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Standards Mapping

for Arkansas Introduction to Computer Science

60

Standards in this Framework

34

Standards Mapped

56%

Mapped to Course

Standard Lessons
1.1.1
Demonstrate understanding of various career paths in computer science (e.g., software development, data science, cybersecurity, networking, robotics, computer engineering, and artificial intelligence) and their roles in different industries.
1.1.2
Practice professional communication skills through various activities (e.g., mock interviews, technical presentations, and collaborative coding projects) to prepare for real-world scenarios in computer science careers.
  1. 14.1 CyberEthics
  2. 16.1 Present a Data-Driven Insight
  3. 17.1 The Impacts of Computing
1.1.3
Describe the importance of ethical considerations in computer science (e.g., data privacy, algorithmic bias, and social impact of technology) and how they apply to professional practice.
  1. 13.7 Network Attacks
  2. 14.1 CyberEthics
  3. 17.1 The Impacts of Computing
1.1.4
Evaluate the concept and importance of a professional online presence in computer science careers (e.g., portfolio websites, professional networking platforms, open-source contributions) and explore ways to showcase skills and projects.
1.1.5
Identify key elements of a professional portfolio and explain the purpose of a portfolio (e.g., showcasing skills, documenting growth, and supporting college applications or job searches).
2.1.1
Recognize situations where leveraging computational approaches (e.g., data analysis, automation, or simulations) would be beneficial for solving real-world problems.
  1. 15.1 Getting Started with Data
  2. 15.2 Visualizing and Interpreting Data
  3. 15.3 Data Collection & Limitations
  4. 15.4 Unintentional Data Sharing
  5. 15.5 Metadata and Data Collection
  6. 16.1 Present a Data-Driven Insight
2.1.2
Apply the core computational thinking principles—abstraction (focusing on essential elements), decomposition (breaking problems into manageable parts), algorithm development (creating step-by-step solutions), and pattern recognition (identifying recurring elements)—to designing effective problem-solving strategies.
  1. 1.7 Top Down Design and Decomposition in Karel
  2. 1.9 Abstraction
  3. 1.16 Debugging Strategies
2.2.1
Use various data types (e.g., Booleans, characters, integers, floating points, and strings) appropriately within a program.
  1. 3.4 Variables
  2. 4.1 Booleans
2.2.2
Create and use variables to store and manage data within a program.
  1. 3.4 Variables
2.2.3
Construct expressions using arithmetic operators (e.g., +, -, *, /, and %) and numeric data types to perform calculations within a program.
  1. 3.6 Basic Math in JavaScript
2.2.4
Convert between different data types when necessary within a program (e.g., casting a string into an integer).
2.2.5
Perform operations that encode and decode data from one form into another form (e.g., binary to hexadecimal, numeric values to colored pixels, or numbers to ASCII/Unicode representations).
  1. 8.2 Number Systems
  2. 8.3 Encoding Text with Binary
  3. 8.5 Hexadecimal
2.2.6
Implement data structures (e.g., arrays, lists, sets, and maps) to organize, store, manipulate, and perform operations on collections of data within a program.
  1. 7.1 Intro to Lists/Arrays
  2. 7.2 Indexing Into an Array
  3. 7.3 Adding/Removing From an Array
  4. 7.4 Array Length and Looping Through Arrays
  5. 7.5 Iterating Over an Array
  6. 7.6 Finding an Element in a List
  7. 7.7 Removing an Element From an Array
  8. 7.8 Applying Iteration - Usernames and Passwords
  9. 7.9 Simulation
2.3.1
Analyze a program in terms of steps of execution and expected outcomes (e.g., storyboards, flowcharts, and pseudocode).
  1. 2.1 Practice PT: Pair-Programming Paint!
  2. 6.1 Project: Tell a Story
  3. 10.3 Vigenère Cipher
2.3.2
Construct Boolean expressions using relational operators (e.g., <, >, <=, >=, ==, and !=) within a program.
  1. 4.1 Booleans
  2. 4.3 Comparison Operators
2.3.3
Construct Boolean expressions using logical operators (e.g, AND, OR, and NOT) within a program.
  1. 4.1 Booleans
  2. 4.2 Logical Operators
2.3.4
Create programs that implement selection control structures (e.g., if statements and switch statements) to make decisions and execute different code paths based on conditions.
  1. 4.4 If Statements
  2. 4.5 Applying Conditionals - Password Validation
2.3.5
Create programs that implement iteration control structures (e.g., while loops and for loops) to repeat code blocks a specific number of times or until a condition is met.
  1. 4.7 For Loops in JavaScript
