39 secs (based on estimate)

Conclusions

The purpose of this task analysis was to familiarize the class with basic GOMS analysis techniques with a simple operation. Using KLM timings was a big point of contention, so much so that one group had a similiar analysis, but used times that they thought would more accurately depict the actual time for small movements. The total time for that model was 6.39 secs. The predicted total time using KLM timings was somewhat higher than those averaged from our trial averages which was 6.03secs. We came to the conclusion that for simple operations extra cognitive time was unnecessary for all steps. People tend to learn simple operations very quickly. Another factor is that timings have to be tailored for the specific apparatus being used.

Results

GOMS can be used both both quantitatively and qualitatively. Quantitatively, it gives good predictions of performance time and learning. If, for instance, you have to choose between two systems, say you must decide whether your company should buy this or that package of office automation tools, you can apply a GOMS model. To do this, build a GOMS model of Application A and a GOMS model of Application B, and examine the quantitative predictions. Perhaps Application A has a lower up-front capital cost, but will be slower to perform frequent tasks. Perhaps Application B will be faster to perform tasks, but has a longer learning time. With these quantitative predictions, you can examine such tradeoffs in the light of what is important to your company, and what is relevant to your user-group or task situation. This is exactly how NYNEX arrived at a choice of telephone-operator workstations. Qualitatively, GOMS can be used to design training programs and help systems. The GOMS model is a careful description of the knowledge needed to perform a given task and thus it describes the content of task-oriented documentation. You only need to tell the new user what the goals are, what different methods could be used to achieve them, and when to use each method(selection rules). This approach has been shown to


be an efficient way to organize help systems, tutorials, and training programs as well as user documentation.

Applications

GOMS can be used in various application and fields. Some GOMS application examples include but are not limited to:

Telephone operator workstation using CPM-GOMS CAD system for ergonomic design using NGOMSL Intelligent tutoring system using NGOMSL Mouse driven text editor using KLM Bank deposit reconciliation system using KLM Space operations database system using KLM

All of these application topics can be found in Kieras and John, 1994.

Limitations

Card et al. (1980) provided the most detailed list of the weaknesses of GOMS. The weaknesses are as follows:

The model applied to skilled users, not to beginners or intermediates. The model doesn't account for either learning of the system or its recall after a period of disuse. Even skilled users occasionally make errors; however, the model doesn't account for errors. Within skilled behavior, the model is explicit about elementary perceptual and motor

components. The cognitive processes in skilled behavior are treated in a less distinguished fashion.

Mental workload is not addressed in the model. The model doesn't address functionality. That is the model doesn't address which tasks should

be performed by the system. The model addresses only the usability of a task on a system. Users experience fatigue while using a system. The model does not address the amount and

kind of of fatigue. Individual differences among users is not accounted for in the model. Guidance in predicting whether users will judge the system to be either useful or satisfying, or

whether the system will be globally acceptable is not included in the model. How computer-supported work fits or misfits office or organizational life is not addressed in the

model.

Introduction Models of human cognition can help developers design good interfaces by representing aspects of the human's understanding, knowledge, intentions, or processing. Such models are called generative, that is, they are used to help build artifacts instead of to evaluate them after they are built. Cognitive models can also be further divided into competence models and performance models. Competence models


represent the kinds of behavior that a user might want to perform. Performance models are quantitative measures of the demands of that behavior on the user. The most successful generative model is GOMS (Goals, Operators, Methods, and Selection), first proposed by Card, Moran and Newell in 1983. GOMS is a hierarchical model involving the decomposition of tasks into subtasks. There are several different versions of GOMS, involving both competence and performance measures.

Overview The GOMS model is an engineering model of human cognition. It looks at the human mind as a computing machine. Thus, it assumes that the user is an expert that will behave at maximum performance without any mistakes. A GOMS description consists of the knowledge that a user needs in order to carry out tasks, represented as a hierarchical breakdown of these tasks. GOMS stands for Goals, Operators, Methods, and Selection. A GOMS description consists of these four major elements. They are each described below:

Goals are the specific states that the user wants to achieve. Methods are a decomposition of the goals into subtasks. They can also be thought of as the

sequence of operators needed to accomplish a task. Operators are the basic actions that the user can perform. Thus, these are the lowest level of

decomposition of tasks. Selection is the choice between comparable methods that both accomplish a certain goal.

A GOMS description contains each of these elements, written in a hierarchical fashion. The following examples illustrate a simple GOMS description.

