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Interaction Design Models

Interaction Design Models. Dewan Tanvir Ahmed, PhD University of Ottawa dahmed@site.uottawa.ca. Chapter 7 Interaction Design Models. Model Human Processor (MHP) Keyboard Level Model (KLM) GOMS Modeling Structure Modeling Dynamics Physical Models. Predictive Models. Predictive models

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Interaction Design Models

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  1. Interaction Design Models Dewan Tanvir Ahmed, PhD University of Ottawa dahmed@site.uottawa.ca

  2. Chapter 7 Interaction Design Models • Model Human Processor (MHP) • Keyboard Level Model (KLM) • GOMS • Modeling Structure • Modeling Dynamics • Physical Models Dewan Tanvir Ahmed

  3. Predictive Models • Predictive models • are a priori (pre-experience) models - give approximations of user actions before real users are brought into the testing environment. • also known as engineering or performance model • Proved accurate! • Example: • Model Human Processor (MHP) and • Keyboard Level Model (KLM) Dewan Tanvir Ahmed

  4. Descriptive Models • Descriptive models provide a framework for thinking about user interaction • They can help us to understand how people interact with dynamic systems. • Example: • State networks and • Three-State Model Dewan Tanvir Ahmed

  5. Model Human Processor (MHP) The Model Human Processor can make general predictions about human performance • The MHP is a predictive model • Prediction of gross human behaviour • It is described by a set of memories and processors that function according to a set of principles (principles of operation) Dewan Tanvir Ahmed

  6. Model Human Processor (MHP) Dewan Tanvir Ahmed

  7. Model Human Processor (MHP) • Perceptual system (sensory image stores - SIS) • Sensors • eyes • ears • Buffers • Visual image store (VIS) • Auditory image store (AIS) • Cognitive system • Working memory (WM)—Short-term memory • Long-term memory (LTM) • Motor system • arm-hand-finger system • head-eye system Dewan Tanvir Ahmed

  8. MHP – Working Memory • WM consists of a subset of “activated” elements from LTM • The sensory image stores (SISs) encode only the nonsymbolic physical parameters of stimuli. • Example: the number ‘8’ – a curving line that creates two stacked circular shapes. • SISs are sensitive to the intensity of stimuli such as volume, brightness.. • Shortly after the inception of a stimulus, a symbolic representation is registered in the WM Dewan Tanvir Ahmed

  9. MHP – Working Memory • The sensory image stores (SISs) • Can store certain amount of information • Has a rapid decay rate • If stimuli are complex and voluminous WM will not have enough time to encode them in LTM Dewan Tanvir Ahmed

  10. MHP – Working Memory • Chunk Information - The activated elements from LTM are called chunks. • Chunks can be composed of smaller units like the letters in a word • A chunk might also consist of several words, as in a well-known phrase • Chunk depends on the user, task and contents of LTM Dewan Tanvir Ahmed

  11. MHP – Working Memory • Hard to remember – the following 9 characters • BCSBMICRA Dewan Tanvir Ahmed

  12. MHP – Working Memory • BCSBMICRA • CBS • IBM • RCA Dewan Tanvir Ahmed

  13. MHP – Working Memory Chunks in WM can interfere with each other due to LTM associations • Chunks are interrelated • Associations progresses and early associations fade due to limited WM • This is called interference which depends on context Robert Robin Wing Bird Fly Dewan Tanvir Ahmed

  14. MHP – Long-Term Memory • The cognitive processor can add items to WM but not to LTM • The WM must interact with LTM over a significant length of time before an item can be stored in LTM • This increases the number of cues that can be used to retrieve the item later • Items with numerous associations have a greater probability of being retrieved Dewan Tanvir Ahmed

  15. MHP – Processor Timing Dewan Tanvir Ahmed

  16. MHP – Processor Timing • Perceptual—The perceptual system captures physical sensations by way of the visual and auditory receptor channels • Perceptual decay is shorter for the visual channel than for the auditory channel Dewan Tanvir Ahmed

  17. MHP – Processor Timing • Perceptual storage capacity is highly variable. Card et al. use • Perceptual processor cycle time is variable according to the nature of the stimuli Dewan Tanvir Ahmed

  18. MHP – Processor Timing • Cognitive—The cognitive system bridges the perceptual and motor systems • It can function as a simple conduit or it can involve complex processes, such as learning, fact retrieval, and problem solving • Cognitive coding in the WM is predominantly visual and auditory Dewan Tanvir Ahmed

