The document discusses modeling human performance through techniques like the Model Human Processor, GOMS, and the Keystroke Level Model to quantitatively predict how users will interact with systems. These techniques model human cognition and motor processes to estimate times for tasks and inform interface design. Parameters like memory capacity and decay time, along with principles such as Fitts' law, allow modeling different users and generating testable predictions about performance.
This document discusses the objectives and topics of the CS-311 Design and Analysis of Algorithms course. The objectives are to design algorithms using techniques like divide and conquer, develop problem solving skills, and analyze algorithms to compare efficiencies. An algorithm is defined as a sequence of unambiguous instructions to solve a problem. Sorting algorithms like selection sort and merge sort are presented as examples and analyzed based on time complexity. The process of solving a problem with algorithms includes understanding the problem, designing a solution, implementing and testing code, and analyzing performance. Key constructs like sequences, selections, iterations, and recursion are discussed for analyzing time complexity of algorithms.
This document discusses the role of psychology in human-computer interaction (HCI). It explains that psychology provides a scientific approach to understanding human abilities and limitations that can inform system design. It introduces the Model Human Processor as a model of human cognition and outlines how aspects of perception, cognition and motor control follow principles like Fitts' Law. It argues that understanding principles from psychology can help design systems that are optimized for human capabilities and limitations.
Design & Analysis of Algorithm course .pptxJeevaMCSEKIOT
This document discusses algorithms and their analysis. It defines an algorithm as a well-defined computational procedure that takes inputs and produces outputs. Key characteristics of algorithms include definiteness, finiteness, effectiveness, correctness, simplicity, and unambiguousness. The document discusses two common algorithm design techniques: divide and conquer, which divides a problem into subproblems, and greedy techniques, which make locally optimal choices to find a near-optimal solution. It also covers analyzing algorithms, including asymptotic time and space complexity analysis to determine how resource usage grows with problem size.
2-Algorithms and Complexit data structurey.pdfishan743441
The document discusses algorithms design and complexity analysis. It defines an algorithm as a well-defined sequence of steps to solve a problem and notes that algorithms always take inputs and produce outputs. It discusses different approaches to designing algorithms like greedy, divide and conquer, and dynamic programming. It also covers analyzing algorithm complexity using asymptotic analysis by counting the number of basic operations and deriving the time complexity function in terms of input size.
This document provides an overview of a lecture on designing and analyzing computer algorithms. It discusses key concepts like what an algorithm and program are, common algorithm design techniques like divide-and-conquer and greedy methods, and how to analyze algorithms' time and space complexity. The goals of analyzing algorithms are to understand their behavior, improve efficiency, and determine whether problems can be solved within a reasonable time frame.
This document discusses several cognitive modeling theories relevant to human-computer interaction (HCI), including direct manipulation theory, Norman's theory of action involving gulfs of execution and evaluation, and the Keystroke-Level Model (KLM) and GOMS modeling approaches. It provides examples of how direct manipulation relates to engagement and distance between thoughts and system requirements. It also explains Norman's gulfs of execution and evaluation and provides examples. The document defines the basic operations in KLM like key presses and mouse movements. It describes how GOMS breaks tasks down into goals, operators, methods and selection rules. Finally, it provides an example GOMS model for deleting a file.
This document discusses various cognitive models used in human-computer interaction design. It describes goal and task hierarchies which model user goals as a hierarchy of subgoals. It also covers linguistic models like Backus-Naur Form (BNF) and Task-Action Grammar (TAG) which model dialogs between users and systems. Other models discussed include GOMS, Cognitive Complexity Theory (CCT), the Keystroke Level Model (KLM), and Buxton's three-state model for modeling physical interactions. The models aim to represent aspects of user understanding, knowledge, intentions, and processing to inform the design of interactive systems.
This document discusses the objectives and topics of the CS-311 Design and Analysis of Algorithms course. The objectives are to design algorithms using techniques like divide and conquer, develop problem solving skills, and analyze algorithms to compare efficiencies. An algorithm is defined as a sequence of unambiguous instructions to solve a problem. Sorting algorithms like selection sort and merge sort are presented as examples and analyzed based on time complexity. The process of solving a problem with algorithms includes understanding the problem, designing a solution, implementing and testing code, and analyzing performance. Key constructs like sequences, selections, iterations, and recursion are discussed for analyzing time complexity of algorithms.
