IB Computer Science Tutor Online

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Personalized IB Computer Science Support

Every Computer Science student has a different combination of strengths and difficulties.

Some students are confident programmers but need help with computer systems, networks or databases. Others understand the theoretical content but struggle to translate a problem into an algorithm.

A student may also be comfortable completing classroom exercises but find it difficult to work independently on a complex Internal Assessment project.

Lessons can be adapted to:

• Standard Level or Higher Level
• The curriculum followed by the student’s school
• The student’s final assessment session
• Python or Java
• Current school topics
• Programming experience
• Upcoming tests and mock examinations
• Paper 1 and Paper 2 preparation
• The legacy HL Paper 3
• Case-study preparation
• Internal Assessment progress
• Predicted-grade objectives
• Areas identified by the schoolteacher
• Available preparation time

Students may receive regular weekly support or focused assistance with a specific topic, examination or assessment deadline.

Why IB Computer Science Can Be Challenging

IB Computer Science brings together several different types of learning.

Students are expected to:

• Recall accurate technical knowledge
• Understand how computing systems operate
• Analyze unfamiliar situations
• Decompose complex problems
• Develop algorithms
• Trace and evaluate code
• Write programs
• Work with data structures
• Interpret diagrams and models
• Test computational solutions
• Identify limitations
• Discuss ethical and social implications
• Communicate technical ideas clearly
• Manage a substantial independent project

A student can be strong in one area and still find another part of the course difficult.

For example, someone who enjoys programming may lose marks on written explanations because the response does not use precise terminology. Conversely, a student who performs well on theoretical questions may struggle to design an efficient computational solution.

Our tutors help students connect these different areas rather than studying them as isolated topics.

Support for IB Computer Science SL and HL

IB Computer Science is available at Standard Level and Higher Level.

The IB recommends 150 teaching hours for SL and 240 for HL. Higher Level students study the subject in greater breadth and depth and face additional content and assessment demands.

Support is adapted according to:

• The student’s course level
• The depth of knowledge required
• The complexity of programming questions
• The topics included in the course
• The assessment model
• The level of independence expected
• The student’s existing programming skills
• The time remaining before assessment

SL students may need support building a reliable understanding of the main concepts and applying computational thinking.

HL students may also require help with more complex programming, object-oriented design, abstract data types, advanced problem-solving and deeper analysis.

Support for the 2026 and 2027 Computer Science Courses

IB Computer Science is currently transitioning between two curricula.

Students taking their final examination in 2026 follow the previous course. Students assessed from 2027 follow the revised Computer Science curriculum. The revised course introduces a new content structure, includes machine learning and permits students to answer programming questions in either Python or Java.

The assessment models are also different.

Therefore, before lessons begin, it is important to establish:

• The student’s examination session
• The course guide used by the school
• Whether the student follows the 2026 or 2027 model
• The programming language used in class
• Which Papers the student will take
• Which version of the Internal Assessment applies
• Whether a separate HL Paper 3 is required

This prevents students from preparing with outdated topics, assessment structures or practice materials.

The Revised IB Computer Science Course: First Assessment 2027

The revised course is organized into two principal themes:

Theme A: Concepts in Computer Science

• Computer fundamentals
• Networks
• Databases
• Machine learning

Theme B: Computational Thinking and Problem-Solving

• Computational thinking
• Programming
• Object-oriented programming
• Abstract data types for HL students

The course also includes a case study, the computational solution completed as the Internal Assessment and the collaborative sciences project. Students can study the programming content in Python or Java.

Assessment from 2027

For students assessed under the revised course, the external assessment consists of Paper 1 and Paper 2.

Standard Level

• Paper 1: 1 hour 15 minutes, worth 35%
• Paper 2: 1 hour 15 minutes, worth 35%
• Computational solution: worth 30%

Higher Level

• Paper 1: 2 hours, worth 40%
• Paper 2: 2 hours, worth 40%
• Computational solution: worth 20%

Paper 1 focuses on the four topics in Theme A and also contains three questions connected to the case study. Paper 2 focuses on computational thinking, programming and object-oriented programming, with additional HL questions on abstract data types. Programming questions are available in equivalent Python and Java versions.

Unlike the previous HL course, the revised assessment model does not include a separate Paper 3. Instead, case-study questions are incorporated into Paper 1.

The Previous IB Computer Science Course: Last Assessment 2026

Students completing the previous course in 2026 follow a different syllabus and assessment structure.

