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Introduction to KLM
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Today, we are diving into the Keystroke-Level Model, often abbreviated as KLM. Can anyone tell me what KLM stands for?
It stands for Keystroke-Level Model!
Exactly! KLM helps us predict how long it takes for expert users to perform tasks using an interactive system. Why do you think predicting time is important in interface design?
So we can improve the design before building it?
Yes! Early evaluation saves resources. Letβs remember 'Predict to Protect' as a key concept. Now, can someone list the main operators defined by KLM?
There are Keystroke, Pointing, Homing, Drawing, Mental Preparation, and System Response!
Great summary! K for Keystroke is about pressing keys, and P for Pointing refers to using a mouse. Can anyone explain 'H'?
Homing is moving the user's hand between input devices.
Exactly right! Homing is essential since it reflects physical transitions. Let's wrap this session with 'KLM helps analyze efficiency.'
Operators and Their Importance
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Now that we've set the groundwork for KLM, let's delve into the operators. What is the significance of defining these operators in KLM?
They help break the task into measurable actions!
Exactly! Each operator has an empirical time associated with it. K, for instance, has variable times based on typing skills. What are the typical times for a skilled typist versus someone who types slowly?
I think skilled typists do about 0.08 seconds per keystroke, while average typists take around 0.20 seconds.
Spot on! And this variability is crucial for accurately modeling user interactions. If we lump everyone together, we might misestimated efficiencies. Can someone give a real-life example?
Like when design teams evaluate how long it takes to write a report? Different speeds can affect the interface design!
Yes, good example! Remember, understanding user capabilities leads to effective designs. Let's summarize that operators provide a structure to the process.
Application of KLM
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Letβs shift gears and discuss applying KLM. How do we start modeling a task?
We need to define the task clearly, right?
Correct! Step one is defining the task, followed by breaking it down into a sequence of actions β this is called method decomposition. Why is task decomposition vital?
It helps us identify every action the user will take!
Exactly! Then we assign operators to these actions. Whatβs the next step after that?
Yes! KLM relies on additive time for overall predictions. For example, if the total time is higher than acceptable, how should designers respond?
They might need to optimize the design or reduce steps in the task sequence.
Right again! Analyzing expected times helps shape better interactions. So, remember, KLM guides design towards efficiency.
Real-World Example: Copy-Paste Task
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Let's analyze a common task: copying text. What might be the steps involved?
Moving the cursor to the text, right-click to copy, and then move to paste.
Exactly! How do we apply KLM to these steps? What operators would be involved?
We would use P for pointing to the text, B for button clicks, and possibly M for mental preparation!
Yes! As we break this down with operators, we need to consider system responses, too. What impacts the final prediction?
The time it takes for the system to respond after an action!
Exactly! Always factor in system response times as they can affect your total estimate. Remember, evaluating methods with KLM can help improve design efficiency.
Introduction & Overview
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Quick Overview
Standard
KLM decomposes user actions into atomic operators, offers quantifiable predictions of task execution time for expert users, and focuses exclusively on error-free, routine tasks. It is essential for optimizing interface design before prototype creation.
Detailed
Detailed Summary of the Keystroke-Level Model (KLM)
The Keystroke-Level Model (KLM) is a foundational analytical tool within Human-Computer Interaction (HCI), crafted by Card, Moran, and Newell in 1980. It emphasizes the rigorous analysis of user performance by predictively breaking down routine tasks into defined operators, assigning empirical time values to each operator, and calculating total task execution times based on a simple additive principle. The KLM comprises six primary operators β Keystroke (K), Pointing (P), Homing (H), Drawing (D), Mental Preparation (M), and System Response (R) β each with specific, average timeframes associated with expert, error-free users. The model is best utilized for tasks performed consistently by proficient users without mistakes, thereby offering a unique perspective on design efficiency during initial stages before extensive prototyping or empirical testing occurs. Its systematic approach aids designers in pinpointing inefficiencies in user interactions, allowing for targeted design improvements that enhance usability and interface effectiveness.