This document discusses the role of psychology in human-computer interaction (HCI). It explains that psychology provides a scientific approach to understanding human abilities and limitations that can inform system design. It introduces the Model Human Processor as a model of human cognition and outlines how aspects of perception, cognition and motor control follow principles like Fitts' Law. It argues that understanding principles from psychology can help design systems that are optimized for human capabilities and limitations.
Design & Analysis of Algorithm course .pptxJeevaMCSEKIOT
This document discusses algorithms and their analysis. It defines an algorithm as a well-defined computational procedure that takes inputs and produces outputs. Key characteristics of algorithms include definiteness, finiteness, effectiveness, correctness, simplicity, and unambiguousness. The document discusses two common algorithm design techniques: divide and conquer, which divides a problem into subproblems, and greedy techniques, which make locally optimal choices to find a near-optimal solution. It also covers analyzing algorithms, including asymptotic time and space complexity analysis to determine how resource usage grows with problem size.
2-Algorithms and Complexit data structurey.pdfishan743441
The document discusses algorithms design and complexity analysis. It defines an algorithm as a well-defined sequence of steps to solve a problem and notes that algorithms always take inputs and produce outputs. It discusses different approaches to designing algorithms like greedy, divide and conquer, and dynamic programming. It also covers analyzing algorithm complexity using asymptotic analysis by counting the number of basic operations and deriving the time complexity function in terms of input size.
This document provides an overview of a lecture on designing and analyzing computer algorithms. It discusses key concepts like what an algorithm and program are, common algorithm design techniques like divide-and-conquer and greedy methods, and how to analyze algorithms' time and space complexity. The goals of analyzing algorithms are to understand their behavior, improve efficiency, and determine whether problems can be solved within a reasonable time frame.
This document discusses several cognitive modeling theories relevant to human-computer interaction (HCI), including direct manipulation theory, Norman's theory of action involving gulfs of execution and evaluation, and the Keystroke-Level Model (KLM) and GOMS modeling approaches. It provides examples of how direct manipulation relates to engagement and distance between thoughts and system requirements. It also explains Norman's gulfs of execution and evaluation and provides examples. The document defines the basic operations in KLM like key presses and mouse movements. It describes how GOMS breaks tasks down into goals, operators, methods and selection rules. Finally, it provides an example GOMS model for deleting a file.
This document discusses various cognitive models used in human-computer interaction design. It describes goal and task hierarchies which model user goals as a hierarchy of subgoals. It also covers linguistic models like Backus-Naur Form (BNF) and Task-Action Grammar (TAG) which model dialogs between users and systems. Other models discussed include GOMS, Cognitive Complexity Theory (CCT), the Keystroke Level Model (KLM), and Buxton's three-state model for modeling physical interactions. The models aim to represent aspects of user understanding, knowledge, intentions, and processing to inform the design of interactive systems.
On the Semantics of Real-Time Domain Specific Modeling LanguagesJose E. Rivera
This document summarizes Jose E. Rivera's PhD thesis on providing formal semantics for real-time domain specific modeling languages. The thesis defines a framework for specifying the timed behavior of domain specific modeling languages using rewriting logic. It extends in-place model transformations to include time-dependent behavior. Models are formally specified in Maude, enabling simulation and analysis of real-time properties. The work contributes tools and techniques for formally specifying, simulating, and analyzing real-time domain specific modeling languages.
The document discusses various C programming concepts like algorithms, flowcharts, tokens, data types, operators, functions, and hardware components of a computer. It includes questions and answers on these topics. Key points covered are definition of algorithm and flowchart, different types of tokens in C, differences between while and do-while loops, definition of software and its types, and examples of standard header files.
