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Course map · Planning data

AP CSP course map

All five Big Ideas and 35 topics from the launch kit curriculum map (version 0.1). Every topic is planned. Available in this preview: a 12-lesson, 8-lab pilot course, Sequence 1 on list traversal, and a short sample journey (a lesson, a lab and a three-question check) that touches 3 topics.

Record counts

Topics in map
35
Proposed lesson slots
70
Proposed course labs
30
Sample activities available
3

Counts are calculated from the supplied records. Planned slots are not completed teaching content.

Big Idea 1: Creative Development

CRD · 4 topics
Topic 1.1

Collaboration

Planned

Explain how collaboration, diverse perspectives and computing tools support development.

Objectives:
CRD-1.A, CRD-1.B, CRD-1.C
Course lab:
F01 Collaborative product brief (Planned)

Proposed lessons

  • C01Working together on a problemPlanned
  • C02Reviewing a collaborative solutionPlanned
Topic 1.2

Program Function and Purpose

Planned · sample touches

Distinguish program purpose, behavior, inputs and outputs.

Objectives:
CRD-2.A, CRD-2.B, CRD-2.C, CRD-2.D
Course lab:
F02 Input to output investigation (Planned)

Proposed lessons

  • C03Purpose and usersPlanned
  • C04Inputs outputs and behaviorPlanned

Available now: one sample activity uses ideas from this topic. It is not full topic coverage.

Topic 1.3

Program Design and Development

Planned

Develop, document and evaluate a solution against user needs.

Objectives:
CRD-2.E, CRD-2.F, CRD-2.G, CRD-2.H
Course lab:
F01 Collaborative product brief (Planned)

Proposed lessons

  • C05Designing for a userPlanned
  • C06Iterating documenting and evaluatingPlanned
Topic 1.4

Identifying and Correcting Errors

Planned

Identify and correct errors; choose inputs with expected outputs that test requirements.

Objectives:
CRD-2.I, CRD-2.J
Course lab:
F03 Bug clinic (Planned)

Proposed lessons

  • C07Kinds of program errorsPlanned
  • C08Boundary cases and test evidencePlanned

Big Idea 2: Data

DAT · 4 topics
Topic 2.1

Binary Numbers

Planned

Represent information with bits; explain analog/digital representation and binary values.

Objectives:
DAT-1.A, DAT-1.B, DAT-1.C
Course lab:
F04 Binary message studio (Planned)

Proposed lessons

  • C09Bits and representationsPlanned
  • C10Binary values and representation limitsPlanned
Topic 2.2

Data Compression

Planned

Compare lossless and lossy compression and choose a method for a purpose.

Objectives:
DAT-1.D
Course lab:
F05 Compression tradeoff (Planned)

Proposed lessons

  • C11Recovering compressed informationPlanned
  • C12Choosing a compression tradeoffPlanned
Topic 2.3

Extracting Information from Data

Planned

Explain data, metadata and how processing can reveal information and limitations.

Objectives:
DAT-2.A, DAT-2.B, DAT-2.C
Course lab:
F06 School transport data inquiry (Planned)

Proposed lessons

  • C13Data metadata and questionsPlanned
  • C14Patterns and limits of inferencePlanned
Topic 2.4

Using Programs with Data

Planned

Use programs to process data and explain challenges in large or inconsistent datasets.

Objectives:
DAT-2.D, DAT-2.E
Course lab:
F06 School transport data inquiry (Planned)

Proposed lessons

  • C15Cleaning and filtering dataPlanned
  • C16Combining datasets responsiblyPlanned

Big Idea 3: Algorithms and Programming

AAP · 18 topics
Topic 3.1

Variables and Assignments

Planned

Represent values with variables and determine assignment effects.

Objectives:
AAP-1.A, AAP-1.B
Course lab:
F07 Variable trace studio (Planned)

Proposed lessons

  • C17Variables and statePlanned
  • C18Tracing assignmentsPlanned
Topic 3.2

Data Abstraction

Planned

Represent collections with lists and explain how data abstraction manages complexity.

Objectives:
AAP-1.C, AAP-1.D
Course lab:
F13 Club list filter (Planned)

Proposed lessons

  • C19Representing a collectionPlanned
  • C20Explaining list abstractionPlanned
Topic 3.3

Mathematical Expressions

Planned

Represent algorithms and evaluate sequencing and arithmetic expressions.