  19. MHP – Processor Timing • Cognitive coding in LTM is involved with associations and is considered to be predominantly semantic • Cognitive decay time of WM requires a large range • Cognitive decay is highly sensitive to the number of chunks involved in the recalled item Dewan Tanvir Ahmed

  20. MHP – Processor Timing • Cognitive decay of LTM is considered infinite • Cognitive processor cycle time is variable according to the nature of the stimuli • Motor—The motor system converts thought into action • Motor processor cycle time is calculated in units of discrete micromovements Dewan Tanvir Ahmed

  21. Keyboard Level Model (KLM) • The KLM is a practical design tool that can capture and calculate the physical actions a user will have to carry out to complete specific tasks on a specific interface • This model captures • Human cognition and response time The KLM can be used to determine the most efficient method and its suitability for specific contexts. Dewan Tanvir Ahmed

  22. Keyboard Level Model (KLM) • Main idea: • Walk through the interface, counting how many operations it would take an expert user to perform • Look for ways to optimize • Look for potential sources of error • KLM is very low-level (tiny operations) Dewan Tanvir Ahmed

  23. Keyboard Level Model (KLM) • How to make a KLM • List specific actions a user does to perform task • Keystrokes and button presses • Mouse movements • Hand movements between keyboard & mouse • System response time (if it makes user wait) • Add Mental operators • Assign execution times to steps • Add up execution times • Only provides execution time and operator sequence Dewan Tanvir Ahmed

  24. Keyboard Level Model (KLM) • Given: • A task (possibly involving several subtasks) • The command language of a system • The motor skill parameter of the user • The response time parameters • Predict: The time an expert user will take to execute the task using the system • Provided that he or she uses the method without error • It predicts the time of an error-free execution of a task. • The method of task completion is predetermined. Dewan Tanvir Ahmed

  25. Keyboard Level Model (KLM) • The KLM is comprised of: • Operators • Encoding methods • Heuristics for the placement of mental (M) operators Dewan Tanvir Ahmed

  26. KLM - Operators Dewan Tanvir Ahmed

  27. KLM – Encoding Methods • Encoding methods define how the operators involved in a task are to be written (DOS command “ipconfig”) MK[i] K[p] K[c] K[o] K[n] K[f] K[i] K[g] K[RETURN] It would be encoded in the short-hand version as M 8K [ipconfig RETURN] This results in a timing of 1.35 + 8 * 0.20 = 2.95 seconds for an average skilled typist. Dewan Tanvir Ahmed

  28. KLM – Encoding Methods • The same function in Microsoft XP by using the network Connection Icon and selecting the Repair item in the pop-up menu. H[mouse] MP[Network Icon] K[Right Click] P[Prepare]K[Left Click] This results in a timing of 0.4 + 1.35 + 2P + 2K = 4.35 seconds for an average skilled typist. Dewan Tanvir Ahmed

  29. KLM – Heuristics for M Operator Placement • The KLM operators can be placed into one of two groups— • physical or • cognitive. • Physical operators • are defined by the chosen method of operation • such as clicking an icon or entering a command string. • The cognitive operators • are governed by the set of heuristics Dewan Tanvir Ahmed

  30. What the KLM Does Not Do • The KLM models the time it takes to complete a routine task • The KLM was not designed to consider the following: • Errors • Learning • Functionality • Recall • Concentration • Fatigue • Acceptability Dewan Tanvir Ahmed

  31. Applications for the KLM • Case 1 (Mouse-Driven Text Editor) • During the development of the Xerox Star KLMs served as expert proxies • Case 2 (Directory Assistance Workstation) • The KLM clarified the tradeoffs between the number of keystrokes entered in the query and the number of returned fields Dewan Tanvir Ahmed

  32. GOMS • MHP and KLM can determine how long it will take to complete a task and the most efficient way to accomplish that task. • Say nothing about why a person would choose one method over another. Goal/task models can be used to explore the methods people use to accomplish their goals Dewan Tanvir Ahmed

  33. GOMS Characterization of a task in terms of • The user's Goals • The Operators available to accomplish those goals • Methods ( ) to accomplish those goals • The Selection rules used to choose between methods frequently used sequences of operators and sub-goals Dewan Tanvir Ahmed