This document outlines an assignment to analyze and design a program for sequential control flow. The objectives are to understand computational problem solving, sequential logic, and how to create IPO charts, algorithms, and flow charts. As an example, students are instructed to create these analysis tools to solve a temperature conversion problem that takes Celsius input and outputs Fahrenheit and Kelvin scales using defined formulas. The document provides background on problem solving techniques, sequential control flow, and common analysis tools like IPO charts, algorithms using pseudocode, and flow charts using standard symbols.
human computer Interaction cognitive models.pptJayaprasanna4
This document discusses cognitive models used to represent users of interactive systems. It describes hierarchical models that represent a user's task and goal structure, as well as linguistic models that represent the user-system grammar. GOMS and CCT are two common cognitive models discussed in detail, with both modeling a user's goals, operators, and methods in a hierarchical structure. The document also covers Keystroke-Level Modeling (KLM) which uses cognitive understandings to predict user performance times for simple tasks.
human computer Interaction cognitive models.pptJayaprasanna4
This document discusses cognitive models used to represent users of interactive systems. It describes hierarchical models that represent a user's task and goal structure, as well as linguistic models that represent the user-system grammar. GOMS and CCT are two common cognitive models discussed in detail, with both modeling a user's goals, operators, and methods in a hierarchical structure. The document also covers Keystroke-Level Modeling (KLM) which uses cognitive understandings to predict user performance times for simple tasks.
C LANGUAGE-FLOWCHARTS,PSEUDOCODE,ALGORITHMS APPROCHESHarshJha34
The document discusses various topics related to unit 1 of a programming course, including flowcharts, algorithm design using top-down and bottom-up approaches, and pseudocode. It covers elements of flowcharts like input/output, branching, and iteration. It describes problem solving in two phases - problem solving and implementation. It also explains algorithm development, properties of algorithms, and constructs like sequence, decision, and iteration. Pseudocode representation and writing pseudocode from algorithms and flowcharts is also mentioned.
The document provides information about algorithms including:
- Algorithms are step-by-step procedures to solve problems and are represented in programs, pseudocode, or flowcharts.
- Common algorithm applications include sorting, data retrieval, routing, and games.
- Pseudocode is an informal way to describe algorithms using a combination of English and programming languages.
- Algorithm analysis evaluates correctness, efficiency, complexity, and other factors.
This document provides information about computers and algorithms:
1. It explains that computers are powerful machines that can perform many tasks but have no intelligence - they simply follow the step-by-step instructions provided by users or programmers.
2. It discusses the problem solving process, noting that problems must be clearly defined, analyzed, and solutions developed before coding instructions for the computer.
3. It provides definitions and examples of algorithms - step-by-step processes for solving problems that must be unambiguous, finite, and effectively coded for a computer.
This document discusses computer architecture and organization. It defines computer architecture as the attributes of a computer system that are visible to programmers, such as instruction set and data type sizes, while computer organization refers to the structural relationships not visible to programmers, such as clock frequency. Performance metrics like response time and throughput are also introduced. Architectural choices aim to balance factors like cost, performance, and new technology.
Problem solving using computers - Unit 1 - Study materialTo Sum It Up
Problem solving using computers involves transforming a problem description into a solution using problem-solving strategies, techniques, and tools. Programming is a problem-solving activity where instructions are written for a computer to solve something. The document then discusses the steps in problem solving like definition, analysis, approach, coding, testing etc. It provides examples of algorithms, flowcharts, pseudocode and discusses concepts like top-down design, time complexity, space complexity and ways to swap variables and count values.
The document introduces Microsoft Word 2013 as a sophisticated word processing program that makes it easy to create a wide range of business and personal documents. Word allows users to create professional-looking documents with graphics and styles, store reusable elements, make information accessible through features like tables of contents, and collaborate by controlling document access. As Word is part of the Microsoft Office suite, skills learned in it can be applied to other Office programs that share a common interface.
The document discusses data structures and algorithms. It defines data structures as organized ways of storing data to allow efficient processing. Algorithms manipulate data in data structures to perform operations like searching and sorting. Big-O notation is introduced to analyze algorithms' time complexity as the problem size increases. Common time complexities like O(1), O(log n), O(n), O(n log n), O(n^2), O(n^3) and O(2^n) are defined. An example algorithm to find the minimum element in an array is analyzed, showing it has O(n) time complexity. Selection sort is analyzed and shown to have O(n^2) time complexity.