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Origin of the KLM
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The KLM was developed by Card, Moran, and Newell in 1980 as a direct output of their pioneering work on human information processing and as the simplest, most fundamental component within their broader GOMS (Goals, Operators, Methods, Selection Rules) framework.
Detailed Explanation
The Keystroke-Level Model (KLM) was created in 1980 by researchers Card, Moran, and Newell. It's the result of their exploration into human information processing. KLM is fundamental to the Goals, Operators, Methods, and Selection Rules (GOMS) framework, which helps analyze how users interact with systems. Understanding its origin is key for grasping its purpose in predicting user performance in routine tasks.
Examples & Analogies
Think of KLM as the basic building block of a house, where the house represents overall user interaction models. Just as you cannot build a complex structure without a solid foundation, researchers understood that to create detailed assessments of user interaction, they first needed a straightforward model to analyze basic actions.
Core Principle of KLM
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Its central premise is to predict the total time required to complete a specified task by meticulously decomposing that task into a finite sequence of atomic, primitive, and directly observable operators. Each of these operators is assigned an empirically derived, fixed time duration.
Detailed Explanation
The KLM aims to accurately predict how long it will take to complete a task by breaking it down into very simple actions called operators. Each operator reflects a specific step, like typing a key or clicking a mouse. By analyzing these steps and their associated times, the KLM can estimate total task completion time effectively. This method helps designers understand user efficiency and anticipate potential delays.
Examples & Analogies
Imagine baking a cake. You first need to gather ingredients, mix them, bake, and finally decorate. If you know how long each step takes, you can predict the total baking time. Similarly, KLM allows designers to predict how long it will take a user to complete each step in a digital task.
Applicability Scope of KLM
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KLM is specifically tailored for analyzing highly routine, completely error-free tasks that are performed by expert users. Its focus is strictly on the physical motor actions and minimal cognitive overhead directly involved in the rapid, uninterrupted execution of a task, deliberately excluding considerations of learning, problem-solving, or error recovery.
Detailed Explanation
KLM is intended for use in evaluating tasks that are simple, routine, and performed flawlessly by users who are already trained. It emphasizes the practical actions users take, not the mental processes involved when they learn or solve complex problems. This focus allows it to yield reliable time predictions for expert performance in straightforward tasks.
Examples & Analogies
Consider a professional pianist playing a familiar piece. The pianist can focus on performance without thinking about each finger movement because they have practiced extensively. So in KLM, we are evaluating expert users who perform tasks similarly, acting almost automatically.
Additive Nature of KLM
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The model operates on a simple additive principle: the total predicted execution time for a task is calculated as the straightforward sum of the individual time durations of each operator in the derived sequence.
Detailed Explanation
KLM uses basic addition to determine the total time for completing a task. By summing the time taken for every action in the task, it provides a precise estimate of how long users will need to finish the task entirely. This additive nature makes KLM simple to apply for quick estimates.
Examples & Analogies
Think about building a Lego structure where each piece takes a certain amount of time to put together. If it takes 2 minutes for one piece and 3 minutes for another, the total time is simply 2 + 3 = 5 minutes. KLM functions in a similar straightforward way to arrive at total task time.
KLM's Fundamental Operators
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KLM defines a small, well-defined set of basic operators, each representing a distinct user or system action, with an associated average execution time. The operators include:
- K (Keystroke): The act of pressing a key/button.
- P (Pointing): Moving a pointing device to acquire a target.
- H (Homing): Moving the user's hand between input devices.
- D (Drawing): The time taken to draw segments using a pointing device.
- M (Mental Preparation): Time spent on internal cognitive processes.
- R (System Response): Time taken by the system to respond.
Detailed Explanation
KLM consists of a set of defined operators, each corresponding to an action taken by the user or the system during task execution. By categorizing actions into operators, it becomes straightforward to analyze what individual actions contribute to total task time. Each operator has a specific duration based on empirical studies, adding depth to the model's predictions.
Examples & Analogies
Picture a race car driver preparing for a race. Each action, from starting the engine (K), steering (P), switching gears (H), recalibrating instruments (M), to getting feedback from the car and race control (R), can be measured. Just as each action contributes to their overall racing time, in KLM, each operator's time adds to the total task duration.
