All Topics

Everything on EduCS.me in one place. Click any topic to jump straight in.


Computational Thinking

The thinking skills behind every solution – before you write any code.

Topic What you’ll learn
Problem Specification Problem statement, constraints, objectives, goals, inputs, outputs, evaluation criteria
CT Concepts Decomposition, pattern recognition, abstraction, algorithmic design
Flowcharts Standard symbols, tracing algorithms, drawing flowcharts from algorithms

Data Representation

How computers store numbers, text, images, and sound as binary.

Topic What you’ll learn
Number Systems Binary, hexadecimal, decimal conversions
Representing Data Encoding text, images, audio, video in binary

Hardware & Architecture

How computers work at the physical level – from logic gates to cloud services.

Topic What you’ll learn
CPU Architecture ALU, CU, registers, fetch-decode-execute cycle
GPU Graphics processing, CPU vs GPU comparison
Primary Memory RAM, ROM, cache (L1/L2/L3), registers
Secondary Storage SSD, HDD, optical, flash – internal and external
Logic Gates AND, OR, NOT, NAND, NOR, XOR, XNOR, truth tables, logic diagrams
Compression Lossy vs lossless, run-length encoding
Cloud Computing SaaS, PaaS, IaaS, deployment models
Translation Compilers vs interpreters, JIT, bytecode
Practice Library Every Hardware practice question, filtered by difficulty and marks

Programming Fundamentals

Core programming concepts – variables, strings, error handling, debugging.

Topic What you’ll learn
Variables & Types int, double, boolean, char, String – declaration and usage
Strings length, substring, indexOf, charAt, equals, compareTo
Exception Handling try/catch/finally, handling runtime errors
Debugging Trace tables, breakpoints, print debugging

Programming Constructs

Control flow – making decisions and repeating actions.

Topic What you’ll learn
Selection (if/else) if, else if, else, boolean conditions, nested selection
Iteration (Loops) for, while, do-while, nested loops
Methods & Modularization Parameters, return values, scope, why modularize

Algorithms

Solving problems efficiently – searching, sorting, and measuring performance.

Topic What you’ll learn
Big-O Notation O(1), O(log n), O(n), O(n^2) – comparing efficiency
Searching Algorithms Linear search, binary search – when to use each
Sorting Algorithms Bubble sort, selection sort – tracing and comparing
Recursion Base/recursive case, call stack, factorial, Fibonacci (HL)
Quicksort Pivot, partition, recursion tree, BST link, best/average/worst case, O(n log n) average vs O(n^2) worst (HL, explain-only)

Data Structures

Organizing and storing collections of data.

Topic What you’ll learn
1D Arrays Declaration, traversal, common operations, copying
2D Arrays Rows and columns, nested traversal, matrix operations
ArrayList Dynamic lists, add/remove/traverse, wrapper types, comparison with arrays
Stacks LIFO, push/pop/peek, undo, browser history, bracket matching
Queues FIFO, enqueue/dequeue, print queues, scheduling, task processing

Abstract Data Types (HL)

Building data structures from scratch – nodes, references, and the theory behind the tools.

Topic What you’ll learn
What Are ADTs? Interface vs implementation, static vs dynamic, core operations, ADT as a contract (HL)
Linked Lists Node class, singly linked list, traversal, insertion, deletion, search (HL)
Ordered Linked Lists Sorted insertion, early-termination search, ordered vs unordered trade-offs (HL)
Doubly Linked Lists prev + next references, bidirectional traversal, O(1) deletion, circular variant (HL)
Stacks from Scratch Building a stack with linked list nodes, push/pop internals, bracket matching (HL)
Queues from Scratch Building a queue with linked list nodes, enqueue/dequeue internals, FIFO applications (HL)
Binary Search Trees TreeNode, BST property, recursive insert and search, in-order traversal (HL)
BST Operations Delete (3 cases), tree height, balanced vs unbalanced, pre-order and post-order traversals (HL)
Sets and HashMaps HashSet, HashMap, hashing, collision resolution, load factor, set operations (HL)
Choosing the Right Structure Comparison table, decision flowchart, trade-offs, when to use each structure (HL)

File Processing

Reading from and writing to text files for data persistence.

Topic What you’ll learn
Reading Files Scanner, BufferedReader, parsing CSV, error handling
Writing Files FileWriter, BufferedWriter, overwrite vs append, CSV output

Object-Oriented Programming

Modelling real-world systems with classes, objects, and relationships.