The document discusses data structures and algorithms. It defines data structures as organized ways of storing data to allow efficient processing. Algorithms manipulate data in data structures to perform operations like searching and sorting. Big-O notation provides an asymptotic analysis of algorithms, estimating how their running time grows with input size. Common time complexities include constant O(1), linear O(n), quadratic O(n^2), and exponential O(2^n).
The document discusses data structures and algorithms. It defines data structures as organized ways of storing data to allow efficient processing. Algorithms manipulate data in data structures to perform operations like searching and sorting. Big-O notation provides an asymptotic analysis of algorithms, estimating how their running time grows with input size. Common time complexities include constant O(1), linear O(n), quadratic O(n^2), and exponential O(2^n).
Download Complete Material - https://www.instamojo.com/prashanth_ns/
This Data Structures and Algorithms contain 15 Units and each Unit contains 60 to 80 slides in it.
Contents…
• Introduction
• Algorithm Analysis
• Asymptotic Notation
• Foundational Data Structures
• Data Types and Abstraction
• Stacks, Queues and Deques
• Ordered Lists and Sorted Lists
• Hashing, Hash Tables and Scatter Tables
• Trees and Search Trees
• Heaps and Priority Queues
• Sets, Multi-sets and Partitions
• Dynamic Storage Allocation: The Other Kind of Heap
• Algorithmic Patterns and Problem Solvers
• Sorting Algorithms and Sorters
• Graphs and Graph Algorithms
• Class Hierarchy Diagrams
• Character Codes
Java Foundations: Data Types and Type ConversionSvetlin Nakov
Learn how to use data types and variables in Java, how variables are stored in the memory and how to convert from one data type to another.
Watch the video lesson and access the hands-on exercises here: https://softuni.org/code-lessons/java-foundations-certification-data-types-and-variables
Algorithm for computational problematic sitSaurabh846965
A computer requires precise instructions from a user in order to perform tasks correctly. It has no inherent intelligence or ability to solve problems on its own. For a computer to solve a problem, a programmer must break the problem down into a series of simple steps and write program code that provides those step-by-step instructions in a language the computer can understand. This process involves understanding the problem, analyzing it, developing a solution algorithm, and coding the algorithm so the computer can execute it. Flowcharts can help visualize algorithms and problem-solving logic in a graphical format before writing program code.
This document provides an introduction to algorithms and algorithm problem solving. It discusses understanding the problem, designing an algorithm, proving correctness, analyzing the algorithm, and coding the algorithm. It also provides examples of algorithm problems involving air travel, a xerox shop, document similarity, and drawing geometric figures. Key aspects of algorithms like being unambiguous, having well-defined inputs and outputs, and being finite are explained. Techniques for exact and approximate algorithms are also covered.
Here are the steps to solve the problems using IPO table, pseudo code and flowchart:
1. Define the problem and understand requirements
2. Make IPO table:
- Input, Process, Output
3. Write pseudo code using proper indentation and comments
4. Draw flowchart using standard symbols
5. Test and debug the program
This systematic approach helps analyze the problem, design the algorithm and implement it properly. The key is breaking down the problem into smaller understandable steps.
On the Semantics of Real-Time Domain Specific Modeling LanguagesJose E. Rivera
This document summarizes Jose E. Rivera's PhD thesis on providing formal semantics for real-time domain specific modeling languages. The thesis defines a framework for specifying the timed behavior of domain specific modeling languages using rewriting logic. It extends in-place model transformations to include time-dependent behavior. Models are formally specified in Maude, enabling simulation and analysis of real-time properties. The work contributes tools and techniques for formally specifying, simulating, and analyzing real-time domain specific modeling languages.
The document discusses various C programming concepts like algorithms, flowcharts, tokens, data types, operators, functions, and hardware components of a computer. It includes questions and answers on these topics. Key points covered are definition of algorithm and flowchart, different types of tokens in C, differences between while and do-while loops, definition of software and its types, and examples of standard header files.
