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Are you currently writing your Computer Science EE? Don't worry, we are here to help you write an Extended Essay that will meet the new IB assessment criteria for first assessment in 2027. Keep reading for our suggestions based on the official IB EE rubric!
Computer Science EE Criteria and Checklist
The extended essays are graded for a total of 30 points based on the following criteria:
Criterion A: Framework for the essay - 6 points
This criterion evaluates whether your research question, chosen research methods, and structural conventions provide an effective foundation for your technical investigation. In Computer Science, this means your framework must be rooted in scientific or engineering methodology, often involving the development of falsifiable hypotheses and replicable data collection. You need to justify why your specific tools like benchmarking software, simulators, or specific programming languages are suitable for answering your question. Your essay must follow standard academic structures, including appropriately labelled code snippets, tables, and diagrams that help the reader follow your logic.
For a maximum of 6 points:
Frame your research question to focus on a specific technical aspect, such as the efficiency of a particular algorithm or the performance of a hardware configuration.
Explicitly define your independent and dependent variables, such as "execution speed" as a function of "input dataset size".
Include a detailed methodology section that describes your hardware specs, software versions, and environment settings so your experiment is fully replicable.
Use standard structural conventions like a Table of Contents, informative subheadings, and a clear introduction to organize your technical argument.
Ensure all pseudo-code or code fragments are presented in a fixed-width font, numbered as figures, and actively referenced in your text.
Criterion B: Knowledge and understanding - 6 points
To score high here, you must show you truly understand the mechanics of your topic. This involves the accurate and consistent application of subject-specific terminology like "Big O notation," "memory management," or "neural network architecture". You should show how your investigation fits into the wider academic field by referencing peer-reviewed journals, documentation, or technical white papers. Your understanding is also shown through the correct use of concepts to frame your research.
For a maximum of 6 points:
Integrate technical terms like "latency," "complexity," or "concurrency" naturally and accurately throughout your essay.
Explain the theoretical logic behind the algorithms or systems you are testing, such as how a specific sorting method handles memory.
Use high-quality sources, such as official documentation or computer science journals, to provide the background context for your research.
Apply consistent symbols and units, ensuring that memory sizes (e.g., MiB) and speeds are labelled correctly.
Avoid over-relying on basic definitions; instead, synthesize information from sources into your own words to demonstrate personal mastery of the topic.
Criterion C: Analysis and line of argument - 6 points
This criterion focuses on your ability to break down technical data to reach meaningful findings. Your analysis must follow standard scientific processes, including quantitative approaches like benchmarking or qualitative comparisons of system architectures. You need to maintain a clear reasoned thread that links your research question, your experimental results, and your final conclusions. Every part of your analysis should be backed by the data you collected and move the reader toward a convincing answer to your question.
For a maximum of 6 points:
Maintain a reasoned thread by explicitly stating how each test result or observation directly relates back to your research question.
Include snippets of your data-processing scripts to show exactly how you arrived at your final metrics.
Use graphical representations of your data if relevant, like line graphs for complexity trends.
Establish mini-conclusions at the end of each major section to summarize your findings and signal the next step in your argument.
Criterion D: Discussion and evaluation - 8 points
This criterion requires a balanced, critical review of your findings in a professional context. You must discuss the significance of your results, weighing them against established industry standards or theoretical limits. Evaluation is key; you must honestly appraise your methodology, identifying specific strengths and technical limitations like hardware bottlenecks or biased datasets. Your conclusion should be a supported synthesis of your work that addresses the research question and mentions any unresolved technical issues.
For a maximum of 8 points:
Compare your experimental findings with published benchmarks or theoretical performance limits to discuss their real-world significance.
Critically evaluate your test environment by identifying specific variables that could have skewed your results, like background OS processes.
Suggest specific, technically feasible improvements or extensions to your research, such as testing on different architectures or using larger datasets.
Ensure your final conclusion is a synthesis of your analysis that directly answers the research question rather than just summarizing the essay.
Discuss the reliability and validity of the data you used, identifying any potential biases or limitations.
Criterion E: Reflection - 4 points
This criterion assesses your growth and engagement through a 500-word reflective statement on the Reflection and Progress Form (RPF). You should evaluate how the process changed you as a learner and developer, providing specific examples of how you tackled bugs or managed your time. Reflections must show evidence of growth and learning. Explain how the skills you gained, like system debugging or data analysis, will help you in future coding projects or university studies.
For a maximum of 4 points:
Use your Researcher’s Reflection Space (RRS) to log specific technical hurdles you faced, such as a script that wouldn't run, and how you solved them.
Reflect on how your understanding of a complex topic, like machine learning or data structures, evolved as you moved from theory to testing.
Provide concrete examples of how you responded to setbacks, such as having to pivot your research question due to a lack of processing power.
Detail exactly how you plan to transfer the research skills you've mastered to your future academic or professional life.
Discuss how the three mandatory reflection sessions with your supervisor helped you refine your technical approach and stay on track.
We hope you found this post helpful in learning more about the IB Computer Science criteria. For more useful materials associated with the IB, check out the wide variety of IA, EE and TOK exemplars available at Clastify and other guides available on our blog.
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