Lecture 2: Keystroke-Level Model - I - 3.2 | Module 3: Model-based Design | Human Computer Interaction (HCI) Micro Specialization
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3.2 - Lecture 2: Keystroke-Level Model - I

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Interactive Audio Lesson

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Introduction to the Keystroke-Level Model (KLM)

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0:00
Teacher
Teacher

Today, we will start with the Keystroke-Level Model, or KLM. Can anyone tell me the purpose of this model in Human-Computer Interaction?

Student 1
Student 1

Is it to predict how quickly users can complete tasks with technology?

Teacher
Teacher

Exactly! KLM aims to predict the time required for expert users to perform routine tasks by breaking them down into observable actions. It's part of a framework that helps us understand user interactions more clearly.

Student 2
Student 2

What kind of tasks does KLM apply to?

Teacher
Teacher

KLM is designed for expert users engaged in routine tasks that are executed without errors. This means it doesn’t consider learning stages or complex problem-solving.

Student 3
Student 3

How does it actually calculate the time?

Teacher
Teacher

Great question! KLM calculates total time by summing the fixed times assigned to each operator involved in performing the task.

Student 4
Student 4

What are those operators?

Teacher
Teacher

There are five primary operators: K for keystrokes, P for pointing, H for homing, D for drawing, M for mental preparation, and R for system response. Each has a specified time, which varies based on user skill or context.

Teacher
Teacher

In summary, KLM forms the backbone of user interaction analysis by enabling us to quantitatively assess interface efficiency through structured decomposition. Any questions before we move on?

Detailed Discussion on KLM Operators

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Teacher
Teacher

Let’s delve into the KLM operators in detail. Who can define what a 'Keystroke' operator (K) is?

Student 1
Student 1

It’s pressing a key or button on a device, right?

Teacher
Teacher

Correct! The duration for a keystroke can vary greatly, from very fast for expert typists to considerably longer for those who are less proficient. Can anyone point out another operator?

Student 2
Student 2

How about 'Pointing' (P)?

Teacher
Teacher

Absolutely. Pointing involves moving a device to select a target, and it typically takes an average time based on empirical studies. Next, we have 'Homing'. Can anyone tell me what that is?

Student 3
Student 3

It's moving your hands between devices, like from a keyboard to a mouse.

Teacher
Teacher

Right! The 'Homing' time adds to task duration and can be crucial in user experience design. Now, let’s discuss 'Mental Preparation' (M). Why might this operator be challenging to position?

Student 4
Student 4

Because it depends on how much thinking or planning the user is doing, right?

Teacher
Teacher

Exactly. It’s often subjective and can vary widely from user to user. Lastly, we have 'System Response' (R), which factors in the delays from the system itself. Any questions on the operators?

Student 1
Student 1

So, all these operators come together to create a picture of how efficient an interface is?

Teacher
Teacher

Correct! It allows us to gauge usability quantitatively, which is especially valuable in the design process. Let’s summarize: KLM's operators allow us to break down interactions effectively, making predictions about user performance clearer.

Assumptions of KLM

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0:00
Teacher
Teacher

Now that we know about the operators, it's crucial to discuss the assumptions behind KLM. What is one major limitation?

Student 2
Student 2

It only works with expert users, right?

Teacher
Teacher

Exactly! KLM assumes the user will only perform tasks correctly and without mistakes, which limits its application. What happens if a user is a novice?

Student 3
Student 3

KLM wouldn't be effective because novices might make errors that KLM can't accommodate.

Teacher
Teacher

Correct again! Additionally, as KLM is applied to routine tasks, it doesn’t account for complex problem-solving or creative thinking. Can anyone think of real-world examples where this might pose issues?

Student 1
Student 1

Designing software for beginners would be tricky because they might take a long time learning.

Teacher
Teacher

Exactly. Understanding these assumptions allows us to use KLM effectively and also realize when it's inadequate. Remember, these limitations help guide us in selecting more suitable models when needed. To summarize, KLM is restricted to expert performance in routine tasks without errors, and doesn’t accommodate the learning curve.

Introduction & Overview

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Quick Overview

This section introduces the Keystroke-Level Model (KLM), detailing its operators and the assumptions involved in its application for predicting expert user performance on routine tasks.

Standard

The Keystroke-Level Model (KLM), developed by Card, Moran, and Newell, is a predictive framework that breaks down tasks into observable operators with fixed time durations. This lecture focuses on explaining the operators, their typical execution times, and the specific context in which KLM is applicable, primarily addressing expert users engaged in routine and error-free tasks.