This document outlines an assignment to analyze and design a program for sequential control flow. The objectives are to understand computational problem solving, sequential logic, and how to create IPO charts, algorithms, and flow charts. As an example, students are instructed to create these analysis tools to solve a temperature conversion problem that takes Celsius input and outputs Fahrenheit and Kelvin scales using defined formulas. The document provides background on problem solving techniques, sequential control flow, and common analysis tools like IPO charts, algorithms using pseudocode, and flow charts using standard symbols.
human computer Interaction cognitive models.pptJayaprasanna4
This document discusses cognitive models used to represent users of interactive systems. It describes hierarchical models that represent a user's task and goal structure, as well as linguistic models that represent the user-system grammar. GOMS and CCT are two common cognitive models discussed in detail, with both modeling a user's goals, operators, and methods in a hierarchical structure. The document also covers Keystroke-Level Modeling (KLM) which uses cognitive understandings to predict user performance times for simple tasks.
human computer Interaction cognitive models.pptJayaprasanna4
This document discusses cognitive models used to represent users of interactive systems. It describes hierarchical models that represent a user's task and goal structure, as well as linguistic models that represent the user-system grammar. GOMS and CCT are two common cognitive models discussed in detail, with both modeling a user's goals, operators, and methods in a hierarchical structure. The document also covers Keystroke-Level Modeling (KLM) which uses cognitive understandings to predict user performance times for simple tasks.
C LANGUAGE-FLOWCHARTS,PSEUDOCODE,ALGORITHMS APPROCHESHarshJha34
The document discusses various topics related to unit 1 of a programming course, including flowcharts, algorithm design using top-down and bottom-up approaches, and pseudocode. It covers elements of flowcharts like input/output, branching, and iteration. It describes problem solving in two phases - problem solving and implementation. It also explains algorithm development, properties of algorithms, and constructs like sequence, decision, and iteration. Pseudocode representation and writing pseudocode from algorithms and flowcharts is also mentioned.
The document provides information about algorithms including:
- Algorithms are step-by-step procedures to solve problems and are represented in programs, pseudocode, or flowcharts.
- Common algorithm applications include sorting, data retrieval, routing, and games.
- Pseudocode is an informal way to describe algorithms using a combination of English and programming languages.
- Algorithm analysis evaluates correctness, efficiency, complexity, and other factors.
This document provides information about computers and algorithms:
1. It explains that computers are powerful machines that can perform many tasks but have no intelligence - they simply follow the step-by-step instructions provided by users or programmers.
2. It discusses the problem solving process, noting that problems must be clearly defined, analyzed, and solutions developed before coding instructions for the computer.
3. It provides definitions and examples of algorithms - step-by-step processes for solving problems that must be unambiguous, finite, and effectively coded for a computer.
This document discusses computer architecture and organization. It defines computer architecture as the attributes of a computer system that are visible to programmers, such as instruction set and data type sizes, while computer organization refers to the structural relationships not visible to programmers, such as clock frequency. Performance metrics like response time and throughput are also introduced. Architectural choices aim to balance factors like cost, performance, and new technology.
Problem solving using computers - Unit 1 - Study materialTo Sum It Up
Problem solving using computers involves transforming a problem description into a solution using problem-solving strategies, techniques, and tools. Programming is a problem-solving activity where instructions are written for a computer to solve something. The document then discusses the steps in problem solving like definition, analysis, approach, coding, testing etc. It provides examples of algorithms, flowcharts, pseudocode and discusses concepts like top-down design, time complexity, space complexity and ways to swap variables and count values.
The document introduces Microsoft Word 2013 as a sophisticated word processing program that makes it easy to create a wide range of business and personal documents. Word allows users to create professional-looking documents with graphics and styles, store reusable elements, make information accessible through features like tables of contents, and collaborate by controlling document access. As Word is part of the Microsoft Office suite, skills learned in it can be applied to other Office programs that share a common interface.
The document discusses data structures and algorithms. It defines data structures as organized ways of storing data to allow efficient processing. Algorithms manipulate data in data structures to perform operations like searching and sorting. Big-O notation is introduced to analyze algorithms' time complexity as the problem size increases. Common time complexities like O(1), O(log n), O(n), O(n log n), O(n^2), O(n^3) and O(2^n) are defined. An example algorithm to find the minimum element in an array is analyzed, showing it has O(n) time complexity. Selection sort is analyzed and shown to have O(n^2) time complexity.
