Computer Science Worksheets

Learning Objectives

• Define the terms recommendation, preference, suggestion, and algorithm in the context of recommendation systems.
• Explain how recommendation systems use past choices and patterns in data to suggest items.
• Identify examples of how suggestions can be helpful but not always perfect.

Learning Objectives

• Explain how recommendation systems use data and patterns to suggest content.
• Compare common recommendation approaches and describe their strengths and limits.
• Evaluate how recommendations can differ for users and affect choices.

Learning Objectives

• Define recommendation, preference, suggestion, and algorithm in the context of online content
• Explain how recommendation systems use patterns in data to rank what people see
• Identify factors that can shape recommendations and describe an effect on what people view+ Show more

Learning Objectives

• Explain how responsible AI use includes checking results and thinking critically.
• Use key vocabulary such as responsible, judgment, accuracy, and ethics in context.
• Identify safe, fair, and ethical ways to use AI tools in school tasks.

Learning Objectives

• Explain what input is and how it is used in a computer system to produce output
• Identify common examples of input devices and describe how they send data to the computer
• Evaluate whether a statement about input and output is correct+ Show more

Learning Objectives

• Explain how normalization reduces data redundancy and update anomalies in relational databases.
• Compare an unnormalized table with a normalized design and justify the benefits.
• Identify functional dependencies that motivate splitting tables during normalization.

Learning Objectives

• Define the terms input, output, and response using simple program examples.
• Identify what counts as an input and what counts as an output in a short scenario.
• Use the words input, output, program, and response to complete sentences about how programs work.+ Show more

Learning Objectives

• Explain how input devices collect data and how programs process that data.
• Differentiate between input and output in a computer system using examples.
• Evaluate statements about signals, devices, and the flow of information.

Learning Objectives

• Identify the correct sequence of the software development lifecycle (planning, design, building, testing, maintenance).
• Explain how lifecycle steps help teams manage projects and reduce risks.
• Apply lifecycle knowledge to scenarios involving games, apps, and websites.

Learning Objectives

• Explain how inheritance supports code reuse and extension in object-oriented design.
• Describe polymorphism and how it enables different objects to respond to the same method call.
• Identify correct uses of method overriding and dynamic dispatch in class hierarchies.

Learning Objectives

• Explain how validation, access control, and design choices support data integrity in databases.
• Identify common security risks to data confidentiality, integrity, and availability.
• Apply correct decision-making when choosing safeguards for real-world data storage and access.

Learning Objectives

• Explain how file input/output (I/O) works, including reading data and writing results to files.
• Describe serialization and how it enables structured data to be stored or transmitted and later reconstructed.
• Evaluate common design choices for saving and loading data, such as format, versioning, and error handling.

Learning Objectives

• Explain how encryption and decryption protect information during secure communication.
• Identify the role of a key in encryption and understand why it matters for security.
• Evaluate whether statements about encryption and secure communication are correct.

Learning Objectives

• Explain how functions help organize reusable code and reduce repetition
• Describe the roles of parameters and return values in modular programming
• Use function ideas to reason about program flow, debugging, and readability

Learning Objectives

• Identify how CSS selectors and properties work together to style HTML elements.
• Interpret simple CSS rules and predict how they affect the look of a webpage.
• Choose readable styling choices using font, color, spacing, and borders.

Learning Objectives

• Explain how version control helps teams track changes and collaborate safely.
• Describe the roles of commits, branches, and merges in the development workflow.
• Evaluate scenarios to determine why version history matters and how overwriting is avoided.+ Show more

Learning Objectives

• Order key incident response steps from detection to recovery while explaining why each step matters.
• Describe how documentation supports evidence handling, communication, and post-incident improvement.
• Select the best action during incident recovery to restore normal operations safely.

Learning Objectives

• Explain how decomposition breaks a complex problem into smaller subproblems with clear goals.
• Apply a decomposition strategy to plan steps for a solution.
• Evaluate whether proposed steps match the subproblems needed for a solution.

Learning Objectives

• Explain how conditional statements use comparisons to make decisions in a program.
• Use branching logic with if/else to choose between actions based on true or false conditions.
• Recognize and describe the purpose of nested decision structures in more complex problems.

Learning Objectives

• Define decomposition as breaking a big problem into smaller steps or subproblems
• Identify steps and subproblems in a planning task and match them to a correct solution
• Use the idea of steps to describe how a solution can be built in parts+ Show more

Learning Objectives

• Identify the purpose of a project plan before writing code.
• Describe how goals and features guide the steps of a simple coding project.
• Use clear instructions to reduce errors when building a digital tool or game.

Learning Objectives

• Explain how conditional logic uses an if-then condition to choose between branches in an algorithm.
• Identify how inputs and data lead to different outcomes in branching systems.
• Use conditional vocabulary (condition, branch, outcome) correctly when describing decision paths.

Learning Objectives

• Explain how website performance affects search visibility and user behavior.
• Identify common performance metrics and optimization techniques related to SEO.
• Apply data-informed reasoning to improve page load speed and technical SEO.