  2. 4.8 General For Loops
  3. 4.9 For Loop Practice
  4. 4.11 While Loops
  5. 4.12 Loop and a Half
2.3.6
Create subroutines (e.g., procedures and functions) to modularize code for reusability and organization within a program.
  1. 5.1 Functions and Parameters 1
  2. 5.2 Functions and Parameters 2
  3. 5.3 Functions and Parameters 3
  4. 5.4 Functions and Return Values 1
  5. 5.5 Functions and Return Values 2
2.3.7
Debug errors (e.g., syntax, runtime, and logic) within a program to ensure program functionality.
  1. 1.16 Debugging Strategies
2.4.1
Use the console for basic data input and output operations within a program.
  1. 3.5 User Input
2.4.2
Explain the structure and purpose of different file types (e.g., txt, csv, bmp, and json) used for data storage.
2.4.3
Develop programs that perform file operations including reading data from, writing data to, and appending data to files.
2.5.1
Implement consistent formatting and naming conventions within the code (e.g., indentation, spacing, variable names) to improve code readability and maintainability.
  1. 1.18 How to Indent Your Code
  2. 3.4 Variables
2.5.2
Craft clear and concise comments within the code to explain the purpose of different code sections, algorithms used, and non-obvious logic.
  1. 1.8 Commenting Your Code
3.1.1
Categorize data into different types (e.g., quantitative - continuous and discrete, qualitative - nominal and ordinal) and understand the distinction between them.
3.1.2
Identify potential sources of data (e.g., sensors, surveys, databases, and web scraping) based on the type of data needed.
  1. 15.3 Data Collection & Limitations
3.1.3
Explain the advantages and disadvantages of different data collection methods (e.g., surveys for user opinions, experiments for cause-effect relationships, and observational studies for naturalistic data) considering factors like accuracy, cost, and time.
  1. 15.1 Getting Started with Data
  2. 15.2 Visualizing and Interpreting Data
  3. 15.3 Data Collection & Limitations
  4. 15.4 Unintentional Data Sharing
  5. 15.5 Metadata and Data Collection
  6. 16.1 Present a Data-Driven Insight
3.1.4
Design basic data collection methods (e.g., short surveys, observation checklists, and simple experiments) appropriate for a specific purpose.
  1. 15.1 Getting Started with Data
  2. 15.2 Visualizing and Interpreting Data
  3. 15.3 Data Collection & Limitations
  4. 15.4 Unintentional Data Sharing
  5. 15.5 Metadata and Data Collection
  6. 16.1 Present a Data-Driven Insight
3.2.1
Identify and address data quality issues (e.g., missing values, inconsistencies, and outliers) to ensure the data is suitable for analysis.
  1. 15.1 Getting Started with Data
  2. 15.2 Visualizing and Interpreting Data
  3. 15.3 Data Collection & Limitations
  4. 15.4 Unintentional Data Sharing
  5. 15.5 Metadata and Data Collection
  6. 16.1 Present a Data-Driven Insight
3.2.2
Analyze data sets utilizing appropriate descriptive statistics (e.g., mean, median, quartiles, and range) and visualizations (e.g., histograms and box plots).
3.2.3
Visually inspect and use exploratory analysis techniques to discern patterns, trends, and relationships within the data.
  1. 15.1 Getting Started with Data
  2. 15.2 Visualizing and Interpreting Data
  3. 15.3 Data Collection & Limitations
  4. 15.4 Unintentional Data Sharing
  5. 15.5 Metadata and Data Collection
  6. 16.1 Present a Data-Driven Insight
3.2.4
Develop programs that perform data analysis techniques (e.g., finding correlations and comparison of means) appropriate for the data and question at hand.
3.2.5
Create programs to generate suitable visualizations (e.g., bar charts for comparisons, line charts for trends, scatter plots for relationships, and pie charts for part-to-whole) for the data and question at hand.
3.3.1
Recognize and explain how potential biases (e.g., confirmation, selection, and reporting) within a data source could influence the analysis and resulting insights.
3.3.2
Develop clear and concise narratives that effectively communicate data insights to diverse audiences, leveraging storytelling techniques to enhance understanding and engagement.
  1. 16.1 Present a Data-Driven Insight
3.3.3
Demonstrate a clear understanding of the difference between correlation and causation, ensuring conclusions accurately reflect the relationships identified in the data analysis.
3.3.4
Develop sound inferences from the data to support informed decision-making, avoiding overstated or misleading implications based on the findings.
3.3.5
Present both positive and negative findings in a comprehensive and unbiased way, ensuring accurate data representation for the audience.
