< AI and Machine Learning Module
AI and Machine Learning Module Standards
AI4K12 National Guidelines 2021
BI-3 - Computers can learn from data
3-A-ii - Nature of Learning - finding patterns in data
- 3-A-ii.3-5 - Model how supervised learning identifies patterns in labeled data.
3-A-iv - Nature of Learning - constructinv vs using a reasoner
- 3-A-iv.3-5 - Demonstrate how training data are labeled when using a machine learning tool.
CSTA K-12 Computer Science Standards (2017)
AP - Algorithms & Programming
- 2-AP-17 - Systematically test and refine programs using a range of test cases.
IC - Impacts of Computing
- 2-IC-21 - Discuss issues of bias and accessibility in the design of existing technologies.
AI4K12 National Guidelines 2021
BI-3 - Computers can learn from data
3-A-i - Nature of Learning - humans vs machines
- 3-A-i.6-8 - Contrast the unique characteristics of human learning with the ways machine learning systems operate.
- 3-A-i.9-12 - Define supervised, unsupervised, and reinforcement learning algorithms, and give examples of human learning that are similar to each algorithm.
CSTA K-12 Computer Science Standards (2017)
IC - Impacts of Computing
- 2-IC-20 - Compare tradeoffs associated with computing technologies that affect people's everyday activities and career options.
AI4K12 National Guidelines 2021
BI-3 - Computers can learn from data
3-A-ii - Nature of Learning - finding patterns in data
- 3-A-ii.K-2 - Identify patterns in labeled data and determine the features that predict labels.
3-C-i - Datasets - feature sets
- 3-C-i.K-2 - Create a labeled dataset with explicit features to illustrate how computers can learn to classify things like foods, movies, or toys.
CSTA K-12 Computer Science Standards (2017)
DA - Data & Analysis
- 2-DA-09 - Refine computational models based on the data they have generated.
AI4K12 National Guidelines 2021
BI-3 - Computers can learn from data
3-A-ii - Nature of Learning - finding patterns in data
- 3-A-ii.3-5 - Model how supervised learning identifies patterns in labeled data.
3-A-iii - Nature of Learning - training a model
- 3-A-iii.6-8 - Train and evaluate a classification or prediction model using machine learning on a tabular dataset
CSTA K-12 Computer Science Standards (2017)
DA - Data & Analysis
- 3A-DA-12 - Create computational models that represent the relationships among different elements of data collected from a phenomenon or process.
AI4K12 National Guidelines 2021
BI-3 - Computers can learn from data
3-A-i - Nature of Learning - humans vs machines
- 3-A-i.6-8 - Contrast the unique characteristics of human learning with the ways machine learning systems operate.
3-A-iii - Nature of Learning - training a model
- 3-A-iii.6-8 - Train and evaluate a classification or prediction model using machine learning on a tabular dataset
3-C-i - Datasets - feature sets
- 3-C-i.3-5 - Create a labeled dataset with explicit features of several types and use a machine learning tool to train a classifier on this data.
- 3-C-i.6-8 - Create a dataset for training a decision tree classifier or predictor and explore the impact that different feature encodings have on the decision tree.
CSTA K-12 Computer Science Standards (2017)
AP - Algorithms & Programming
- 2-AP-17 - Systematically test and refine programs using a range of test cases.
DA - Data & Analysis
- 3A-DA-12 - Create computational models that represent the relationships among different elements of data collected from a phenomenon or process.
- 3B-DA-05 - Use data analysis tools and techniques to identify patterns in data representing complex systems.
CSTA K-12 Computer Science Standards (2017)
AP - Algorithms & Programming
- 2-AP-13 - Decompose problems and subproblems into parts to facilitate the design, implementation, and review of programs.
- 3A-AP-16 - Design and iteratively develop computational artifacts for practical intent, personal expression, or to address a societal issue by using events to initiate instructions.
IC - Impacts of Computing
- 2-IC-21 - Discuss issues of bias and accessibility in the design of existing technologies.
AI4K12 National Guidelines 2021
BI-3 - Computers can learn from data
3-A-iii - Nature of Learning - training a model
- 3-A-iii.3-5 - Train a classification model using machine learning, and then examine the accuracy of the model on new inputs
3-C-i - Datasets - feature sets
- 3-C-i.9-12 - Compare two real world datasets in terms of the features they comprise and how those features are encoded.
3-C-iii - Datasets - bias
- 3-C-iii.3-5 - Examine features and labels of training data to detect potential sources of bias.
- 3-C-iii.6-8 - Explain how the choice of training data shapes the behavior of the classifier, and how bias can be introduced if the training set is not properly balanced.
CSTA K-12 Computer Science Standards (2017)
IC - Impacts of Computing
- 2-IC-21 - Discuss issues of bias and accessibility in the design of existing technologies.
- 3A-IC-25 - Test and refine computational artifacts to reduce bias and equity deficits.