Objectives:
AAP-2.A, AAP-2.B, AAP-2.C
Course lab:
F07 Variable trace studio (Planned)

Proposed lessons

  • C21Expressions and arithmeticPlanned
  • C22Sequencing an algorithmPlanned
Topic 3.4

Strings

Planned

Evaluate string operations using the behavior defined in a prompt.

Objectives:
AAP-2.D
Course lab:
F08 Text transformation (Planned)

Proposed lessons

  • C23Strings and text valuesPlanned
  • C24Reading specified string operationsPlanned
Topic 3.5

Boolean Expressions

Planned

Write and evaluate relational and logical expressions.

Objectives:
AAP-2.E, AAP-2.F
Course lab:
F09 Eligibility decision (Planned)

Proposed lessons

  • C25Comparisons and Boolean valuesPlanned
  • C26Combining conditionsPlanned
Topic 3.6

Conditionals

Planned

Represent selection and determine which statements run.

Objectives:
AAP-2.G, AAP-2.H
Course lab:
F09 Eligibility decision (Planned)

Proposed lessons

  • C27Choosing a branchPlanned
  • C28Conditions at the boundaryPlanned
Topic 3.7

Nested Conditionals

Planned

Write and evaluate nested selection.

Objectives:
AAP-2.I
Course lab:
F10 Nested route selector (Planned)

Proposed lessons

  • C29Decisions inside decisionsPlanned
  • C30Checking nested decision pathsPlanned
Topic 3.8

Iteration

Planned · sample touches

Represent repetition and determine its results and termination.

Objectives:
AAP-2.J, AAP-2.K
Course lab:
F11 Loop termination detective (Planned)

Proposed lessons

  • C31Counted repetitionPlanned
  • C32Conditional repetition and terminationPlanned

Available now: one sample activity uses ideas from this topic. It is not full topic coverage.

Topic 3.9

Developing Algorithms

Planned

Compare, combine and modify algorithms to achieve a purpose.

Objectives:
AAP-2.L, AAP-2.M
Course lab:
F12 Algorithm comparison (Planned)

Proposed lessons

  • C33Comparing algorithm behaviorPlanned
  • C34Building and combining algorithmsPlanned
Topic 3.10

Lists

Planned · sample touches

Use list operations and traverse collections to produce results.

Objectives:
AAP-2.N, AAP-2.O
Course lab:
F13 Club list filter (Planned)

Proposed lessons

  • C35List indices and operationsPlanned
  • C36Traversing and filtering listsPlanned

Available now: one sample activity uses ideas from this topic. It is not full topic coverage.

  • Sample labTraverses a list and counts elements that meet a condition.
Topic 3.11

Binary Search

Planned

Explain sorted-data requirements and determine search iterations.

Objectives:
AAP-2.P
Course lab:
F14 Binary search explorer (Planned)

Proposed lessons

  • C37Halving a sorted search spacePlanned
  • C38Counting search stepsPlanned
Topic 3.12

Calling Procedures

Planned

Use procedure calls, arguments and returned values.

Objectives:
AAP-3.A
Course lab:
F15 Procedure call tracer (Planned)

Proposed lessons

  • C39Arguments parameters and callsPlanned
  • C40Tracing returned valuesPlanned
Topic 3.13

Developing Procedures

Planned

Develop procedural abstractions and explain their effect on complexity.

Objectives:
AAP-3.B, AAP-3.C
Course lab:
F16 Reusable recommendation procedure (Planned)

Proposed lessons

  • C41Writing a useful procedurePlanned
  • C42Explaining procedural abstractionPlanned
Topic 3.14

Libraries

Planned

Use documented libraries while understanding their abstractions.

Objectives:
AAP-3.D
Course lab:
F17 Library documentation challenge (Planned)

Proposed lessons

  • C43Reading a library interfacePlanned
  • C44Using documented functionalityPlanned
Topic 3.15

Random Values

Planned

Use random values and reason about possible outputs.

Objectives:
AAP-3.E
Course lab:
F18 Random outcome explorer (Planned)

Proposed lessons

  • C45Random ranges and outcomesPlanned
  • C46Testing programs with randomnessPlanned
Topic 3.16

Simulations

Planned

Explain how simulations model phenomena and evaluate their limitations.

