# 23

Standards in this Framework

Standards Mapped

# 43%

Mapped to Course

Standard Lessons
7351.D1.1
Define and discuss different examples of level-appropriate quantitative and qualitative data.
1. 4.1 Introduction to Predictive Models
2. 4.2 Correlation
3. 4.3 Programming Linear Regression
4. 4.4 Training and Testing Data
5. 4.5 Multivariable Linear Regression
6. 4.6 Classification and Logistic Regression
7. 4.7 Building Unsupervised Models
8. 4.8 Creating Your Own Predictive Model
7351.D1.2
Evaluate the tradeoffs in how data elements are organized and where data is stored.
7351.D1.3
Analyze and interpret data by identifying patterns and consider limitations of data analysis (e.g., measurement error, sample selection).
1. 4.2 Correlation
2. 4.3 Programming Linear Regression
3. 4.4 Training and Testing Data
4. 4.5 Multivariable Linear Regression
5. 4.6 Classification and Logistic Regression
6. 4.7 Building Unsupervised Models
7351.D1.4
Design and implement a plan using data collection tools and techniques to collect appropriate data to answer a relevant research question.
1. 4.1 Introduction to Predictive Models
2. 4.8 Creating Your Own Predictive Model
7351.D1.5
Create interactive data visualizations using software tools to help others better understand real-world phenomena.
1. 4.1 Introduction to Predictive Models
2. 4.2 Correlation
3. 4.3 Programming Linear Regression
4. 4.4 Training and Testing Data
5. 4.5 Multivariable Linear Regression
6. 4.6 Classification and Logistic Regression
7. 4.7 Building Unsupervised Models
8. 4.8 Creating Your Own Predictive Model
7351.D2.1
Compare and contrast concepts and uses of machine learning, deep learning, general artificial intelligence, and narrow artificial intelligence.
1. 1.1 What is Artificial Intelligence?
2. 1.2 Subsets of Artificial Intelligence
7351.D2.2
Investigate imbalances in training data in terms of gender, age, ethnicity, or other demographic variables that could result in a biased model, by using a data visualization tool.
1. 1.3 The Ethics of Artificial Intelligence
7351.D2.3
Research and describe the risks and risk mitigation strategies associated with the implementation of artificial intelligence and machine learning in the real world (e.g., biased decision making, lethal autonomous weapons, social media echo chambers, surveillance).
1. 1.3 The Ethics of Artificial Intelligence
2. 1.4 Project: Research an Ethical Dilemma in AI
7351.D2.4
Evaluate a dataset used to train a real AI system by considering the size of the dataset, the way that the data were acquired and labeled, the storage required, and the estimated time to produce the dataset.
7351.D2.5
Select the appropriate type of machine learning algorithm (supervised, unsupervised, or reinforcement learning) to solve a reasoning problem.
1. 4.8 Creating Your Own Predictive Model
7351.D2.6
Use a learning algorithm to train a model on data collected to answer a relevant research question, then evaluate the results.
1. 4.1 Introduction to Predictive Models
2. 4.2 Correlation
3. 4.3 Programming Linear Regression
4. 4.4 Training and Testing Data
5. 4.5 Multivariable Linear Regression
6. 4.6 Classification and Logistic Regression
7. 4.7 Building Unsupervised Models
8. 4.8 Creating Your Own Predictive Model
7351.D3.1
Analyze game elements of analog games (e.g., board, card, dice) and how those elements can be represented as algorithms for digital games.
1. 2.1 Artificial Intelligence in Gaming
2. 2.2 Building Tic Tac Toe
3. 2.3 Creating a Non Player Character
4. 2.4 Recursion
5. 2.5 Minimax
6. 2.6 Exploring Depth and Pruning
7. 2.7 Implementing Connect Four
7351.D3.2
Research and discuss best practices of user experience design for building video games and apps.
7351.D3.3
Document design decisions using text, graphics, presentations, and/or demonstrations in the development of games and applications.
7351.D3.4
Using the software application life cycle and prototype development model, develop a new application or game working in team roles using collaborative tools.
7351.D3.5
Develop and use a series of test cases to verify that a program performs according to its design specifications.
7351.D4.1
Examine the positive and negative impacts of a person/organizationâ€™s digital footprint.
7351.D4.2
Analyze the motives of threat actors.
7351.D4.3
Discuss the role that cyber ethics plays in current society.
7351.D4.4
Research and describe common attacks on hardware, software, and networks and identify methods of mitigating risk associated with each.
7351.D4.5
Evaluate authentication and authorization methods and the risks associated with failure.
7351.D4.6
Analyze the vulnerabilities of Internet of Things devices.
7351.D4.7
Utilizing cybersecurity best practices and the software development life cycle, make appropriate updates to a game or application design to protect it from vulnerabilities.