Examples To illustrate a GOMS description I will use an example of iconizing a window in the Solaris windowing system. The currently selected window can be iconized either by selecting the minimize option from a menu, or by clicking an icon on the header bar of the window.

Goal: Iconize the window Method 1. Use Menu 1. Move mouse to header bar 2. Right-click the mouse 3. Move mouse to minimize option 4. Left-click the mouse Method 2. Use Header Bar Button 1. Move mouse to iconize button on header bar 2. Left-click the mouse In this example the user George would always use the menu method except when the mouse only has one button:

For user George: use the "Use Icon" method unless the mouse has only one button. Then use the "Use Header Bar Button" method.


For this example, the method "Use Menu" is accomplished by serially executing the four methods beneath it. The selection occurs between using this method and the second method of clicking a button on the header bar. The selection involves an "if-then" statement about when each method would occur.

The following example focuses on saving a document in a simple text editor. The user can either select the option from the File menu or can use a shortcut of CONTROL-S. The GOMS description of this goal would be:

Goal: Save the document Method 1: Use Save Menu 1. Move mouse to File Menu 2. Left-click mouse 3. Move mouse to save option 4. Left-click mouse Method 2: Use Save Shortcut 1. Hit CONTROL-S on the keyboard George would usually use the save shortcut method except when on a certain computer where the control key is mapped to another escape key. Then George would use the menu method. This selection rule would look as follows:

For user George: if on computer X use the "Use Save Menu" method, otherwise use the "Use Save Shortcut" method.

Extensions Several extensions to the generic GOMS outlined above have been proposed to add additional information about systems.

The Keystroke-Level Model (KLM) version is a physical model used to predict operation execution times based on the individual times of the operations.

CMN-GOMS is the version proposed by Card, Moran, and Newell. It is more specific than the generic GOMS outlined above in that it uses a formal notation to specify the goal, methods, and operators that resembles pseudo-code.

Natural GOMS Language (NGOMSL) uses a well defined, yet natural language for defining the goal, method hierarchy. In addition, NGOMSL can be used to predict learning time as well as performance time.

Cognitive Perceptual Motor (CPM) GOMS removes the conception of serially performed operators. CPM-GOMS is based on the parallel multi-process stage model of human information processing.

The models differ in the levels of detail of the goal decomposition and the operators. They also differ in their representation of cognitive load. Thus, different versions would be appropriate for different analysis, depending on the tasks involved and the operations that can be measured.

Applications The GOMS model was originally intended to be used as a cognitive model to provide the ability to make a priori predictions, the ability to be learned and used by computer system designers as well as


researchers, coverage of relevant tasks, and approximation. Thus GOMS strictly measures humans on specific tasks on specific systems. Analysis of a GOMS decomposition can yield both qualitative and quantitative measures of performance. The decomposition involves thinking about what the user will want and need to perform. The depth of a goal structure can provide insight into the amount of short-term memory needed to perform those goals as well as the performance and learning times given by the operators. Experiments have shown GOMS to be reasonably accurate at predicting performance for expert systems. Additionally, GOMS can be used to evaluate or compare two competitive design or products for performance. It can be used in design to focus on the areas causing the users most problems or to find ways to speed up performance. The task decomposition is fairly simple and fast, enabling designers to examine user goals. In contrast to other forms of task analysis, this decomposition can then be used quantitatively to predict performance, and in the case of NGOMSL, learning. For example, Nynex used CPM-GOMS in designing a new operator workstation. They determined that the proposed new design would actually be slower than the old system, and thus did not build the new system - saving the company large development and training costs. Other example system that GOMS has been used with include a mouse-driven text editor, a bank deposit reconciliation system, and a space operations database system to name but a few.

Restrictions While the GOMS approach is relatively simple and inexpensive, modeling a large number of tasks could be restrictive and cumbersome. Yet the original authors intended GOMS to be used in certain situations. Card, Moran, and Newell have provided the most detailed list of weaknesses. These weaknesses include:

The model only applies to skilled users, not beginners or even intermediates. Only NGOMSL accounts for a restricted notion of learning, and none account for recall after a

period of disuse. The models don't account for the slips that even skilled users make. The cognitive processes involved in behavior are not treated in as much detail as the motor

components. The model doesn't address which tasks should be performed, it only addresses the usability of a

particular task on a system. The model does not express fatigue or differences among users on a system. The model doesn't help evaluate the usefulness of the overall system, such as the acceptance of

the system features and how the system fits into the work of the users.

