IB past paper analysis
ESS
May 2023 · Paper 2 · TZ2
See what this paper tests, focus on the skills that carry the marks, and practise matched Exam Vault questions with AI marking.
Original Exam Vault questions with AI markscheme feedback for this topic.
- 7questions
- 105marks
- 28question parts
- 21micro-topics
Mark distribution
- Foundations of ESS24%
- Ecology21%
- Biodiversity and Conservation15%
- Natural Resources15%
- Land12%
- Atmosphere and Climate Change11%
- Water2%
Hardest areas (by marks)
- 1.Energy choices and sustainability9 marks
- 2.Terrestrial food production systems9 marks
- 3.Technocentric, anthropocentric and ecocentric9 marks
Question types
- outline29%
- explain / justify29%
- write down / state18%
- identify7%
0% of parts are show / prove.
- Paper 2
Questions in this paper
Open a question to see what each part tests, how often that micro-topic comes up, and practise matched questions.
Want to practise the skills from this paper?
Open original Exam Vault questions matched by topic, marks and command term, with AI marking and step-by-step feedback.
Original Exam Vault questions with AI markscheme feedback for this topic.
Explore topic pages
Every micro-topic in this paper, with its exam pattern and practice.
Browse ESS topicsPredicted questions
The likely question types in the next sitting, from the same data.
Predicted Paper 2Good to know
Can I download ESS May 2023 TZ2 Paper 2 here?
No. The paper and its mark scheme belong to the IB and are not reproduced. This page gives the structure of every question (marks, command term, micro-topic, the skill tested), the exam analysis, and practice questions we wrote to test the same skills. Subscribers can also open our summary of how each part's marks are awarded.
Which topics dominate May 2023 TZ2 Paper 2?
Foundations of ESS 24% (typical 16%), Ecology 21% (typical 19%), Biodiversity and Conservation 15% (typical 14%).
How do I practise this paper without the paper?
Press "Practise similar" on any question. We open questions matched by micro-topic, command term and mark range with model answers and AI marking, then bring you back here.