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Datasets:
phinniaspp
/
igcse-past-papers

Tasks:
Question Answering
Text Generation
Languages:
English
Size:
10K<n<100K
Tags:
education
igcse
cambridge
exam-questions
multiple-choice
structured-questions
License:
Dataset card Files
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Dataset Viewer
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
subject: string
subject_code: string
year: string
session: string
difficulty: string
paper_type: string
component: string
source: string
question_number: string
question_type: string
question_text: string
options: struct<A: string, B: string, C: string, D: string>
  child 0, A: string
  child 1, B: string
  child 2, C: string
  child 3, D: string
correct_answer: string
explanation: string
text: string
mark_scheme_answer: string
marks: int64
to
{'text': Value('string'), 'subject': Value('string'), 'subject_code': Value('string'), 'year': Value('string'), 'session': Value('string'), 'difficulty': Value('string'), 'paper_type': Value('string'), 'question_number': Value('string'), 'question_type': Value('string'), 'question_text': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 299, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              subject: string
              subject_code: string
              year: string
              session: string
              difficulty: string
              paper_type: string
              component: string
              source: string
              question_number: string
              question_type: string
              question_text: string
              options: struct<A: string, B: string, C: string, D: string>
                child 0, A: string
                child 1, B: string
                child 2, C: string
                child 3, D: string
              correct_answer: string
              explanation: string
              text: string
              mark_scheme_answer: string
              marks: int64
              to
              {'text': Value('string'), 'subject': Value('string'), 'subject_code': Value('string'), 'year': Value('string'), 'session': Value('string'), 'difficulty': Value('string'), 'paper_type': Value('string'), 'question_number': Value('string'), 'question_type': Value('string'), 'question_text': Value('string')}
              because column names don't match

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IGCSE Past Paper Questions (2018–2025)

Structured dataset of past exam questions and mark-scheme answers extracted from Cambridge IGCSE past papers. Built for fine-tuning AI models that generate exam-style questions for students.

Dataset at a Glance

Stat Value
Total questions 32
MCQ questions 0
Structured questions 32
Years 2018 – 2025
Sessions Oct/Nov (primary), May/Jun, Feb/Mar
Source Cambridge Assessment International Education (CAIE)

Subjects Covered

Subject CAIE Code
Accounting 0452
Biology 0610
Chemistry 0620
Computer Science 0478
English Language 0500
Mathematics 0607
Physics 0625

Dataset Structure 

questions.jsonl              # Full dataset (MCQ + structured)
questions_mcq.jsonl          # MCQ questions only
questions_structured.jsonl   # Structured questions only
metadata.json                # Dataset metadata and schema
stats.json                   # Per-subject extraction statistics
pdfs/                        # Original source PDFs (organised by subject/session)

Record Format

MCQ Record 

{
  "text": "### Subject: Physics\n### Difficulty: basic\n### Type: Multiple Choice\n\nQuestion: ...\n\nOptions:\n  A. ...\n  B. ...\n  C. ...\n  D. ...\n\n### Answer: C\n### Explanation: ...",
  "subject": "Physics",
  "subject_code": "0625",
  "year": "2022",
  "session": "Oct/Nov",
  "difficulty": "basic",
  "paper_type": "mcq",
  "component": "11",
  "question_number": 5,
  "question_type": "mcq",
  "question_text": "...",
  "options": {"A": "...", "B": "...", "C": "...", "D": "..."},
  "correct_answer": "C",
  "explanation": "The correct answer is C. ...",
  "source": "IGCSE"
}

Structured Record 

{
  "text": "### Subject: Chemistry\n### Difficulty: exam-style\n### Type: Structured\n### Marks: 6\n\nQuestion: ...\n\n### Model Answer:\n...",
  "subject": "Chemistry",
  "subject_code": "0620",
  "year": "2023",
  "session": "Oct/Nov",
  "difficulty": "exam-style",
  "paper_type": "structured",
  "component": "41",
  "question_number": "3(b)(ii)",
  "question_type": "structured",
  "question_text": "...",
  "mark_scheme_answer": "...",
  "marks": 6,
  "source": "IGCSE"
}

Difficulty Levels

Component Difficulty Description
Paper 1 (11/12/13) basic MCQ — core knowledge recall
Paper 2 (21/22/23) application Short structured — apply knowledge
Paper 3/4 (31–43) exam-style Extended theory — analysis & evaluation
Paper 6 (61/62/63) practical Alternative to Practical

Intended Use

Fine-tune a language model to:

  • Generate MCQ questions with 4 options and a correct answer
  • Generate structured questions with model answers
  • Accept subject + difficulty as conditioning inputs

The text field is pre-formatted as an instruction-tuning prompt, ready for use with SFTTrainer (TRL) or similar supervised fine-tuning setups.

Citation

Original papers are copyright of Cambridge Assessment International Education (CAIE).

License

CC BY 4.0
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