Learning Objectives

• Explain how privacy, transparency, accountability, and responsibility apply to AI systems
• Identify ethical risks that can happen when AI makes or affects decisions
• Suggest responsible actions for using AI in school and society

Learning Objectives

• Define responsible, judgment, accuracy, and privacy in AI contexts
• Identify strategies for checking AI results for accuracy
• Explain why human judgment remains necessary when using AI+ Show more

Learning Objectives

• Define survey, data, collect, and response and use the words in sentences.
• Identify the best way to collect information using a simple classroom survey.
• Use collected survey data to choose, match, and explain correct ideas about preferences and classroom information.+ Show more

Learning Objectives

• Identify pattern, rule, and predict in numeric, visual, and action examples.
• Explain how a pattern rule helps decide what comes next.
• Use vocabulary words (pattern, rule, predict, repeat) to describe pattern thinking.+ Show more

Learning Objectives

• Identify common uses of different data visualization types such as charts and graphs.
• Explain how visualizations reveal patterns and trends that may be hard to see in raw data.
• Use key visualization vocabulary correctly, including visualization, chart, graph, and trend.

Learning Objectives

• Choose an appropriate graph type to match different kinds of data.
• Use key vocabulary (visualization, graph, chart, category) to explain why a graph fits the data.
• Compare how line graphs, bar graphs, and circle graphs show patterns and comparisons.

Learning Objectives

• Explain how hash functions and keys enable fast lookup in hash tables
• Analyze how collisions and load factor affect lookup performance
• Compare hash-table search efficiency with other lookup methods

Learning Objectives

• Explain how testing helps validate an algorithm under different conditions.
• Identify ways to revise an algorithm based on observed test results.
• Use vocabulary such as testing, validation, refinement, and revision to describe improvement steps.

Learning Objectives

• Explain how linear search works and when it is efficient.
• Describe how binary search works and why it requires a sorted list.
• Compare searching algorithms by time efficiency and suitability for different data sets.

Learning Objectives

• Explain the difference between testing and debugging and when to use each.
• Apply strategies such as unit testing, tracing, and edge-case checks to improve reliability.
• Distinguish expected vs. actual behavior and identify likely causes of common failures.

Learning Objectives

• Compare how linear search and binary search locate a target value in a collection.
• Trace the step-by-step decisions made by linear and binary search.
• Explain when binary search requires sorted data and how that affects correctness.+ Show more

Learning Objectives

• Explain how ethics, accountability, transparency, and responsibility relate to AI decisions.
• Identify ethical risks in everyday AI examples, including privacy and biased outcomes.
• Describe practical steps to improve trust in AI systems.

Learning Objectives

• Interpret flowcharts by following arrows and decisions to predict an outcome.
• Use correct flowchart vocabulary to describe a process and its branches.
• Create a simple process model using decision points and branches.

Learning Objectives

• Explain how unit tests verify small parts of code and support reliable changes.
• Use test results, error messages, and tracing to debug software systematically.
• Design test cases that include both normal and edge conditions.+ Show more

Learning Objectives

• Explain how databases support reporting by selecting, filtering, and aggregating stored data.
• Describe how analytics can reveal trends and compare groups using grouped results and clear charts.
• Evaluate whether a report’s summary accurately reflects the underlying records.

Learning Objectives

• Define and use spreadsheet vocabulary including worksheet, cell, row, and column.
• Identify rows, columns, and cells in a simple spreadsheet layout.
• Use sorting to reorganize information while keeping related data together.+ Show more

Learning Objectives

• Explain how classes define attributes and behaviors, and how objects are created from classes.
• Use constructors and methods correctly to initialize and operate on object state.
• Describe encapsulation and how access control helps protect an object's data.

Learning Objectives

• Describe the difference between a pattern and a trend in data.
• Explain how correlation relates to pattern changes over time or across categories.
• Use careful analysis to make a reasonable prediction from data.

Learning Objectives

• Explain how testing helps find problems in a digital project and how it supports improvement.
• Use user feedback to identify errors, confusion, and missing features.
• Describe practical steps to revise and improve a project after collecting observations.

Learning Objectives

• Explain how loops use repetition to perform the same steps multiple times efficiently
• Identify iteration patterns in algorithms and predict what a loop will do
• Use correct loop vocabulary such as loop, iteration, repeat, and efficiency

Learning Objectives

• Explain how program commands run in the order they are written.
• Identify how changing the order of commands changes the output.
• Use sequence and order vocabulary to describe working vs. non-working code.

Learning Objectives

• Explain what debugging is and why errors happen in programs.
• Use debugging steps to find where a program goes wrong and fix it.
• Identify common error causes such as incorrect order, missing code, and wrong values.

Learning Objectives

• Explain how primary keys uniquely identify records in a table.
• Describe how foreign keys create relationships between tables.
• Interpret relationships to understand how connected data preserves structure.

Learning Objectives

• Explain how SQL joins combine rows from multiple tables and when to use different join types.
• Interpret SQL queries that use GROUP BY and aggregate functions to summarize data.
• Predict query results by reasoning about matching keys, grouping, and filtering.

Learning Objectives

• Explain what bias is in AI and how it can affect outcomes.
• Describe fairness by using the ideas of representation and evaluation.
• Identify ways to reduce bias by checking data and test results.