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Standards Mapping

for Indiana Topics in Computer Science


Standards in this Framework


Standards Mapped


Mapped to Course

Standard Lessons
Define and discuss different examples of level-appropriate quantitative and qualitative data.
  1. 7.3 Gathering Data
Evaluate the tradeoffs in how data elements are organized and where data is stored.
  1. 7.8 Pandas DataFrames
Analyze and interpret data by identifying patterns and consider limitations of data analysis (e.g., measurement error, sample selection).
  1. 7.7 Measures of Spread
Design and implement a plan using data collection tools and techniques to collect appropriate data to answer a relevant research question.
  1. 8.8 Aggregating Data
  2. 8.10 Your Business Data
Create interactive data visualizations using software tools to help others better understand real-world phenomena.
  1. 8.5 Data Visualizations
  2. 8.6 Line and Bar Charts
Compare and contrast concepts and uses of machine learning, deep learning, general artificial intelligence, and narrow artificial intelligence.
  1. 9.2 Artificial Intelligence and Machine Learning
  2. 9.3 Machine Learning and Neural Networks
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. 8.11 Bias in Data Analytics
  2. 9.4 The Ethics of Artificial Intelligence
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. 9.4 The Ethics of Artificial Intelligence
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.
  1. 12.1 Final Project
Select the appropriate type of machine learning algorithm (supervised, unsupervised, or reinforcement learning) to solve a reasoning problem.
  1. 9.2 Artificial Intelligence and Machine Learning
Use a learning algorithm to train a model on data collected to answer a relevant research question, then evaluate the results.
  1. 12.1 Final Project
Analyze game elements of analog games (e.g., board, card, dice) and how those elements can be represented as algorithms for digital games.
  1. 1.3 What Makes a Good Game?
Research and discuss best practices of user experience design for building video games and apps.
  1. 1.3 What Makes a Good Game?
  2. 2.2 Develop Your Game
Document design decisions using text, graphics, presentations, and/or demonstrations in the development of games and applications.
  1. 2.2 Develop Your Game
Using the software application life cycle and prototype development model, develop a new application or game working in team roles using collaborative tools.
  1. 2.1 Software Development Life Cycle
  2. 2.2 Develop Your Game
Develop and use a series of test cases to verify that a program performs according to its design specifications.
  1. 2.2 Develop Your Game
Examine the positive and negative impacts of a person/organization’s digital footprint.
  1. 3.2 Digital Footprint and Responsibility
  2. 3.3 Personal Data and Collection
Analyze the motives of threat actors.
  1. 3.8 Common Cyber Attacks and Prevention
Discuss the role that cyber ethics plays in current society.
  1. 3.5 Cyber Ethics and Laws
Research and describe common attacks on hardware, software, and networks and identify methods of mitigating risk associated with each.
  1. 3.8 Common Cyber Attacks and Prevention
Evaluate authentication and authorization methods and the risks associated with failure.
  1. 3.6 Personal Data Security
  2. 3.7 Cybersecurity Essentials
Analyze the vulnerabilities of Internet of Things devices.
  1. 3.8 Common Cyber Attacks and Prevention
Utilizing cybersecurity best practices and the software development life cycle, make appropriate updates to a game or application design to protect it from vulnerabilities.
  1. 3.8 Common Cyber Attacks and Prevention