The document discusses data structures and algorithms. It defines data structures as organized ways of storing data to allow efficient processing. Algorithms manipulate data in data structures to perform operations like searching and sorting. Big-O notation provides an asymptotic analysis of algorithms, estimating how their running time grows with input size. Common time complexities include constant O(1), linear O(n), quadratic O(n^2), and exponential O(2^n).
The document discusses data structures and algorithms. It defines data structures as organized ways of storing data to allow efficient processing. Algorithms manipulate data in data structures to perform operations like searching and sorting. Big-O notation provides an asymptotic analysis of algorithms, estimating how their running time grows with input size. Common time complexities include constant O(1), linear O(n), quadratic O(n^2), and exponential O(2^n).
Download Complete Material - https://www.instamojo.com/prashanth_ns/
This Data Structures and Algorithms contain 15 Units and each Unit contains 60 to 80 slides in it.
Contents…
• Introduction
• Algorithm Analysis
• Asymptotic Notation
• Foundational Data Structures
• Data Types and Abstraction
• Stacks, Queues and Deques
• Ordered Lists and Sorted Lists
• Hashing, Hash Tables and Scatter Tables
• Trees and Search Trees
• Heaps and Priority Queues
• Sets, Multi-sets and Partitions
• Dynamic Storage Allocation: The Other Kind of Heap
• Algorithmic Patterns and Problem Solvers
• Sorting Algorithms and Sorters
• Graphs and Graph Algorithms
• Class Hierarchy Diagrams
• Character Codes
Java Foundations: Data Types and Type ConversionSvetlin Nakov
Learn how to use data types and variables in Java, how variables are stored in the memory and how to convert from one data type to another.
Watch the video lesson and access the hands-on exercises here: https://softuni.org/code-lessons/java-foundations-certification-data-types-and-variables
Algorithm for computational problematic sitSaurabh846965
A computer requires precise instructions from a user in order to perform tasks correctly. It has no inherent intelligence or ability to solve problems on its own. For a computer to solve a problem, a programmer must break the problem down into a series of simple steps and write program code that provides those step-by-step instructions in a language the computer can understand. This process involves understanding the problem, analyzing it, developing a solution algorithm, and coding the algorithm so the computer can execute it. Flowcharts can help visualize algorithms and problem-solving logic in a graphical format before writing program code.
This document provides an introduction to algorithms and algorithm problem solving. It discusses understanding the problem, designing an algorithm, proving correctness, analyzing the algorithm, and coding the algorithm. It also provides examples of algorithm problems involving air travel, a xerox shop, document similarity, and drawing geometric figures. Key aspects of algorithms like being unambiguous, having well-defined inputs and outputs, and being finite are explained. Techniques for exact and approximate algorithms are also covered.
Here are the steps to solve the problems using IPO table, pseudo code and flowchart:
1. Define the problem and understand requirements
2. Make IPO table:
- Input, Process, Output
3. Write pseudo code using proper indentation and comments
4. Draw flowchart using standard symbols
5. Test and debug the program
This systematic approach helps analyze the problem, design the algorithm and implement it properly. The key is breaking down the problem into smaller understandable steps.
A Free 200-Page eBook ~ Brain and Mind Exercise.pptxOH TEIK BIN
(A Free eBook comprising 3 Sets of Presentation of a selection of Puzzles, Brain Teasers and Thinking Problems to exercise both the mind and the Right and Left Brain. To help keep the mind and brain fit and healthy. Good for both the young and old alike.
Answers are given for all the puzzles and problems.)
With Metta,
Bro. Oh Teik Bin 🙏🤓🤔🥰
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
Information and Communication Technology in EducationMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 2)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐈𝐂𝐓 𝐢𝐧 𝐞𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧:
Students will be able to explain the role and impact of Information and Communication Technology (ICT) in education. They will understand how ICT tools, such as computers, the internet, and educational software, enhance learning and teaching processes. By exploring various ICT applications, students will recognize how these technologies facilitate access to information, improve communication, support collaboration, and enable personalized learning experiences.