Detailed

Lecture 2: Keystroke-Level Model - I

The Keystroke-Level Model (KLM) is a fundamental predictive model developed by Card, Moran, and Newell in 1980, serving as a core component within their broader GOMS framework. Its main objective is to predict the total time necessary to complete a specified task by systematically decomposing that task into a sequence of atomic, directly observable actions known as operators. Each operator is assigned a fixed, empirically derived time duration, enabling the efficient estimation of task performance, especially for expert users.

Key Aspects Covered in This Section

  1. Operators of the KLM: The section introduces the five primary operators used in KLM:
  2. K (Keystroke): Pressing a key or button, with varying times based on typist skill.
  3. P (Pointing): Moving a pointing device to select a target, assuming an average time.
  4. H (Homing): Moving hands between different input devices (e.g., keyboard to mouse).
  5. D (Drawing): The time taken to draw a line segment, relevant for graphical tasks.
  6. M (Mental Preparation): Time spent in cognitive processing, which can be quite variable.
  7. R (System Response): The time it takes for the system to respond to a user's action, which is context-dependent.
  8. Assumptions of KLM: The model applies exclusively to expert users performing routine, error-free tasks. It does not consider the complexities of learning curves, error recovery, or creative problem-solving.
  9. Calculation Method: The total predicted execution time for completing a task is calculated by summing the estimated times of each operator involved in the task. The simplicity and additive nature of the model are highlighted as strengths, allowing quick predictions during the design process before prototypes are created.

In summary, KLM provides a streamlined approach to estimate user performance through focused analysis of user interactions with systems, emphasizing its utility in design and usability assessments.

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Deep Dive into the Keystroke-Level Model (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) is a tool designed to estimate how long it will take an expert user to complete a task by breaking that task down into individual actions called 'operators'. This model was created in 1980 by researchers Card, Moran, and Newell, as a simplified version of a larger conceptual framework known as GOMS. The idea is that by carefully examining each individual action that makes up a task, we can predict the total time needed to accomplish that task.

Examples & Analogies

Imagine if you wanted to bake a cake. Instead of thinking about the entire process at once, you'd break it down into parts: gathering ingredients, mixing the batter, pouring it into a pan, and so on. Each of these steps would have its own time requirements. KLM does this for digital tasks by analyzing each keyboard press or mouse movement involved in a task.

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 main idea of KLM is that we can predict how long it takes to finish a task by breaking it down into small, clear actions (called operators). Each operator, like pressing a key or clicking a mouse, has a specific time based on real-world observations. For instance, pressing a key might take about 0.28 seconds, and such time estimates help build a total time for the entire task.

Examples & Analogies

Think of it like assembling a piece of furniture. Instead of managing the whole assembly process in your head, you focus on each part: opening the box, sorting pieces, finding the right tools, and following instructions. Each of these actions takes time, and if you know the time for each step, you can estimate how long the whole project will take.

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 works best for tasks that are simple, routine, and completed without errors by users who are already trained and experienced. This means it does not take into account what happens if a user makes a mistake or if they need to learn something new while performing the task. KLM is about speed and efficiency in actions without the complexity of cognitive challenges.

Examples & Analogies

Consider an expert typist whose fingers fly across the keyboard without hesitation. If tasked with typing a report that they’ve formatted before, they’ll complete it faster than someone experienced who is typing from scratch. KLM measures how long it would take that expert, not accounting for how long it would take a beginner who would need to think about each keystroke.

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 a straightforward method for calculating total task timeβ€”adding up the time it takes to execute each small action one after the other. This simplicity means designers can easily see how changing one action (like using a different button) affects the overall time for a task.

Examples & Analogies

Think of stacking blocks. If each block takes a certain amount of time to put in place, and you want to build a tower, KLM helps you see that if you add one more block, it simply increases your total time by the time it takes for that block.

Exhaustive Definition of 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.

Detailed Explanation

KLM identifies several basic actions, known as operators, which include pressing keys, moving the mouse, and system responses. Each of these actions is assigned a time value based on empirical research, creating a clear model for predicting task execution times.

Examples & Analogies

Imagine a recipe that lists out steps like chopping onions (which takes time), stirring a pot (also takes time), and boiling (takes time). Each step in your cooking has a duration just like each operator in KLM does. Knowing how long each step takes helps you plan when dinner will be ready.

Operators in Detail: K, P, H, D, M, R

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K (Keystroke), P (Pointing), H (Homing), D (Drawing), M (Mental Preparation), R (System Response) are the defined operators, each with typical time values assigned.