The common SL and HL syllabus includes:

• System fundamentals
• Computer organization
• Networks
• Computational thinking
• Problem-solving
• Programming

HL students also study:

• Abstract data structures
• Resource management
• Control

Students study one option selected by the school:

• Databases
• Modelling and simulation
• Web science
• Object-oriented programming

The previous HL course also includes an annually issued case study assessed through Paper 3.

Legacy Standard Level assessment

• Paper 1: 45%
• Paper 2: 25%
• Internal Assessment solution: 30%

Legacy Higher Level assessment

• Paper 1: 40%
• Paper 2: 20%
• Paper 3: 20%
• Internal Assessment solution: 20%

Students following this course need practice materials and tutoring that correspond specifically to the legacy assessment model.

Meet Some of Our IB Computer Science Educators

Our network includes experienced IB teachers, examiners, coordinators, and subject specialists. Below are examples of educators who support Physics students within the IB Diploma Programme.

These profiles represent examples of educators within our network. Tutor matching depends on subject requirements, language preferences, availability, and student needs.

Computational Thinking and Problem-Solving

Computational thinking lies at the centre of IB Computer Science.

It involves understanding a problem, separating it into manageable parts, identifying the essential information, designing a logical solution and evaluating whether that solution is effective.

Students learn to:

• Define a problem clearly
• Identify inputs, processes and outputs
• Establish success criteria
• Decompose a complex problem
• Recognize patterns
• Use abstraction
• Develop an algorithm
• Test the proposed solution
• Identify errors and limitations
• Improve efficiency
• Generalize a solution to similar problems

The revised IB course specifically emphasizes problem specification, decomposition, abstraction, algorithmic thinking, testing and evaluation.

A tutor can help the student understand not only what the final algorithm should do, but also how to develop it systematically.

Algorithm Development

Many students understand an algorithm after seeing the completed solution but find it difficult to create one independently.

Support may include:

• Translating a written problem into logical steps
• Identifying the required data
• Selecting variables and data structures
• Using sequence, selection and iteration
• Developing modular solutions
• Writing functions and procedures
• Creating flowcharts
• Writing pseudocode
• Tracing algorithms manually
• Testing edge cases
• Comparing alternative algorithms
• Evaluating efficiency
• Identifying logical errors

Students learn to explain why an algorithm works rather than simply reproduce code from memory.

Pseudocode and Algorithm Tracing

Examination questions may ask students to interpret, trace, complete or develop an algorithm.

Common difficulties include:

• Following nested loops
• Tracking changes in variables
• Understanding array indexes
• Recognizing termination conditions
• Tracing recursive processes
• Distinguishing assignment from comparison
• Identifying off-by-one errors
• Predicting output
• Interpreting Boolean expressions
• Explaining the purpose of an algorithm

Tutors can help students develop a consistent tracing method using tables, diagrams and carefully selected examples.

With practice, students become better able to work accurately under examination conditions.

Programming Support in Python and Java

The revised Computer Science course can be studied using Python or Java. Programming is required for some Paper 2 questions, and students answer the version corresponding to the language they have studied.

Support can include:

• Variables and data types
• Input and output
• Operators and expressions
• Conditional statements
• Loops
• Strings
• Arrays and lists
• Functions and methods
• Parameters and return values
• File processing
• Validation
• Error handling
• Testing and debugging
• Object-oriented programming
• Program structure
• Code readability
• Documentation

Lessons focus on understanding and problem-solving rather than memorizing syntax.

Students may work through short exercises before progressing to more complex programs and examination-style questions.

Learning Python for IB Computer Science

Python is often accessible to students because its syntax is relatively concise.

However, students still need to understand the logic behind the program.

Support can cover:

• Data types
• Lists
• Dictionaries
• Loops
• Functions
• Classes and objects
• File handling
• Searching and sorting
• Recursion
• Testing
• Debugging

A tutor can also help students recognize the difference between writing code that works for one example and developing a solution that works reliably for a full range of valid inputs.

Learning Java for IB Computer Science

Java requires students to work carefully with types, methods, classes and object-oriented structures.

Students may need help with:

• Primitive and reference types
• Variables and constants
• Conditional structures
• Iteration
• Arrays
• Array lists
• Methods
• Classes and objects
• Constructors
• Encapsulation
• Inheritance
• Polymorphism
• Exception handling
• Recursion
• Testing and debugging

Support can also help students read unfamiliar Java code and explain its behaviour, which is different from simply writing programs from scratch.