Despite these restrictions, the model meets its original criteria of predicting human performance with reasonable accuracy and has been widely used by both practitioners and researchers. The model has been successful because of its simplicity and because it allows both qualitative and quantitative examination of tasks.

The Keystroke Level Model (KLM)

The Keystroke Level Model is a simplified version of GOMS in that if focuses on very low level tasks. It too was described by Card, Moran, and Newell in the early 1980s. This model focuses on unit tasks


within an interaction that use a small number of sequenced operations. KLM does not focus on more cognitive tasks such as writing a paragraph or creating a diagram as it assumes that there will be no high-level mental activity during execution of the task. Therefore, prediction times are simply the sum of the execution times of the individual operations. This allows the model to provide rapid estimates of performance without any theoretical knowledge.

The physical operations that are performed by users are the following:

K keystroking, actually hitting keys on the keyboard B pressing a mouse button P pointing, moving the mouse or other device to a target H homing, switching the hand from the mouse to the keyboard or vice versa D drawing lines using the mouse M mentally preparing for a physical action R system response, this may be ignored if the user does not have to wait for it.

Thus, each task will be broken into the sequence of the above operators. The M operator is not meant to represent cognitive thinking, but merely the recall that humans do when preparing to do expert tasks. Deciding when to use mental operators can be difficult and should be based on empirical evidence. The P operation is based on Fitt's law which states that the time to point to a certain item depends on the distance and size of the item. The individual times for each of these tasks could be dependent on the users, the hardware, and the application. Thus, the averages would have to be determined by experimentation.

Example The example on the previous page of iconizing a window can be broken up into the above physical operations, thus enabling us to compare the two ways of doing the task. The analysis would be as follows:

For the menu method:

M 1.3s

H: move hand to mouse 0.4s

P: point at the header bar 0.9s

B: right-click 0.2s

P: point at minimize option 0.7s

B: left-click 0.2s


Total 3.7s

For the button method:

M 1.3s

H: move hand to mouse 0.4s

P: point at the minimize button on the header bar 1.6s

B: left-click 0.2s

Total 3.5s

While this data is not real, it does illustrate that differences could be surprising. In the above example, the increase in the pointing due to the small size of the icon could be detrimental. Analyzing the results from performing the KLM activity could suggest ways to improve the system by showing what tasks or operators are taking the most time. By reducing the number of operators or the time for a particular operator, say pointing, the performance could be increased.

Card, Moran, Newell (CMN) Card, Moran, and Newell GOMS (CMN-GOMS) is the version proposed originally. It adds a formal notation for expressing the goal hierarchy that resembles program form. The hierarchy can decompose tasks into many levels of subtasks, with operators being executed in strictly sequential form. Using the previous examples, this form is as follows:

GOAL: ICONIZE-WINDOW [select GOAL: USE-MINIMIZE-MENU-METHOD MOVE-MOUSE-TO-HEADER-BAR RIGHT-CLICK MOVE-MOUSE-TO-MINIMIZE-OPTION LEFT-CLICK GOAL: USE-HEADER-BAR-BUTTON MOVE-MOUSE-TO-HEADER-BAR-BUTTON LEFT-CLICK ] Selection rules: User George: RULE 1: Use USE-MINIMIZE-MENU-METHOD unless another rule applies RULE 2: Use USE-HEADER-BAR-BUTTON if the mouse has one button GOAL: EDIT-DOCUMENT GOAL: OPEN-DOCUMENT


GOAL: SAVE-DOCUMENT [select GOAL: USE-SAVE-MENU-METHOD MOVE-MOUSE-TO-FILE-MENU LEFT-CLICK MOVE-MOUSE-TO-SAVE-OPTION LEFT-CLICK GOAL: USE-SAVE-SHORTCUT HIT-CONTROL-S-KEYS ] Selection rules: User George: RULE 1: Use USE-SAVE-SHORTCUT-METHOD unless another rule applies RULE 2: Use USE-SAVE-MENU-METHOD if on machine X

CMN-GOMS also applies a physical model that maps to the K and P operators of KLM. The main difference is that CMN-GOMS places the mental time in verify procedures at the end of a sequence of operators, whereas in KLM this mental time is generally in the beginning. This difference is generally not important.

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HCI (Human Computer Interaction ) Unit 3 of Rgpv Syllabus