Fundamentals

Topic What you’ll learn
What is OOP? Four principles, procedural vs OOP, why it exists
Classes & Objects Class as blueprint, instantiation with new
Constructors Default, parameterised, overloaded constructors
The this Keyword Parameter shadowing, this.field pattern
Encapsulation Private fields, getters/setters, data hiding
Access Modifiers private, public, protected, default
Static Members Static vs instance, shared counters, why main is static
Object References Aliasing, null, == vs .equals()
UML Class Diagrams Three-section box, symbols, drawing from code
Aggregation Has-a relationships, arrays of objects

Inheritance & Polymorphism (HL)

Topic What you’ll learn
Inheritance extends, super(), constructor chaining, is-a
Method Overriding @Override, replacing parent behaviour
Polymorphism Polymorphic arrays, runtime method dispatch
Abstract Classes abstract keyword, forcing subclass implementation
Interfaces interface, implements, multiple interfaces
Composition vs Inheritance Has-a vs is-a, favour composition

Exam Skills

Topic What you’ll learn
OOP Method Patterns Filter, count, accumulate, find-by-ID, max/min, remove
Selection Sort on Objects Sorting by attribute, .compareTo(), swapping references
OOP Terminology Reference Every IB OOP term with mark-scheme definitions for revision

IA Preparation

Topic What you’ll learn
JavaFX Basics Stage, Scene, controls, layouts, event handling, alerts
MVC Architecture Model-View-Controller, separation of concerns, complete example
FXML and Controllers FXML markup, @FXML annotation, FXMLLoader, Scene Builder
Multi-Window Apps Scene swapping, popup windows, shared model, data passing
File I/O in Apps CSV persistence, try-with-resources, load/save patterns
Structuring Your IA Class planning, examiner expectations, MVC checklist, incremental build

JavaFX Cookbook

Plug-and-play JavaFX recipes for building a desktop application. Each recipe is a small, generic pattern you adapt to your own project. JavaFX is not an examined topic; these pages are practical build tooling, useful for an IB IA or any Java GUI project.

Page What you’ll learn
Cookbook Overview What the cookbook is, how to use it, the full recipe list
Start here: how every recipe fits together The two places you edit, and the fx:id and onAction links. Read first
Controlling UI state Enable/disable controls, focus, reacting to Enter
Dialogs and feedback Information and confirmation dialogs, catching bad input
Input controls Radio buttons, checkboxes, combo boxes, date pickers, numbers-only fields
Showing data in a table TableView, filling it, reading the selected row, live filtering
Time, progress, and animation Timeline beats and progress bars
Files and persistence FileChooser, saving and loading text, stepping up to SQLite
Exporting and sharing CSV export, email from a button, opening a web page
App structure and navigation initialize, menus, quitting, second windows, passing data, tabs, CSS
Charts and graphs Line, bar, and pie charts, and updating them
Images and media Showing images, image buttons, sound
Drawing and animation Canvas drawing, mouse handlers, game loops
Keep the UI responsive during slow work Background threads and Task so the window does not freeze

Operating Systems

The software layer that manages hardware, processes, and resources.

Topic What you’ll learn
OS Fundamentals Role and functions of an operating system
Scheduling FCFS, round robin, priority, multilevel queue
Polling & Interrupts How the CPU handles device communication
Multitasking Context switching, deadlock, resource contention
Control Systems Sensors, actuators, feedback loops, real-world applications

Databases

The relational model, design, SQL, and (HL) alternative approaches.

Topic What you’ll learn
Databases Layer (overview) What the layer covers and how it fits together
Relational Database Fundamentals Tables, records, fields, keys; benefits, limitations; object-relational impedance mismatch
Database Schemas Conceptual, logical, physical schema layers
Entity-Relationship Diagrams ERDs with cardinality and modality, junction tables for M:M
Data Types and Keys INT, DECIMAL, VARCHAR, DATE, BOOLEAN; primary, foreign, composite, concatenated keys
Normalisation 1NF, 2NF, 3NF; functional/partial/transitive dependencies; constructing 3NF; denormalisation
SQL Queries DDL vs DML; SELECT/JOIN/WHERE/GROUP BY/HAVING/ORDER BY; aggregates (HL)
SQL Updates INSERT, UPDATE, DELETE; effect of indexed columns on performance
Transactions and Views (HL) ACID, BEGIN/COMMIT/ROLLBACK; virtual vs materialised views
Alternative Databases (HL) NoSQL, cloud, spatial, in-memory database approaches
Data Warehouses (HL) Warehouse objectives, OLAP, data mining techniques
Distributed Databases (HL) Partitioning, replication, transparency, fault tolerance, scalability
Practice Library Every Databases practice question, filtered by difficulty and marks

Networks & Security

How computers communicate, from local networks to global infrastructure.