Core Assumptions and Operational Context of KLM
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KLM operates under several key assumptions:
- Exclusive Focus on Expert User Performance
- Strict Adherence to Error-Free Task Execution
- Application to Routine Unit Tasks
- Prediction of Execution Time Only
- Requirement for Explicit Method Specification.
Detailed Explanation
KLM incorporates foundational assumptions that frame its application. It is designed specifically for expert users performing tasks without errors. KLM only focuses on routine tasks where the sequence of actions is clearly defined and does not account for complexities like learning. This clarity helps prevent excessive variables that could skew predictions and ensures straightforward usage.
Examples & Analogies
Consider a professional dog trainer working with dogs they have trained extensively. The trainer doesn't account for the dogs making mistakes during performance evaluations. Similarly, KLM assumes experts will perform without errors, maintaining clarity in its objective of measuring execution times reliably without considering learning or problem-solving difficulties.
Initial Workflow for Applying KLM
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- Task Definition: Clearly define the specific, routine unit task for analysis.
- Method Decomposition: Break the task down into a sequential list of user actions.
- Preliminary Operator Assignment: Assign KLM operators to each action.
- Iterative Refinement (with 'M' heuristics): Properly identify 'M' operators using heuristics.
- Summation for Prediction: Sum the time durations of each operator to determine total execution time.
Detailed Explanation
To use KLM effectively, a systematic workflow is established. First, the specific task needs to be defined clearly. Then, the task is divided into its sequence of actions. Each step must be linked to one of KLMβs operators. This sequencing helps predict task execution by utilizing an iterative process to ensure all cognitive elements, like 'M', are accurately captured. Finally, summing individual operator times yields the total predicted execution time.
Examples & Analogies
Think about preparing a recipe. You need to identify the dish (task definition), outline steps like chopping vegetables and boiling water (method decomposition), relate each step to specific actions (preliminary operator assignment), verify sequences to know when to prepare ingredients mentally (refinement), and finally tally the cooking times to anticipate when the meal will be ready (summation for prediction). This number-crunching leads to a clear understanding of how long the dish takes to prepare.
Definitions & Key Concepts
Learn essential terms and foundational ideas that form the basis of the topic.
Key Concepts
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Keystroke-Level Model (KLM): A model for predicting user interaction time.
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Operators: Key actions that comprise user interactions, fundamental for KLM.
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Additive Principle: Total time is the sum of each operator's execution time.
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System Response: Factor affecting overall task times.
Examples & Real-Life Applications
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Examples
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Copying text using context menus analyzes the complete user interaction through KLM.
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Estimating the time for expert users performing a routine task provides insights for design improvements.
Memory Aids
Use mnemonics, acronyms, or visual cues to help remember key information more easily.
π΅ Rhymes Time
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KLM makes time easy to see, breaking tasks into parts, just like a key.
π Fascinating Stories
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Imagine a skilled typist swiftly moving fingers over keys as they navigate through textβeach keystroke represents a step in the KLM journey.
π§ Other Memory Gems
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Remember KLMβs Operators: K for Keystroke, P for Pointing, H for Homing, D for Drawing, M for Mental prep, R for Response.
π― Super Acronyms
K-P-H-D-M-R helps recall the KLM operators in quick succession.
Flash Cards
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Glossary of Terms
Review the Definitions for terms.
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Term: KeystrokeLevel Model (KLM)
Definition:
A predictive model used in HCI to estimate the time for expert users to perform tasks by decomposing actions into basic operators.
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Term: Operators
Definition:
The basic, atomic actions in KLM, including Keystroke (K), Pointing (P), Homing (H), Drawing (D), Mental Preparation (M), and System Response (R).
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Term: Additive Principle
Definition:
The method of calculating total task time by summing the execution times of each operator involved.
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Term: Mental Preparation (M)
Definition:
An operator representing the cognitive processes users engage in, such as planning actions or recalling information.
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Term: System Response (R)
Definition:
The time taken by an interactive system to respond to user actions, affecting overall task execution time.