Object-Oriented Programming

Object-oriented programming is part of the revised course for both SL and HL, with greater depth and associated demands at HL.

Students may need to understand:

• Classes
• Objects
• Attributes
• Methods
• Constructors
• Encapsulation
• Inheritance
• Polymorphism
• Associations between classes
• Object interactions
• Abstraction
• Reusability
• Advantages and limitations of OOP

Tutors help students connect diagrams and theoretical definitions to actual code.

For example, a student may understand what inheritance means but still struggle to decide whether it is appropriate within a specific program.

Abstract Data Types for HL

Abstract data types are included as HL-only content in the revised course.

Support may include:

• Stacks
• Queues
• Linked lists
• Trees
• Binary search trees
• Graph structures
• Operations performed on each structure
• Appropriate applications
• Traversal
• Searching
• Insertion and deletion
• Advantages and limitations
• Implementation considerations

Students need to understand both how a structure operates and why it may be more suitable than another structure for a particular problem.

Searching and Sorting Algorithms

Searching and sorting questions require students to understand the steps of an algorithm and evaluate its suitability.

Tutoring may cover:

• Linear search
• Binary search
• Bubble sort
• Selection sort
• Insertion sort
• Recursive approaches
• Preconditions
• Efficiency
• Best- and worst-case situations
• Advantages and disadvantages
• Manual tracing
• Implementation in Python or Java

Students learn that choosing an algorithm depends on the organization of the data, the size of the dataset and the requirements of the problem.

Computer Fundamentals

Computer fundamentals form part of Theme A in the revised course.

Students may need support with:

• Data representation
• Binary and hexadecimal
• Boolean logic
• Logic gates
• Computer architecture
• Processors
• Memory
• Storage
• Operating systems
• System software
• Translators
• Data compression
• Encryption
• Computer performance
• Hardware and software interaction

Tutors can help students move between definitions, diagrams, calculations and applied examination questions.

Binary, Hexadecimal and Data Representation

Number systems often appear straightforward until students need to apply them in unfamiliar situations.

Support may include:

• Binary-to-decimal conversion
• Decimal-to-binary conversion
• Hexadecimal conversion
• Binary arithmetic
• Bit and byte calculations
• Character encoding
• Image representation
• Sound representation
• Data storage
• Precision and range
• Compression
• Overflow

Students learn to show each stage clearly and explain the consequences of using different representations.

Boolean Logic and Logic Gates

Students may need to interpret and create:

• Truth tables
• Boolean expressions
• AND, OR and NOT gates
• NAND and NOR gates
• XOR gates
• Combined logic circuits
• Equivalent expressions
• Real-world control conditions

Tutoring can help students develop a systematic approach to evaluating complex expressions and checking whether a circuit matches the required behaviour.

Networks

Networks form part of both the previous and revised courses.

Support may include:

• Network types
• Network topologies
• Protocols
• Packets
• Routing
• IP addressing
• Domain names
• Client-server systems
• Peer-to-peer networks
• Wireless communication
• Network security
• Encryption
• Authentication
• Firewalls
• Reliability
• Performance
• Ethical and social implications

Students should be able to explain how a network functions and evaluate the suitability of different solutions in a particular context.

Databases

Databases are a central topic in the revised course and were also available as an option in the previous course.

Students may need support with:

• Database purpose
• Tables and records
• Fields and attributes
• Primary keys
• Foreign keys
• Relationships
• Entity-relationship diagrams
• Normalization
• Data redundancy
• Data integrity
• Queries
• SQL concepts
• Transactions
• Security
• Access control
• Distributed databases
• Advantages and limitations

Tutors help students connect database design principles to realistic organizational needs.

Machine Learning

Machine learning is explicitly included in the revised Computer Science curriculum.

Students may explore:

• The purpose of machine learning
• Training data
• Features
• Models
• Classification
• Prediction
• Supervised and unsupervised approaches
• Testing
• Accuracy
• Bias
• Overfitting
• Limitations
• Ethical considerations
• Social consequences
• Appropriate and inappropriate applications

Students do not benefit from memorizing fashionable terminology without understanding the underlying process.

Tutoring helps them explain how a model learns from data, how its performance may be evaluated and why the quality of the data matters.