Detailed Explanation

Each operator represents a specific action that a user might take during a task. For instance, pressing a key takes time, moving the mouse takes time, and the system's response also takes a certain time. The standardized values for these times help ensure consistent predictions across different tasks.

Examples & Analogies

If you run a relay race, each part of your run (the start, the main run, the passing of the baton) has its own time. In KLM, every action the user takes has its own timing just like in the race, with the total time being the sum of all those parts.

Core Assumptions and Operational Context of KLM

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KLM is fundamentally designed for users who have achieved a high level of proficiency with the system, performing actions fluidly and without conscious thought, under the assumption that the task is performed perfectly.

Detailed Explanation

KLM operates under several assumptions: that the user is an expert who will not make mistakes and that tasks are straightforward and performed efficiently. If a user has to think hard or if the task is complicated, KLM might not give accurate time predictions.

Examples & Analogies

Consider a racecar driver: they can handle the car at high speeds with immediate reflexes due to their expertise. If a novice driver took the same car on the same track, their slower reactions and potential mistakes would lead to very different race times, which KLM does not account for.

Initial Workflow for Applying KLM

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  1. Task Definition: Clearly and unambiguously define the specific, routine unit task that will be subjected to analysis. 2. Method Decomposition: Break down the defined task into a granular, sequential list of the user's physical and mental actions. 3. Preliminary Operator Assignment: Assign the appropriate KLM operators (K, P, H, D, R) to each action in the decomposed sequence. 4. Iterative Refinement (with 'M' heuristics): The crucial step of correctly placing the 'M' (Mental Preparation) operators. 5. Summation for Prediction: Once the complete sequence of operators is defined, sum the pre-defined, average time durations for each operator.

Detailed Explanation

Applying KLM involves several steps: you first need to clearly outline the task, break it down into individual actions, assign the correct operators, refine the placement of mental preparation times, and finally add everything together to calculate the total time needed for the task. This systematic approach ensures thorough analysis and accurate predictions.

Examples & Analogies

If you were planning a road trip, you would first define your route, list the stops you'll make along the way, assign travel times to each segment of the journey, repeatedly check the best paths, and finally add it all up to get a total travel time. That’s similar to how you would approach using KLM.

Definitions & Key Concepts

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Key Concepts

  • Keystroke-Level Model (KLM): A method for predicting the time it will take an expert user to perform a task by analyzing the basic actions involved.

  • Operators: Basic actions that constitute user interactions, each with assigned time durations for analysis.

  • System Response: The time taken by a system to respond to user input, critical for understanding overall task efficiency.

  • Mental Preparation (M): The time spent by a user in planning or cognitive effort prior to executing a task, which adds variability.

Examples & Real-Life Applications

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Examples

  • An expert user typing a command into a text editor would complete this action much quicker than a novice user, demonstrating the impact of skill on task performance assumptions within KLM.

  • When evaluating the time to copy text from one part of a document to another, KLM allows designers to effectively gauge the differences in efficiency between methods like using keyboard shortcuts versus mouse clicks.

Memory Aids

Use mnemonics, acronyms, or visual cues to help remember key information more easily.

🎡 Rhymes Time

  • In KLM we trust, experts we discuss, breaking tasks down, with operators around.

πŸ“– Fascinating Stories

  • Imagine a pilot navigating a complex flight path. They rely on a set of procedures, just as KLM relies on operators to navigate user tasks efficiently.

🧠 Other Memory Gems

  • Each action contributes to our task completion time!

🎯 Super Acronyms

KLM

  • K: for Keystrokes
  • P: for Pointing
  • H: for Homing
  • M: for Mental preparation
  • D: for Drawing
  • R: for Response.

Flash Cards

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Glossary of Terms

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  • Term: KeystrokeLevel Model (KLM)

    Definition:

    A predictive model that decomposes user tasks into a sequence of observable actions, each assigned a fixed time duration, primarily applicable to expert users performing routine tasks.

  • Term: Operators

    Definition:

    Atomic actions within the KLM framework that represent interactions with systems, including keystrokes, pointing actions, and system responses.

  • Term: Expert User

    Definition:

    A user who is highly proficient in performing tasks with minimal cognitive load, executing actions fluidly and efficiently.

  • Term: Mental Preparation (M)

    Definition:

    The cognitive processing time before a user performs an action, often subjective and variable.

  • Term: System Response (R)

    Definition:

    The time taken by the system to respond to user actions, which can significantly differ based on system conditions.