AI4K12 National Guidelines 2021
BI-3 - Computers can learn from data
3-A-iii - Nature of Learning - training a model
- 3-A-iii.6-8 - Train and evaluate a classification or prediction model using machine learning on a tabular dataset
3-C-i - Datasets - feature sets
- 3-C-i.3-5 - Create a labeled dataset with explicit features of several types and use a machine learning tool to train a classifier on this data.
- 3-C-i.6-8 - Create a dataset for training a decision tree classifier or predictor and explore the impact that different feature encodings have on the decision tree.
CSTA K-12 Computer Science Standards (2017)
AP - Algorithms & Programming
- 2-AP-19 - Document programs in order to make them easier to follow, test, and debug.
DA - Data & Analysis
- 3A-DA-12 - Create computational models that represent the relationships among different elements of data collected from a phenomenon or process.
- 3B-DA-05 - Use data analysis tools and techniques to identify patterns in data representing complex systems.
CSTA K-12 Computer Science Standards (2017)
AP - Algorithms & Programming
- 2-AP-11 - Create clearly named variables that represent different data types and perform operations on their values.
- 2-AP-12 - Design and iteratively develop programs that combine control structures, including nested loops and compound conditionals.
- 2-AP-13 - Decompose problems and subproblems into parts to facilitate the design, implementation, and review of programs.
- 2-AP-19 - Document programs in order to make them easier to follow, test, and debug.
AI4K12 National Guidelines 2021
BI-3 - Computers can learn from data
3-A-ii - Nature of Learning - finding patterns in data
- 3-A-ii.3-5 - Model how supervised learning identifies patterns in labeled data.
3-A-iii - Nature of Learning - training a model
- 3-A-iii.3-5 - Train a classification model using machine learning, and then examine the accuracy of the model on new inputs
CSTA K-12 Computer Science Standards (2017)
DA - Data & Analysis
- 3A-DA-12 - Create computational models that represent the relationships among different elements of data collected from a phenomenon or process.
AI4K12 National Guidelines 2021
BI-3 - Computers can learn from data
3-A-iii - Nature of Learning - training a model
- 3-A-iii.6-8 - Train and evaluate a classification or prediction model using machine learning on a tabular dataset
CSTA K-12 Computer Science Standards (2017)
AP - Algorithms & Programming
- 2-AP-17 - Systematically test and refine programs using a range of test cases.
DA - Data & Analysis
- 3A-DA-12 - Create computational models that represent the relationships among different elements of data collected from a phenomenon or process.
- 3B-DA-05 - Use data analysis tools and techniques to identify patterns in data representing complex systems.
CSTA K-12 Computer Science Standards (2017)
AP - Algorithms & Programming
- 2-AP-11 - Create clearly named variables that represent different data types and perform operations on their values.
- 2-AP-13 - Decompose problems and subproblems into parts to facilitate the design, implementation, and review of programs.
- 2-AP-19 - Document programs in order to make them easier to follow, test, and debug.
CSTA K-12 Computer Science Standards (2017)
IC - Impacts of Computing
- 2-IC-21 - Discuss issues of bias and accessibility in the design of existing technologies.
- 3A-IC-24 - Evaluate the ways computing impacts personal, ethical, social, economic, and cultural practices.
AI4K12 National Guidelines 2021
BI-3 - Computers can learn from data
3-A-ii - Nature of Learning - finding patterns in data
- 3-A-ii.9-12 - Use either a supervised or unsupervised learning algorithm to train a model on real world data, then evaluate the results.
3-A-iii - Nature of Learning - training a model
- 3-A-iii.6-8 - Train and evaluate a classification or prediction model using machine learning on a tabular dataset
3-C-i - Datasets - feature sets
- 3-C-i.6-8 - Create a dataset for training a decision tree classifier or predictor and explore the impact that different feature encodings have on the decision tree.
3-C-iii - Datasets - bias
- 3-C-iii.6-8 - Explain how the choice of training data shapes the behavior of the classifier, and how bias can be introduced if the training set is not properly balanced.
CSTA K-12 Computer Science Standards (2017)
AP - Algorithms & Programming
- 2-AP-19 - Document programs in order to make them easier to follow, test, and debug.
DA - Data & Analysis
- 3A-DA-12 - Create computational models that represent the relationships among different elements of data collected from a phenomenon or process.
- 3B-DA-05 - Use data analysis tools and techniques to identify patterns in data representing complex systems.
AI4K12 National Guidelines 2021
BI-3 - Computers can learn from data
3-A-iv - Nature of Learning - constructinv vs using a reasoner
- 3-A-iv.9-12 - Illustrate what happens during each of the steps required when using machine learning to construct a classifier or predictor.
CSTA K-12 Computer Science Standards (2017)
AP - Algorithms & Programming
- 2-AP-15 - Seek and incorporate feedback from team members and users to refine a solution that meets user needs.
AI4K12 National Guidelines 2021
BI-3 - Computers can learn from data
3-A-iv - Nature of Learning - constructinv vs using a reasoner
- 3-A-iv.9-12 - Illustrate what happens during each of the steps required when using machine learning to construct a classifier or predictor.
3-C-i - Datasets - feature sets
- 3-C-i.K-2 - Create a labeled dataset with explicit features to illustrate how computers can learn to classify things like foods, movies, or toys.