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐢𝐧𝐭𝐞𝐫𝐧𝐞𝐭:
-Students will be able to discuss what constitutes reliable sources on the internet. They will learn to identify key characteristics of trustworthy information, such as credibility, accuracy, and authority. By examining different types of online sources, students will develop skills to evaluate the reliability of websites and content, ensuring they can distinguish between reputable information and misinformation.
2. User Performance Modeling
Q uantitatively predict what people will do
before you build a system.
Also called:
Human Behavior Modeling (military)
Cognitive Modeling
Computational Cognitive Modeling
Cognitive Performance Modeling
8. Model Human Processor (MHP)
Goals
Provide approximate predictions of gross human
behavior
Help remember theory & facts about HCI
Provide“good enough” answers to guide design
Provide explanations for & support empirical results
9. Questions the MHP can answer
How long will it take to do a task on the
computer?
Prediction
Is designA likely to be“better” (faster) than design B?
Explanation
Why is designA “better” (faster) than design B?
10. Questions MHP can’t answer
User preferences,likeability
,desirability
Complex problem-solving & learning
Questions dealing with the subtleties of human
behavior
,creative thoughts,humor
,etc.
14. Model Human Processor Subsystems
Three interacting systems
Perceptual system — hear knock
Cognitive system — process knock
Motor system — respond with knock
Each subsystem has:
A processor
Memories it interacts with
15.
16. Parameters
Memory parameters
∂ = Decay time of an item
µ = Storage capacity in items
κ = Code type (physical,acoustic,visual,semantic)
Processor parameter
τ = Cycle time
20. 10 Principles of Operation
P0.Recognize-act cycle of cognitive processor
P1.Variable perceptual processor rate principle
P2.Encoding specificity principle
P3.Discrimination principle
P4.Variable cognitive processor rate principle
P5.Fitt’
s law
P6.Power law of practice
P7. Uncertainty principle
P8.Rationality principle
P9.Problem space principle
26. Fitts’Law predicts pointing
movements
Modeling pointing
movements
Predicts that time
required to rapidly
move to a target is
a function of the
distance to and size
of the target.
home
target
W
D
27. Fitts’Law Parameters
T – the average time taken to complete the movement.
a – the start/
stop time ofthe device (constant)
b – the inherent speed of the device (constant)
D – the distance from the starting point to the center of the target
W – the width of the target measured along the axis of motion
T = a + b * ID ID = log2*(2*D/W)
28. Fitts’Law
T(s)
log2(2D/W )
T =a + b*log2(2*D/W)
T – the average time taken to
complete the movement.
a – the start/
stop time ofthe
device (constant)
b – the inherent speed ofthe
device (constant)
D – the distance from the
starting point to the center of
the target
W – the width of the target
measured along the axis of
motion
30. Summary
Goals of the Model Human Processor
Helps us make approximate predictions of gross
human behavior
Helps us remember facts & predict human-
computer interaction behavior
Informs design decisions when data not available
Helps explain data when it is
31. Model Human Processor consists of:
3 interconnected memories & processors
10 principles of operation
Summarized by 4 parameter values:
µ = Storage capacity in items
∂ = Decay time of an item
κ = Code type (physical,acoustic ,visual,semantic)
τ = Cycle time
35. GOMS
Goals,Operators,Methods, and Selection
Goals – what the user wants to accomplish
Operators – The means that leads to agoal ata
detailed level
M ethods – Sequences of operators
Selection rules – Rules (general or personal) for
choosing a certain method
36. W hat is a GOMS model?
A description of the knowledge that a user must
have in order to carry out tasks on a device or a
system
Composed of methods that are used to achieve specific
goals
M ethods are composed of operators at the lowest
level
Operators are specific steps (gestures) 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.
37. GOMS Characteristics
Combines cognitive aspects with analysis of task
Quantitatively
Results in predictions oftime
Qualitatively
GO MS can explain the predictions
Focus on methods to accomplish goals
38. W hen is GOMS analysis used?
Situations where users will be expected to
perform tasks they have already mastered
Expert users
Routine work
W hen time is crucial
39. GOMS Example
Goal – edit an article
O perators – arrow keys,mouse,other keys
Method
Delete text (sub-goal)
Positioning: 1) arrow key 2) mouse
Marking:1) double click 2) use mouse
Delete (and add text):1) start writing 2) press
delete then write new text
Selection rules – if close,use arrow key
,etc.
