International Baccalaureate · IB Diploma

Computer Science · HL

Papers, samples and curriculum documents for this course.

Handouts, exercise sheets and slides

Shared topic documents retain their source course and topic titles. Use your chosen board’s specification for coverage, tier and exam requirements.

Handouts · A-Level Computer Science (20)

Exercise sheets · A-Level Computer Science (85)

Presentation slides · A-Level Computer Science (20)

These lessons teach selected course objectives. Check the remaining coverage gaps; the material is not a complete preparation programme.

A.1 · Computer fundamentals

Checking an asserted identity.
Separate authentication from authorization. Authentication checks identity; authorization determines permitted actions. A threat model connects a valuable asset, a possible attack and an appropriate control.
Use a fictitious school dataset to define users and permissions. Draw data flows, compare validation and verification, and specify tests for normal, boundary and invalid input. Do not use real credentials or student records in exercises.

A.2 · Networks

Achieved rate of useful data transfer.
Transmission time depends on data size and rate. Total delay may also include propagation, processing and queueing. Encryption protects content under its assumptions but does not remove congestion or every metadata exposure.
Trace a message route using a documented local model. Record payload size, units and measured time. Use school-approved networks and synthetic messages; do not scan or intercept another user traffic.

A.3 · Databases

Attribute set uniquely identifying a table row.
A join combines rows according to a specified condition. The result count depends on relationship cardinality and filters. A foreign key constraint enforces a relationship rule; it does not automatically encrypt personal data.
Design a small synthetic user-order dataset with no real personal information. Declare keys, test duplicate and missing-reference inserts, then check a query result against a hand-worked expected table.

A.4 · Machine learning

An attribute set uniquely identifying a record.
A primary key uniquely identifies a record; a foreign key relates records. In prediction, leakage can expose information unavailable at the real decision time. Accuracy alone may hide an unbalanced target distribution.
Use fictional booking records to identify entities, attributes and relationships. For a learning exercise, use a public non-sensitive dataset, separate training and test data, and describe who may be affected by errors. The current 2027 CS objective scope awaits the acquired guide.

primary key: An attribute set uniquely identifying a record
data leakage: Use of information unavailable at the intended prediction time

B.1 · Computational thinking

A finite procedure solving a stated task.
State input conditions and expected outputs. Use boundary cases, empty collections where allowed, duplicates and invalid values. Distinguish a wrong algorithm from a wrong implementation or an incomplete requirement.
Trace a search over a small fictional sorted list. State the indexing convention. For binary search, update bounds so the remaining interval shrinks and reject unsorted input unless sorting is part of the task.

B.2 · Programming

A finite procedure solving a stated task.
State input conditions and expected outputs. Use boundary cases, empty collections where allowed, duplicates and invalid values. Distinguish a wrong algorithm from a wrong implementation or an incomplete requirement.
Trace a search over a small fictional sorted list. State the indexing convention. For binary search, update bounds so the remaining interval shrinks and reject unsorted input unless sorting is part of the task.

B.3 · Object-oriented programming

Controlling access to state through an interface.
A method call acts on a particular instance. State changes should satisfy preconditions and postconditions. Polymorphism lets code use a common interface with different implementations when the contract is respected.
Implement a small synthetic account or inventory model. Test two independent instances, rejected invalid operations and boundary values. Keep the model away from real financial accounts and credentials.

B.4 · Abstract data types (HL only)

A last-in-first-out abstract data type.
Choose the structure for the required access pattern. Complexity depends on implementation and assumptions: removing the first item from a shifting array differs from advancing a head pointer in a queue.
Trace operation sequences by hand, then compare a program with the expected states. Test empty, singleton and repeated operations. State overflow behaviour if capacity is fixed.

Case study · Case study

Completed work per unit time under a stated workload.
A recommendation should connect a requirement to a mechanism and an observable test. For example, a concurrency problem requires a strategy that prevents conflicting updates, plus tests demonstrating the invariant. A claim about faster response requires comparable workload measurements rather than only a complexity label. Evaluate alternatives under the same stated conditions.
Use an original school-approved scenario and synthetic data. Build a matrix of claim, supporting scenario evidence, technical explanation, limitation and acceptance test. Mark missing facts explicitly and show how the recommendation would change if an assumption failed. This prepares case-based reasoning without reproducing an unavailable IB assessment case study or inventing its examination rubric.

IA · Computational solution

A finite procedure solving a stated task.
State input conditions and expected outputs. Use boundary cases, empty collections where allowed, duplicates and invalid values. Distinguish a wrong algorithm from a wrong implementation or an incomplete requirement.
Trace a search over a small fictional sorted list. State the indexing convention. For binary search, update bounds so the remaining interval shrinks and reject unsorted input unless sorting is part of the task.

This ordering records the 2027 A/B structure separately from the 2014 course final assessment in 2026.
Do not carry forward old Options A–D or the old assessment weights. A.4 machine learning and B.3 OOP are in the new public structure; B.4 is HL-only.
Current paper marks, case-study assessment differences, approved language conventions and IA rubric remain blocked pending the current guide.
The project is a school-supervised working computational solution with requirements, tests and user evaluation, never an invented written replacement.