Ethical, Social and Environmental Issues

Computer Science is not assessed only as a technical discipline.

The course also develops awareness of the environmental, economic, cultural, social and ethical effects of computing and emerging technologies.

Students may need to discuss:

• Data privacy
• Surveillance
• Cybersecurity
• Algorithmic bias
• Artificial intelligence
• Employment and automation
• Intellectual property
• Digital inequality
• Accessibility
• Environmental costs
• Energy consumption
• Electronic waste
• Ownership of data
• Responsibility for automated decisions
• Reliability of computer systems

Strong responses go beyond listing advantages and disadvantages.

Students should identify affected stakeholders, consider different perspectives, use relevant technical knowledge and reach a reasoned judgment.

Case-Study Preparation

Case-study preparation differs according to the student’s examination session.

Under the legacy 2026 HL course, the annually issued case study is assessed through a separate Paper 3. Under the revised 2027 course, case-study questions are included in Paper 1 for both SL and HL.

Effective case-study preparation may involve:

• Reading the material carefully
• Identifying unfamiliar terminology
• Researching the technical context
• Understanding the systems involved
• Identifying stakeholders
• Considering ethical implications
• Predicting potential problems
• Connecting the case to syllabus topics
• Practising possible questions
• Developing precise technical explanations
• Evaluating alternative solutions
• Working under timed conditions

The objective is not to memorize a prepared essay.

Instead, students develop sufficient knowledge and flexibility to respond to the actual questions.

Paper 1 Preparation

Paper 1 under the revised course focuses on Concepts in Computer Science and includes questions connected to the case study.

Support may include:

• Computer fundamentals
• Networks
• Databases
• Machine learning
• Case-study terminology
• Technical explanations
• Data interpretation
• Ethical evaluation
• Command terms
• Short-answer technique
• Extended-response planning
• Time management

Students practise writing precise responses that correspond to the command term and the available marks.

Paper 2 Preparation

Paper 2 focuses on Computational Thinking and Problem-Solving.

Students may need to:

• Trace algorithms
• Complete algorithms
• Design solutions
• Interpret code
• Write program fragments
• Work with object-oriented programming
• Compare approaches
• Test solutions
• Identify errors
• Evaluate efficiency
• Use abstract data types at HL

In the revised course, students answer programming questions in Python or Java according to the language studied.

Tutoring can combine conceptual review, short programming exercises and timed examination practice.

Legacy HL Paper 3 Preparation

Students completing HL Computer Science in 2026 still take the separate Paper 3 based on the pre-seen case study.

Support may include:

• Researching the case-study technology
• Creating a terminology list
• Identifying syllabus connections
• Understanding relevant systems
• Developing likely technical explanations
• Considering advantages and limitations
• Examining stakeholder effects
• Practising structured Paper 3 questions
• Improving the use of case-study evidence
• Managing the one-hour examination

The preparation must follow the legacy course rather than the new 2027 assessment model.

Understanding Command Terms

Students can know the correct content and still lose marks by responding at the wrong level of depth.

Support may include command terms such as:

• State
• Identify
• Define
• Outline
• Describe
• Explain
• Construct
• Trace
• Distinguish
• Compare
• Analyze
• Discuss
• Evaluate
• Suggest

For example, a question asking the student to state a fact requires a concise response. An explain question requires a developed technical relationship. An evaluate question requires evidence, advantages, limitations and a reasoned conclusion.

Students learn to adapt the length and structure of the response to the command term and mark allocation.

Internal Assessment: The Computational Solution

In the revised course, the Internal Assessment is called the computational solution.

Students develop a computational solution to a real-world problem of their own choosing using concepts, skills and tools from the course and the computational-thinking process. It accounts for 30% of the SL grade and 20% of the HL grade.

Tutoring may support students with:

• Identifying a suitable real-world problem
• Defining the problem precisely
• Establishing system requirements
• Developing success criteria
• Planning the solution
• Decomposing the problem
• Selecting appropriate data structures
• Designing algorithms
• Organizing the program
• Testing individual components
• Developing a testing strategy
• Evaluating the completed solution
• Identifying limitations
• Planning realistic improvements
• Managing the project timeline

The student remains responsible for all design decisions, programming, testing, evaluation and submitted work.

Selecting an Appropriate IA Problem

An appropriate project should be challenging enough to demonstrate computational thinking but realistic enough to complete within the available time.