Objectives:
AAP-3.F
Course lab:
F19 Queue simulation (Planned)

Proposed lessons

  • C47Building a modelPlanned
  • C48Testing simulation assumptionsPlanned
Topic 3.17

Algorithmic Efficiency

Planned

Compare algorithm growth and reasonable versus unreasonable time.

Objectives:
AAP-4.A
Course lab:
F20 Efficiency comparison (Planned)

Proposed lessons

  • C49Counting operations as inputs growPlanned
  • C50Reasonable time and heuristic choicesPlanned
Topic 3.18

Undecidable Problems

Planned

Explain that some problems have no algorithm solving every instance.

Objectives:
AAP-4.B
Course lab:
F21 Decidability case sorting (Planned)

Proposed lessons

  • C51What algorithms can decidePlanned
  • C52Distinguishing hard from undecidablePlanned

Big Idea 4: Computer Systems and Networks

CSN · 3 topics
Topic 4.1

The Internet

Planned

Explain networks, protocols, data routing and Internet design.

Objectives:
CSN-1.A, CSN-1.B, CSN-1.C, CSN-1.D
Course lab:
F22 Packet journey (Planned)

Proposed lessons

  • C53Networks addresses and protocolsPlanned
  • C54Packet journeys and the WebPlanned
Topic 4.2

Fault Tolerance

Planned

Explain redundancy and evaluate system responses to failures.

Objectives:
CSN-1.E
Course lab:
F23 Network failure investigation (Planned)

Proposed lessons

  • C55Redundant pathsPlanned
  • C56Testing network resiliencePlanned
Topic 4.3

Parallel and Distributed Computing

Planned

Compare sequential, parallel and distributed work and evaluate speedup.

Objectives:
CSN-2.A, CSN-2.B
Course lab:
F24 Parallel task scheduler (Planned)

Proposed lessons

  • C57Dividing computational workPlanned
  • C58Speedup and sequential bottlenecksPlanned

Big Idea 5: Impact of Computing

IOC · 6 topics
Topic 5.1

Beneficial and Harmful Effects

Planned

Explain intended and unintended effects on different stakeholders.

Objectives:
IOC-1.A, IOC-1.B
Course lab:
F25 Innovation stakeholder inquiry (Planned)

Proposed lessons

  • C59Stakeholders and consequencesPlanned
  • C60Evaluating a computing innovationPlanned
Topic 5.2

Digital Divide

Planned

Explain differences in access and the effects of attempts to reduce them.

Objectives:
IOC-1.C
Course lab:
F26 Digital access audit (Planned)

Proposed lessons

  • C61Access skills and participationPlanned
  • C62Evaluating an access interventionPlanned
Topic 5.3

Computing Bias

Planned

Explain how data or design can introduce bias and assess mitigations.

Objectives:
IOC-1.D
Course lab:
F27 Recommendation bias investigation (Planned)

Proposed lessons

  • C63Bias in data and designPlanned
  • C64Testing a proposed mitigationPlanned
Topic 5.4

Crowdsourcing

Planned

Explain how broad participation supports solutions and creates tradeoffs.

Objectives:
IOC-1.E
Course lab:
F28 Crowdsourced accessibility map (Planned)

Proposed lessons

  • C65Combining contributionsPlanned
  • C66Quality and participation in crowdsourcingPlanned
Topic 5.5

Legal and Ethical Concerns

Planned

Evaluate computing uses through rights, permissions and ethical responsibilities.

Objectives:
IOC-1.F
Course lab:
F29 Permissions and attribution clinic (Planned)

Proposed lessons

  • C67Attribution permissions and reusePlanned
  • C68Evaluating an ethical tradeoffPlanned
Topic 5.6

Safe Computing

Planned

Explain data risks, protection and the consequences of collecting personal information.

Objectives:
IOC-2.A, IOC-2.B, IOC-2.C
Course lab:
F30 Privacy and safe computing decisions (Planned)

Proposed lessons

  • C69Personal data and attack scenariosPlanned
  • C70Choosing appropriate protectionsPlanned

Start the sample journey with the Designing a useful program lesson.