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Standards Mapping

for Arkansas Introduction to Computer Science

60

Standards in this Framework

21

Standards Mapped

35%

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. 16.1 Software Engineer
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. 6.1 Project: Quiz Game
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.
1.1.4
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. 4.1 Project: Mad Libs
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. 2.5 Top Down Design and Decomposition in Karel
  2. 7.4 Nested Control Structures
  3. 9.1 Functions
  4. 9.2 Functions and Parameters
  5. 9.4 Functions and Return Values
  6. 12.3 For Loops and Lists
  7. 25.21 Putting Together Control Structures
  8. 28.12 Functions and Parameters
  9. 28.17 For Loops and Lists
2.2.1
Use various data types (e.g., Booleans, characters, integers, floating points, and strings) appropriately within a program.
  1. 3.2 Variables and Types
  2. 3.3 User Input
  3. 3.4 Mathematical Operators
  4. 3.5 String Operators
  5. 5.5 Floating Point Numbers and Rounding
  6. 28.3 Variables and Types
  7. 28.5 String Operators
2.2.2
Create and use variables to store and manage data within a program.
  1. 3.2 Variables and Types
  2. 28.3 Variables and Types
2.2.3
Construct expressions using arithmetic operators (e.g., +, -, *, /, and %) and numeric data types to perform calculations within a program.
  1. 3.4 Mathematical Operators
2.2.4
Convert between different data types when necessary within a program (e.g., casting a string into an integer).
  1. 3.3 User Input
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).
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. 12.2 Lists
  2. 12.3 For Loops and Lists
  3. 12.4 List Methods
  4. 28.17 For Loops and Lists
2.3.1
Analyze a program in terms of steps of execution and expected outcomes (e.g., storyboards, flowcharts, and pseudocode).
  1. 2.14 Debugging Strategies
  2. 12.3 For Loops and Lists
  3. 28.17 For Loops and Lists
2.3.2
Construct Boolean expressions using relational operators (e.g., <, >, <=, >=, ==, and !=) within a program.
  1. 5.3 Comparison Operators
2.3.3
Construct Boolean expressions using logical operators (e.g, AND, OR, and NOT) within a program.
  1. 5.4 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. 5.2 If Statements
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. 7.1 While Loops
  2. 7.2 For Loops
  3. 28.10 For Loops
2.3.6
Create subroutines (e.g., procedures and functions) to modularize code for reusability and organization within a program.
  1. 9.1 Functions
  2. 9.2 Functions and Parameters
  3. 9.4 Functions and Return Values
  4. 28.12 Functions and Parameters
2.3.7
Debug errors (e.g., syntax, runtime, and logic) within a program to ensure program functionality.
  1. 2.14 Debugging Strategies
  2. 3.1 Printing in Python
  3. 3.2 Variables and Types
  4. 3.5 String Operators
  5. 28.2 Printing in Python
  6. 28.3 Variables and Types
  7. 28.5 String Operators
2.4.1
Use the console for basic data input and output operations within a program.
  1. 3.1 Printing in Python
  2. 3.3 User Input
  3. 28.2 Printing in Python
2.4.2
Explain the structure and purpose of different file types (e.g., txt, csv, bmp, and json) used for data storage.
  1. 15.1 What is File I/O
2.4.3
Develop programs that perform file operations including reading data from, writing data to, and appending data to files.
  1. 15.1 What is File I/O
  2. 15.3 Reading Line from File
  3. 15.5 Writing to File
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. 2.6 Commenting Your Code
  2. 3.2 Variables and Types
  3. 3.6 Comments
  4. 5.2 If Statements
  5. 7.1 While Loops
  6. 7.2 For Loops
  7. 9.1 Functions
  8. 28.3 Variables and Types
  9. 28.6 Comments
  10. 28.10 For Loops
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. 2.6 Commenting Your Code
  2. 3.6 Comments
  3. 28.6 Comments
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.
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.
3.1.4
Design basic data collection methods (e.g., short surveys, observation checklists, and simple experiments) appropriate for a specific purpose.
3.2.1
Identify and address data quality issues (e.g., missing values, inconsistencies, and outliers) to ensure the data is suitable for analysis.
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.
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.
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.
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.
5.1.2
Describe common cyber threats (e.g., malware, phishing, and ransomware) and how these threats can compromise data and information systems.
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.
5.1.4
Analyze the impact of best practices (e.g., strong passwords and security awareness training) in maintaining a secure digital environment.
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).
5.3.2
Identify and describe the functionalities of common network hardware (e.g., modems, routers, switches, firewalls, and servers) used in networks.
5.3.3
Explain how basic network security principles (e.g., access control, encryption, and firewalls) are used to safeguard networks.
5.3.4
Analyze the relationship between computer systems, software, and networks in creating interconnected computing environments.