Common problems include choosing a project that is:

• Too broad
• Too technically ambitious
• Dependent on unfamiliar external systems
• Difficult to test
• Poorly connected to a real user need
• Based on unclear success criteria
• Too simple to demonstrate sufficient development
• Likely to create privacy or security concerns
• Impossible to finish before the school deadline

A tutor can help the student examine the feasibility of the idea without selecting or completing the project for them.

Defining Requirements and Success Criteria

A computational solution needs a clear purpose.

Students should understand:

• Who will use the solution
• What problem it addresses
• What functions are essential
• What data is required
• What constraints exist
• How success will be measured
• Which requirements are priorities
• How the completed system can be tested

Vague criteria such as “the program should be easy to use” are difficult to evaluate.

Tutoring can help students develop more precise and testable criteria while maintaining full ownership of the project.

Planning and Designing the Solution

Before writing large amounts of code, the student should plan the structure of the solution.

Planning may involve:

• Breaking the project into modules
• Defining inputs and outputs
• Designing data structures
• Creating flowcharts or diagrams
• Writing pseudocode
• Planning classes and objects
• Designing the user interface
• Considering validation
• Identifying security requirements
• Planning file or database structures
• Establishing development milestones

Good planning reduces the risk of producing disconnected code that becomes difficult to test or maintain.

Testing and Debugging

Testing should demonstrate more than running the finished program once.

Students may need to consider:

• Normal data
• Boundary values
• Invalid input
• Empty input
• Unexpected user behaviour
• Individual module testing
• Integration testing
• Expected results
• Actual results
• Errors identified
• Corrections made
• Retesting
• Unresolved limitations

Tutors help students understand how to design meaningful tests and document the reasoning behind them.

They do not fabricate test results or complete the testing process for the student.

Evaluating the Computational Solution

A strong evaluation examines how successfully the solution addresses the original problem and success criteria.

Students may need to consider:

• Which requirements were met
• Which requirements were only partially met
• Evidence from testing
• User feedback where appropriate
• Technical strengths
• Usability
• Efficiency
• Reliability
• Security
• Limitations
• Realistic future improvements

An evaluation should not simply state that the program works.

It should use evidence to explain what works, what remains limited and how the solution could be improved.

Legacy 2026 Internal Assessment Support

Students assessed in 2026 complete the previous Internal Assessment solution, which involves developing a product and associated documentation.

The legacy SL subject brief describes a computational product supported by documentation of up to 2,000 words and expects evidence of independent research and investigation.

Support may include:

• Understanding the legacy criteria
• Defining the client’s problem
• Developing success criteria
• Designing the product
• Planning implementation
• Testing the completed product
• Evaluating it against the criteria
• Organizing the documentation
• Managing files and evidence
• Reviewing whether sections are complete

Students must work with the requirements provided by their school for the 2026 session rather than using the revised computational-solution model.

Academic Integrity and Responsible Tutoring

Computer Science tutoring should help students become more capable and independent.

Tutors can:

• Explain difficult concepts
• Demonstrate similar examples
• Ask questions that guide problem-solving
• Review algorithms
• Identify logical errors
• Discuss testing methods
• Explain assessment expectations
• Provide feedback on clarity and structure
• Help students plan their time

Tutors do not:

• Write the student’s IA code
• Design the entire solution
• Produce submitted documentation
• Invent testing evidence
• Rewrite evaluated work
• Provide undisclosed third-party code
• Complete examination assignments for the student

All submitted work must remain the student’s own.

Reviewing and Debugging Code Constructively

When a program does not work, simply replacing the code does not help the student develop.

A constructive review process considers:

• What the program is intended to do
• Where the observed behaviour changes
• Which input produces the problem
• Whether the logic or syntax is responsible
• How the problem can be isolated
• What test could confirm the cause
• Whether the correction creates another issue
• What the student learned from the error

The objective is to build a debugging method the student can use independently.

Developing Precise Technical Explanations

Students often understand a concept informally but cannot express it with sufficient technical accuracy.

For example, they may know that encryption “protects data” but fail to explain:

• What transformation occurs
• Who can access the information
• What role a key plays
• What threat is being reduced
• What limitations remain

Tutoring helps students replace vague language with accurate explanations while keeping answers proportionate to the number of marks available.

Exam Practice and Feedback

Regular practice helps students become familiar with the style, timing and reasoning required in IB Computer Science assessments.

