# IB DP Computer Science (New Syllabus) - 2-Year Program Sequence

## Course Overview & Framework

* **Course Duration:** 2 Years (Time-agnostic, percentage-based allocation mapping to the 150-hour SL framework)
* **Prerequisite:** None
* **Core Language:** Language-Agnostic Text Code Analysis (Python or Java)
* **Core Mandate:** Integrated Machine Learning Module & Relational Databases
* **Pedagogical Strategy:** Flipped Classroom (Asynchronous conceptual acquisition + In-class collaborative engineering)

## Assessment Architecture

* **Philosophy:** Understanding-first paradigm. Evaluation centers on architectural integrity, systemic logic, and systemic impacts over syntax memorization.
* **AI Policy:** Permitted and operationalized. AI tools are utilized as pair-programmers and design assistants during labs.
* **Internal Assessment (IA):** Computational Software Solution (Open internet + full documentation allowed; capped at 2,000 words + 5-minute functionality video).

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## YEAR 1: Core Foundations & Coding Fundamentals

### Module 1: System Fundamentals & Hardware Architecture

* **Time Allocation:** 15% of total course hours
* **IB Syllabus Alignment:** Theme A1 — Computer Fundamentals (CPU Architecture, System Components, Binary/Gray Code representations, Logic Gates, and Systemic Translation).
* **Assessment Framework:** A System Analysis & Impact Report.
* **Flipped Assignment (Home):** Watch architecture & binary logic lectures; complete algorithmic translation practice problems.
* **Active Learning (In Class):** Hands-on Lab: Map out CPU execution paths and analyze legacy hardware performance trade-offs using AI design assistants.

### Module 2: Introduction to Programming & Text Code Analysis

* **Time Allocation:** 20% of total course hours
* **IB Syllabus Alignment:** Theme B1 — Computational Thinking & Theme B2 — Programming Elements (Sequential logic, variables, conditional branches, loops, and 1D arrays).
* **Assessment Framework:** Core Algorithmic Proficiency Exam (conceptual structural mapping; zero syntax penalties).
* **Flipped Assignment (Home):** Watch text-code trace mechanics lectures; execute manual trace tables for syntax-free flowcharts.
* **Active Learning (In Class):** Hands-on Lab: Use AI pair-programmers to debug logic errors and generate multi-layered array architectures.

### Module 3: Computer Networks & Digital Infrastructure

* **Time Allocation:** 15% of total course hours
* **IB Syllabus Alignment:** Theme A2 — Computer Networks (Network architecture, topologies, data transmission mechanics, packet switching, and digital/cloud infrastructures).
* **Assessment Framework:** A Network Design & Security Case Study.
* **Flipped Assignment (Home):** Watch network models and IP addressing lectures; resolve simulated packet routing problems.
* **Active Learning (In Class):** Hands-on Lab: Model a secure cloud subnet and audit protocol vulnerabilities using AI network simulation tools.

### Module 4: Relational Databases & Schema Design

* **Time Allocation:** 10% of total course hours
* **IB Syllabus Alignment:** Theme A3 — Databases (Core relational database fundamentals, normalization metrics, schemas, and ERDs).
* **Assessment Framework:** Database Design Blueprint & SQL Query Set.
* **Flipped Assignment (Home):** Watch data integrity and normalization lectures; construct logical Entity-Relationship Diagrams (ERDs).
* **Active Learning (In Class):** Hands-on Lab: Generate schema normalization transformations and execute complex AI-assisted SQL query testing against multi-table data sheets.

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## YEAR 2: Advanced Implementation & Examinations

### Module 5: Machine Learning & Intelligent Systems

* **Time Allocation:** 15% of total course hours
* **IB Syllabus Alignment:** Theme A4 — Machine Learning (Mandatory Component: AI definitions, Supervised, Unsupervised, Reinforcement, Deep Learning models, and socio-ethical impacts).
* **Assessment Framework:** A Machine Learning Model & Bias Evaluation Project.
* **Flipped Assignment (Home):** Watch neural network and training data dynamics lectures; resolve classification scenario analysis matrices.
* **Active Learning (In Class):** Hands-on Lab: Train, cross-validate, and evaluate an ML algorithm using AI frameworks, auditing it explicitly for training data skew.

### Module 6: Object-Oriented Programming (OOP)

* **Time Allocation:** 10% of total course hours
* **IB Syllabus Alignment:** Theme B2/B3 — Advanced Programming (Object-Oriented Programming structures: classes, instantiation, encapsulation, and inheritance).
* **Assessment Framework:** OOP Modular Architecture Map.
* **Flipped Assignment (Home):** Watch class-design structure videos; sketch object interaction maps for structural abstraction.
* **Active Learning (In Class):** Hands-on Lab: Refactor linear codebases into clean, modular, object-oriented structures with AI blueprint validation.

### Module 7: The Internal Assessment (IA) Computational Solution

* **Time Allocation:** 15% of total course hours (Mandatory in-class hours allocation)
* **IB Syllabus Alignment:** Computational Solution Project (Mandatory SL/HL internal assessment component).
* **Assessment Framework:** The Final IA Software Project (Technical documentation artifact + functional video submission).
* **Flipped Assignment (Home):** Record asynchronous system component updates; write user-criteria documentation sections.
* **Active Learning (In Class):** Hands-on Lab: Scope, prototype, and build your production codebase using open-internet documentation, using AI solely as an architectural consultant.

### Module 8: IB Pre-Released Case Study & Examination Prep

* **Time Allocation:** Flexible Integration (Utilizes remaining flexible term days / integrated throughout Year 2)
* **IB Syllabus Alignment:** Official Pre-Released Case Study Dossier (Paper 1, Part B) & Multi-Theme Review.
* **Assessment Framework:** Mock Paper 1 and Paper 2 Mock Examinations under understanding-first conditions.
* **Flipped Assignment (Home):** Deconstruct the official IB Case Study dossier; review comprehensive system vocabulary flashcards.
* **Active Learning (In Class):** Hands-on Lab: Simulate actual Paper 1 (Theme A + Case Study) and Paper 2 (Theme B coding analysis) exam papers under systemic, understanding-first conditions.