4.1.1
Define artificial intelligence (AI), identify its key subfields, and explain its benefits and potential drawbacks in real-world applications.
4.1.2
Explain cloud computing, its service models (e.g., Infrastructure as a Service, Platform as a Service, and Software as a Service), and discuss how it has transformed the IT landscape, including its advantages and challenges.
4.1.3
Define the Internet of Things (IoT), recognize its real-world applications, and discuss its impact on data generation, automation, and interconnectedness.
  1. 13.11 Cybersecurity Risk Model-Security Controls
4.1.4
Explain the concept of big data, its defining characteristics, and its impact on data analysis, decision-making, and various industries.
4.2.1
Define quantum computing, explaining its basic principles and potential to revolutionize computing power and cryptography.
4.2.2
Describe edge computing and its role in processing data closer to the source, discussing its advantages in reducing latency and enhancing privacy.
4.2.3
Explain the concept of extended reality (e.g., virtual reality, augmented reality, and mixed reality) and its potential applications across various industries.
4.2.4
Describe the capabilities of blockchain technology and assess its potential uses across different sectors (e.g., supply chain management, identity verification systems, and decentralized financial services).
5.1.1
Analyze the core principles for establishing comprehensive information security strategies (e.g., confidentiality, integrity, availability, authentication, and non-repudiation) when using data and information systems.
  1. 13.2 CIA Triad
5.1.2
Describe common cyber threats (e.g., malware, phishing, and ransomware) and how these threats can compromise data and information systems.
  1. 13.1 Introduction to Cybersecurity
  2. 13.4 Let Me In
  3. 13.6 OSI model activity
  4. 13.7 Network Attacks
5.1.3
Describe essential cybersecurity practices (e.g., data encryption, secure coding, and user authentication) and technologies (e.g., firewalls and multi-factor authentication) implemented to safeguard information.
  1. 13.1 Introduction to Cybersecurity
  2. 13.5 Introduction to Networking
  3. 13.7 Network Attacks
  4. 13.8 Basic Cybersecurity Risk Concepts
  5. 13.9 Cybersecurity Risk Model-Assessing Risk
  6. 13.10 Cybersecurity Risk Model-Vulnerabilities
  7. 13.11 Cybersecurity Risk Model-Security Controls
5.1.4
Analyze the impact of best practices (e.g., strong passwords and security awareness training) in maintaining a secure digital environment.
  1. 13.1 Introduction to Cybersecurity
  2. 13.5 Introduction to Networking
  3. 13.7 Network Attacks
  4. 13.8 Basic Cybersecurity Risk Concepts
  5. 13.9 Cybersecurity Risk Model-Assessing Risk
  6. 13.10 Cybersecurity Risk Model-Vulnerabilities
  7. 13.11 Cybersecurity Risk Model-Security Controls
5.2.1
Identify and explain the purpose of key hardware components (e.g., CPU, RAM, storage, and I/O devices) within various computing platforms (e.g., desktops, laptops, and mobile devices) and how they work together to enable the overall system operation.
5.2.2
Define software and distinguish between system software (e.g., operating systems, device drivers) and application software (e.g., word processors, web browsers, games).
5.2.3
Explain the basic functions of an operating system (e.g., managing hardware resources, providing user interface, handling file systems) and its importance in computer operations.
5.2.4
Summarize the concept of open-source software, its benefits, and its impact on software development and distribution.
5.2.5
Explain the importance of software updates and patches in maintaining security and improving functionality.
5.3.1
Define and explain the significance of common network concepts (e.g., IP address, topology, protocols, bandwidth, and latency).
  1. 12.3 Internet Addresses
  2. 13.5 Introduction to Networking
5.3.2
Identify and describe the functionalities of common network hardware (e.g., modems, routers, switches, firewalls, and servers) used in networks.
  1. 12.6 Routing
5.3.3
Explain how basic network security principles (e.g., access control, encryption, and firewalls) are used to safeguard networks.
  1. 13.1 Introduction to Cybersecurity
  2. 13.5 Introduction to Networking
  3. 13.7 Network Attacks
  4. 13.8 Basic Cybersecurity Risk Concepts
5.3.4
Analyze the relationship between computer systems, software, and networks in creating interconnected computing environments.
  1. 12.1 Welcome to the Internet
  2. 12.2 Internet Hardware
  3. 12.8 Sequential, Parallel & Distributed Computing