3-C-iii - Datasets - bias
- 3-C-iii.6-8 - Explain how the choice of training data shapes the behavior of the classifier, and how bias can be introduced if the training set is not properly balanced.
CSTA K-12 Computer Science Standards (2017)
AP - Algorithms & Programming
- 2-AP-15 - Seek and incorporate feedback from team members and users to refine a solution that meets user needs.
IC - Impacts of Computing
- 2-IC-22 - Collaborate with many contributors through strategies such as crowdsourcing or surveys when creating a computational artifact.
AI4K12 National Guidelines 2021
BI-3 - Computers can learn from data
3-A-iii - Nature of Learning - training a model
- 3-A-iii.6-8 - Train and evaluate a classification or prediction model using machine learning on a tabular dataset
3-A-iv - Nature of Learning - constructinv vs using a reasoner
- 3-A-iv.9-12 - Illustrate what happens during each of the steps required when using machine learning to construct a classifier or predictor.
3-C-i - Datasets - feature sets
- 3-C-i.6-8 - Create a dataset for training a decision tree classifier or predictor and explore the impact that different feature encodings have on the decision tree.
- 3-C-i.9-12 - Compare two real world datasets in terms of the features they comprise and how those features are encoded.
CSTA K-12 Computer Science Standards (2017)
AP - Algorithms & Programming
- 2-AP-19 - Document programs in order to make them easier to follow, test, and debug.
DA - Data & Analysis
- 2-DA-08 - Collect data using computational tools and transform the data to make it more useful and reliable.
- 3A-DA-12 - Create computational models that represent the relationships among different elements of data collected from a phenomenon or process.
IC - Impacts of Computing
- 2-IC-22 - Collaborate with many contributors through strategies such as crowdsourcing or surveys when creating a computational artifact.
AI4K12 National Guidelines 2021
BI-3 - Computers can learn from data
3-A-iv - Nature of Learning - constructinv vs using a reasoner
- 3-A-iv.9-12 - Illustrate what happens during each of the steps required when using machine learning to construct a classifier or predictor.
3-C-iii - Datasets - bias
- 3-C-iii.3-5 - Examine features and labels of training data to detect potential sources of bias.
- 3-C-iii.6-8 - Explain how the choice of training data shapes the behavior of the classifier, and how bias can be introduced if the training set is not properly balanced.
CSTA K-12 Computer Science Standards (2017)
AP - Algorithms & Programming
- 2-AP-15 - Seek and incorporate feedback from team members and users to refine a solution that meets user needs.
IC - Impacts of Computing
- 2-IC-21 - Discuss issues of bias and accessibility in the design of existing technologies.
- 2-IC-22 - Collaborate with many contributors through strategies such as crowdsourcing or surveys when creating a computational artifact.
CSTA K-12 Computer Science Standards (2017)
AP - Algorithms & Programming
- 2-AP-11 - Create clearly named variables that represent different data types and perform operations on their values.
- 2-AP-12 - Design and iteratively develop programs that combine control structures, including nested loops and compound conditionals.
- 2-AP-13 - Decompose problems and subproblems into parts to facilitate the design, implementation, and review of programs.
AI4K12 National Guidelines 2021
BI-3 - Computers can learn from data
3-A-ii - Nature of Learning - finding patterns in data
- 3-A-ii.9-12 - Use either a supervised or unsupervised learning algorithm to train a model on real world data, then evaluate the results.
3-A-iii - Nature of Learning - training a model
- 3-A-iii.6-8 - Train and evaluate a classification or prediction model using machine learning on a tabular dataset
3-A-iv - Nature of Learning - constructinv vs using a reasoner
- 3-A-iv.9-12 - Illustrate what happens during each of the steps required when using machine learning to construct a classifier or predictor.
3-C-i - Datasets - feature sets
- 3-C-i.6-8 - Create a dataset for training a decision tree classifier or predictor and explore the impact that different feature encodings have on the decision tree.
3-C-iii - Datasets - bias
- 3-C-iii.6-8 - Explain how the choice of training data shapes the behavior of the classifier, and how bias can be introduced if the training set is not properly balanced.
CSTA K-12 Computer Science Standards (2017)
AP - Algorithms & Programming
- 2-AP-11 - Create clearly named variables that represent different data types and perform operations on their values.
- 2-AP-12 - Design and iteratively develop programs that combine control structures, including nested loops and compound conditionals.
- 2-AP-13 - Decompose problems and subproblems into parts to facilitate the design, implementation, and review of programs.
- 2-AP-19 - Document programs in order to make them easier to follow, test, and debug.
DA - Data & Analysis
- 2-DA-08 - Collect data using computational tools and transform the data to make it more useful and reliable.
- 3A-DA-12 - Create computational models that represent the relationships among different elements of data collected from a phenomenon or process.
- 3B-DA-05 - Use data analysis tools and techniques to identify patterns in data representing complex systems.
IC - Impacts of Computing
- 2-IC-22 - Collaborate with many contributors through strategies such as crowdsourcing or surveys when creating a computational artifact.