Topic What you’ll learn
Network Fundamentals LAN, WAN, PAN, VPN, network devices
Protocols & Layers TCP, UDP, HTTP, HTTPS, DHCP, TCP/IP model
Network Architecture Topologies, client-server vs P2P, segmentation
Data Transmission IP addressing, packet switching, routing
Network Security Firewalls, vulnerabilities, countermeasures
Encryption Symmetric, asymmetric, digital certificates

Ethics & Implications

The social, ethical, and legal dimensions of computing. Both pages build the “Discuss” skill: weigh both sides and reach a reasoned conclusion.

Topic What you’ll learn
Ethics of Machine Learning Accountability, fairness, bias, consent, privacy, security, environmental and societal impact, transparency; training-data bias; online communication ethics (A4.4.1)
Computing in Daily Life Pervasive computing, surveillance, the digital divide, equity, emerging tech (quantum, AR/VR, pervasive AI), reassessing ethical guidelines (A4.4.2)

IB CS Internal Assessment (2027 syllabus)

Guidance for the coursework component – problem specification, planning, system overview, development, and evaluation. Aligned with first assessment May 2027.

Topic What you’ll learn
Internal Assessment Overview The five criteria, submission bundle, word counts, video length
Guidelines Language choice, researching alternatives, product suggestions
Criterion A: Problem specification Problem scenario, success criteria, computational context (4 marks)
Criterion B: Planning Decomposition and chronology (Gantt / Agile) (4 marks)
Criterion C: System overview System model, algorithms, testing strategy (6 marks)
Criterion D: Development Techniques, justification, video, testing (12 marks)
Criterion E: Evaluation Evaluate against success criteria, justify improvements (4 marks)
Templates IA Proposal Form (.docx) and Testing Strategy Template (.docx) downloads
Common mistakes (and how to avoid them) Pitfalls that pull marks down most often, grouped by criterion, with what to do instead

IB CS Extended Essay (2027 syllabus)

Guidance for the 4,000-word independent research essay. Topic selection, criteria, reflection, and the patterns examiners flag most often. Aligned with first assessment May 2027.

Topic What you’ll learn
Extended Essay Overview What the EE is, what you submit, the five criteria, EE vs IA
Topic Selection Topic test, RQ quality test, example questions, what to avoid
Research and Writing Sources, essay structure, word count rules, formatting, literature matrix
Criteria A–E in Detail Mark bands, CS-specific interpretation, EE+TOK points matrix
Reflection Three sessions, RRS, the 500-word reflective statement
Timeline Suggested year-long calendar with CS-specific milestones
Common Pitfalls The 10 patterns examiners flag most often, with fixes

IB CS Case Study (2027 syllabus)

The pre-seen scenario for Paper 1 Section B, sat by both SL and HL. The 2027 edition is Generative AI for image creation (Visionary Studios). Pages marked (HL) are Higher Level only.

Page What you’ll learn
Case Study Overview What the case study is, the yearly cycle, and how it is examined
2027: Generative AI The scenario, exam shape, and the SL/HL depth ladder
Image Generation Techniques Text-to-image, conditional, and unconditional generation
Diffusion Models Noise, denoising, DDPM, and the CNN denoiser
GANs (HL) Generator, discriminator, adversarial dynamic, mode collapse
Hybrid Models (HL) VAEs, flow-based models, latent space, and why to combine
Evaluating Models The five factors: quality, efficiency, stability, scalability, consistency
Ethics and Law Dataset curation and IP, bias and fairness, transparency and disclosure
Glossary Trainer Every term you must define and apply, SL and HL sets
Challenges and Exam Prep The four challenges, markbands, answer scaffold, and worked Section B questions

What’s on each page?

Every content page includes:

  • Key Concepts – clear explanations with precise terminology
  • Worked Examples – step-by-step solutions you can follow
  • Interactive Quizzes – MCQs, trace tables, code completion, bug spotting, output prediction
  • Practice Exercises – three levels: Core, Extension, Challenge
  • Glossary Tooltips – hover over highlighted terms to see a quick definition and a link to learn more
  • Connections – prerequisites and related topics to explore next

Pages marked (HL) are Higher Level only. Use the HL/SL toggle in the sidebar to show or hide HL content.


© EduCS.me — A resource hub for Computer Science education

This site uses Just the Docs, a documentation theme for Jekyll.