40. CMN-GOMS
CMN = Card,Moran,& Newell who introduced
GOMS to HCI
Operators are strictly sequential
Breadth-first until relevant level of detail,could
be at a keystroke level
42. CPM-GOMS
CPM = Cognitive Perceptual Motor or
Critical Path Method
Based on Model Human Processor
,involving
parallel processing
Uses operators as in CMN-GOMS
44. Keystroke Level Model (KLM)
Simples of GOMS techniques,serial model
Uses duration estimates for keystroke-level
operators
Quantitatively – predicts time for skilled users
Qualitatively – highlights new ideas
45. Keystroke Operators
K = key press
P = pointing
H = home hands
D = drawing a line
M = mental thinking
Rt = system response time
Ttotal = K+P+H+D+M+R
46. Operator Timings
Keying = 0.2 sec
The time it takes to tap a key on the keyboard
Pointing = 1.1 sec
The time it takes a user to point to a position on a display
H oming = 0.4 sec
The time it takes a user’s hand to move from the keyboard to the
GID or from the GID to the keyboard
M entally Preparing = 1.35 sec
The time it takes a user to prepare mentally for the next step
Responding = based on task and system
The time a user must wait for a computer to respond to input
47. Building a KLM
List the overt actions necessary to do the task
Keystrokes and buttons (K)
Mouse movements (P)
Hand movements from keyboard to mouse (H)
System response time (R)
Mental operators (M) by CMN heuristics
Add up execution times
48. Let’
s try it out!
Convert F to C
T
emperature Converter
Choose which conversion is desired,
then type the temperature,and press Enter
Convert C to F
49. Using KLM
Assume an average of four typed characters in
an entered temperature, including any decimal
point and sign
Assume (for simplicity) that typing is perfect;
error detection and notification aren’t needed
50. K = key press
P = pointing
H = home hands
D = drawing a line
M = mental thinking
Rt = system response time
Ttotal = K+P+H+D+M+R
Temperature Converter
Convert F to C
Choose which conversion is desired,
then type the temperature,and press Enter
Convert C to F
51. Inserting Mental Operators:Rule 0
Initial insertion of candidate Ms
Insert Ms in front of all keystrokes (K).
Insert Ms in front of all acts of pointing (P).
Do not insert Ms before any Ps that point to an
argument.
In GUIs, mouse-operated widgets (buttons,check
boxes,etc) are considered commands.
T
ext entry considered as an argument.
52. Arguments?
Pointing to a cell on a spreadsheet is pointing to
an argument – no M
Pointing to a word in a manuscript is pointing to
an argument – no M
Pointing to an icon on atoolbar is pointing to a
command – M
Pointing to the label of a dropdown menu is
pointing to a command -- M
53. Inserting Mental Operators:Rule 1
Deletion of anticipated Ms
If an operator following an M is fully anticipated
in an operator immediately previous to that M,
delete the M.
54. Inserting Mental Operators:Rule 2
If a string of MKs belongs to a cognitive unit,
then delete all Ms except the first.
Example:If typing“100”,MKMKMK becomes
MKKK
55. Inserting Mental Operators:Rule 3
Deletion of Ms before consecutive terminators
If a K is a redundant delimiter at the end of a
cognitive unit,such as the delimiter of a
command immediately following the delimiter of
its argument,then delete the M in front of it.
Example:If coding in Java,ending a line with a“;”
followed by acarriage return. The semi-colon is a
terminator
, and the carriage return is aredundant
terminator
,since both serve to end the line of code,
thus,MKMK = MKK.
56. Inserting Mental Operators:Rule 4
Deletion of Ms that are terminators of
commands
If K is a delimiter that follows a constant string,a
command name (like“print”),or something that
is the same every time you use it,then delete
the M in front of it.
If a K terminates a variable string (e.g.,name of
file to be printed which is different each time),
leave it in.
57. Inserting Mental Operators:Rule 5
Deletion of overlapped Ms
Do not count any portion of an M that overlaps
an R – a delay
,with the user waiting for a
response from the computer