  34. GOMS • The GOMS model has four components: • goals • operators • methods • selection rules • Goals, Operators, Methods, and Selection rules • Higher-level than KLM • Input: detailed description of UI and task(s) • Output: various qualitative and quantitative measures Dewan Tanvir Ahmed

  35. GOMS - Goals • Goals – • Tasks are deconstructed as a set of goals and sub-goals. Select sentence Moved sentence Cut sentence Move to new spot Paste sentence Place it Dewan Tanvir Ahmed

  36. GOMS - Operators • Basic actions available for performing a task (lowest level actions) • Operators – • Tasks can only be carried out by undertaking specific actions. • Printing a document • Issue print command • Clicking the printer icon – an operator Dewan Tanvir Ahmed

  37. GOMS - Methods • Methods - Represent different ways of achieving a goal • Sequence of operators (procedures) for accomplishing a goal (may be multiple) • Comprised of operators that facilitate method completion • Printing: Alt + P • Select sentence • Move mouse pointer to first word • Depress button • Drag to last word • Release Dewan Tanvir Ahmed

  38. GOMS – Selection Rules • Selection Rules – • The method that the user chooses is determined by selection rules • Invoked when there is a choice of a method • GOMS attempts to predict which methods will be used • Example: • Could cut sentence either by menu pull down or by ctrl-x Dewan Tanvir Ahmed

  39. GOMS – CMN-GOMS CMN-GOMS can predict behavior and assess memory requirements • CMN-GOMS (named after Card, Moran, and Newell) -a detailed expansion of the general GOMS model • Includes specific analysis procedures and notation descriptions • Can judge memory requirements • The depth of the nested goal structures • Provides insight into user performance measures Dewan Tanvir Ahmed

  40. GOMS – Other GOMS Models • NGOMSL (Natural GOMS Language), developed by Kieras • It provides a structured natural-language notation for GOMS analysis and describes the procedures for accomplishing that analysis • NGOMSL Provides: • A method for measuring the time it will take to learn specific method of operation • A way to determine the consistency of a design’s methods of operation Dewan Tanvir Ahmed

  41. GOMS – Other GOMS Models • CPM-GOMS represents • Cognitive • Perceptual • Motor operators • CPM-GOMS uses Program Evaluation Review Technique (PERT) charts to • Map task durations using the critical path method (CPM). • CPM-GOMS is based directly on the Model Human Processor • Assumes that perceptual, cognitive, and motor processors function in parallel Dewan Tanvir Ahmed

  42. GOMS – Other GOMS Models • Program Evaluation Review Technique (PERT) chart Resource Flows • Example: locate a paragraph Dewan Tanvir Ahmed

  43. Modeling Structure • Structural models can help us to see the relationship between • the conceptual components of a design and • the physical components of the system • It allows us to judge the design’s relative effectiveness. Dewan Tanvir Ahmed

  44. Modeling Structure – Hicks Law • When an application launched, it opens a default menus • Cannot show all necessary functions • So, we need optional menus Designer must decide what menus are needed as well as what options are included in each menu One level is more efficient than having more levels with fewer choices Dewan Tanvir Ahmed

  45. Modeling Structure – Hicks Law Hick’s law can be used to create menu structures • Hick’s law states that the time it takes to choose one item from n alternatives is proportional to the logarithm (base 2) of the number of choices, plus 1. • This equation is predicated on all items having an equal probability of being chosen Dewan Tanvir Ahmed

  46. Modeling Structure – Hicks Law • The coefficients are empirically determined from experimental design • Raskin(2000) suggests that a = 50 and b =150 are sufficient place holders for “back-of-the-envelope” approximations Dewan Tanvir Ahmed

  47. Modeling Structure – Other Forms of Hicks Law For n equi-probable alternatives: For n alternatives each with a different probability pj: generalizes to H = log2 (n + 1)  a + b log2(n+ 1) H = pjlog2 (1/pj+ 1) c + d * pj log2 (1/pj+ 1) Dewan Tanvir Ahmed

  48. Modeling Structure – Hicks Law Menu listing order must be logical and relevant • Menus are lists grouped according to some predetermined system • If the rules are not understood or if they are not relevant to a particular task, their arrangement may seem arbitrary and random, requiring users to search in a linear, sequential manner. Dewan Tanvir Ahmed

  49. Modeling Structure – Hicks Law • Breadth vs. Depth Dewan Tanvir Ahmed

  50. Modeling Structure – Hicks Law • Breadth vs. Depth Dewan Tanvir Ahmed

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