Practice may include:

• Topic-specific questions
• Algorithm tracing
• Code interpretation
• Programming questions
• Network scenarios
• Database questions
• Machine-learning questions
• Ethical evaluations
• Case-study questions
• Timed Paper 1 sections
• Timed Paper 2 sections
• Legacy Paper 3 practice
• Mock examinations

Afterwards, the tutor can help the student review:

• Whether the command term was addressed
• Whether technical terminology was accurate
• Whether the answer was sufficiently developed
• Whether the algorithm was correct
• Whether working was shown clearly
• Whether the code handled all required cases
• Whether evaluation was balanced
• Where time was lost
• Which mistakes recur
• What should be practised next

Building an Effective Revision Plan

Computer Science contains both theoretical and practical material, so revision should not consist only of reading notes.

A balanced plan may include:

• Reviewing key concepts
• Creating terminology summaries
• Tracing algorithms
• Writing pseudocode
• Completing short programming exercises
• Revisiting weak topics
• Practising calculations and conversions
• Reviewing diagrams
• Answering case-study questions
• Completing timed examination sections
• Reviewing mistakes
• Testing recall without notes

A tutor can help establish realistic priorities based on the student’s remaining time and current performance.

What Students Can Work Toward

The results of tutoring depend on the student’s starting point, commitment, available time and school context. No particular grade can be guaranteed.

However, students may work toward outcomes such as:

• Understanding the main syllabus topics more clearly
• Writing more precise technical explanations
• Developing algorithms independently
• Tracing code more accurately
• Improving Python or Java programming skills
• Selecting appropriate data structures
• Solving unfamiliar problems more systematically
• Applying theory to case-study situations
• Improving examination time management
• Producing a more realistic IA plan
• Designing stronger success criteria
• Testing solutions more thoroughly
• Evaluating programs using evidence
• Reducing recurring coding errors
• Becoming more independent when debugging

Who Can Benefit from IB Computer Science Tutoring?

Tutoring may be particularly useful for students who:

• Study IB Computer Science SL or HL
• Are following the 2026 legacy course
• Are following the revised course assessed from 2027
• Need help with Python
• Need help with Java
• Struggle with algorithms or pseudocode
• Need support with object-oriented programming
• Find data structures challenging
• Need help with computer fundamentals
• Struggle with networks or databases
• Need support with machine learning
• Are preparing for Paper 1
• Are preparing for Paper 2
• Must take the legacy HL Paper 3
• Need help preparing for the case study
• Are developing the Internal Assessment
• Need help testing or evaluating a solution
• Want to improve a predicted grade
• Are preparing for mock examinations
• Are aiming for a grade 6 or 7
• Need regular support throughout the course

Why Students Choose Online Computer Science Tutoring

Online tutoring is especially suitable for Computer Science because the student and tutor can work directly with digital material.

Benefits include:

• Personalized one-to-one lessons
• Flexible scheduling
• Screen sharing
• Live code review
• Algorithm tracing
• Collaborative debugging
• Examination-question practice
• Support in English or Spanish
• Lessons adapted to Python or Java
• Access from any location
• Regular or short-term support

The tutor can observe the student’s reasoning and programming process rather than looking only at the final answer.

Why Families Choose IBMeta

IBMeta specializes in International Baccalaureate academic support and connects students with experienced educators from different countries.

Families benefit from:

• Experienced IB teachers and examiners
• Subject-specific support
• Personalized one-to-one lessons
• Flexible online scheduling
• Support for SL and HL
• Assistance with both the 2026 and 2027 courses
• Paper 1 and Paper 2 preparation
• Legacy Paper 3 support
• Internal Assessment guidance
• Python and Java support
• Lessons in English and Spanish
• Tutor matching based on individual needs
• A free introductory meeting before lessons begin

Our objective is not only to help students improve their academic performance.

We also help them become more logical, confident and independent problem-solvers.

Find the Right IB Computer Science Tutor

Whether your child needs regular support, help with programming, preparation for an examination or guidance with the Internal Assessment, IBMeta can help.

We begin by understanding:

• The student’s course level
• The examination session
• The curriculum followed by the school
• The programming language
• Current performance
• Upcoming assessments
• Internal Assessment progress
• Preferred lesson language
• Principal areas of difficulty
• Availability
• Academic objectives

We then recommend the most appropriate IB Computer Science educator from our network and arrange a free introductory meeting before lessons begin.