Datasets:

colearnlab
/

misconception-detection

---
pretty_name: Misconception Detection in K-12 Student Workings
license: other
license_name: colearn-competition-use-only
license_link: https://www.kaggle.com/competitions/misconception-detection-in-k-12-student-workings/rules
language:
  - id
  - en
task_categories:
  - image-classification
  - image-to-text
tags:
  - education
  - mathematics
  - handwriting
  - misconceptions
  - k-12
  - kaggle-competition
size_categories:
  - 10K<n<100K
extra_gated_heading: Access to the CoLearn competition data
extra_gated_prompt: >-
  This dataset is provided by CoLearn (IQ Edu Pte Ltd) solely for participation in the Kaggle competition
  "Misconception Detection in K-12 Student Workings"
  (https://www.kaggle.com/competitions/misconception-detection-in-k-12-student-workings) and for discussion
  on its Kaggle forums. It is a mirror of the competition data, offered because some participants found the
  Kaggle download slow. By requesting access you confirm that you have joined the competition and accepted
  its rules, and you agree that you will not redistribute the data, and will not use it for any other
  purpose, including any commercial purpose, without CoLearn's prior written agreement. The same applies to
  models trained on it. The data contains images of children's schoolwork: you must not attempt to identify
  any student. To discuss any other use, contact engineering@colearnlab.com.
extra_gated_fields:
  Kaggle username: text
  I have joined the competition and accepted its rules on Kaggle: checkbox
  I will use this data only for the competition and will not redistribute it: checkbox
---
# Misconception Detection in K-12 Student Workings

**Mirror of the competition data for the Kaggle competition
[Misconception Detection in K-12 Student Workings](https://www.kaggle.com/competitions/misconception-detection-in-k-12-student-workings),
hosted and sponsored by CoLearn (IQ Edu Pte Ltd, Singapore).**

This repository exists only because some participants found the Kaggle download slow. The files are identical to
**data version 2** on Kaggle (topic names in English). Submissions, the leaderboard, discussion and the rules all
remain on Kaggle; nothing here is accepted or scored.

## Terms of use

The data may be used **only to take part in the competition and to discuss it on the Kaggle forums**, under the
[competition rules](https://www.kaggle.com/competitions/misconception-detection-in-k-12-student-workings/rules).
You may not redistribute it or use it for any other purpose, including any commercial purpose, without CoLearn's
prior written agreement; the same applies to models trained on it. To discuss other uses, contact
**engineering@colearnlab.com**.

The data contains images of children's schoolwork. Every image was screened for personally identifying
information, and images showing a name, face, signature, school identifier or contact detail were removed. No
student names, account details or source URLs are included. **You must not attempt to identify any student.**

## The task

Each row is one **trace**: a single student's handwritten attempt at a single practice question, photographed with
a phone. Students are in Indonesian grades 4–12 (ages roughly 9–18). The questions and the handwriting are in Bahasa
Indonesia; question texts contain LaTeX for mathematical notation. The 499 questions span 61 curriculum topics:
whole-number and integer operations, fractions and decimals, LCM/GCD, powers and surds, algebra, functions,
measurement, geometry, statistics and probability.

**What you are predicting:** `root_code`, the taxonomy code of the root misconception in the working, or `NONE` if
the working has no major misconception. The root is the misconception the rest of the working follows from, not
necessarily the first error written on the page. A root is always a major misconception (one that changes the
reasoning or the result). `NONE` means the working is fully correct or carries only minor slips; about a third of
rows are `NONE`.

Submissions are evaluated on Kaggle with **MAP@3**: up to three ranked codes per row, scoring 1, 1/2 or 1/3 by the
rank of the correct code.

## Files

| File | Contents |
|---|---|
| `train.csv` | 1,500 labelled rows |
| `test.csv` | 3,498 rows without labels (public/private split hidden) |
| `sample_submission.csv` | a submission in the correct format, `NONE` for every row |
| `taxonomy_v1.csv` | the 75 misconception codes that occur in this dataset, with name, description, nature (conceptual / procedural / careless / meta), default severity, typical grade band and section |
| `unlabelled.csv` | 50,000 additional student workings without labels, for semi-supervised use |
| `images/train/`, `images/test/`, `images/unlabelled/` | one JPEG per row, named by `image_file` |
| `images/questions/` | 119 question diagrams, referenced by `question_image_file` |

## Columns

In `train.csv`, `test.csv` and `unlabelled.csv`:

- `trace_id`: unique identifier of the trace
- `question_id`: identifier of the practice question; the same question appears in train and test, answered by different students
- `grade`: school grade, 4–12
- `topic_name`: curriculum topic of the question, in English
- `question_text`: the question, with LaTeX for mathematical notation
- `question_image_file`: filename of the question's diagram in `images/questions/`, or empty
- `correct_answer`: the answer key, as given to the student
- `image_file`: filename of the photo of the student's working

In `train.csv` only:

- `root_code`: **the target**; taxonomy code of the root misconception, or `NONE`
- `transcription_steps`: line-by-line transcription of the handwriting (`L1 | ...`, `L2 | ...`); student text verbatim, annotator notes in [brackets]
- `final_answer_correct`: whether the student's final answer matches the key
- `clean`: true if the working has no finding at all, major or minor
- `misconceptions`: all findings as JSON (step, span, category, severity, root flag, explanation in English); auxiliary supervision, only `root_code` is scored

**Test-time inputs.** Test rows carry the image and the question context only; there is no transcription. Reading
the handwriting is part of the task.

## How the labels were made

Every trace was annotated in three steps: the working was transcribed line by line; the question was solved
independently of the answer key; each error was recorded as a finding with a taxonomy code and a severity, and one
major finding was marked as the root. The annotations follow the protocol and the taxonomy defined by CoLearn's
teachers, and the labelled set was reviewed by CoLearn before release. Naming a misconception involves judgement,
and some codes have close neighbours on which even trained annotators sometimes differ; expect some label noise in
both train and test.

## Splits and statistics

Splits are disjoint by student and stratified on root code and grade. Questions are shared across splits.

| Split | Rows |
|---|---|
| Train | 1,500 |
| Public leaderboard | 1,000 |
| Private leaderboard | 2,498 |

- The most common categories (correct work, `ANS-01`, `CAL-01`, `WOP-01`, `FRM-01`, `OTH-00`) were capped at about 190 rows each, so the class distribution is flatter than in the raw data.
- 13 codes have a single training example; every code that is an answer in the test set has at least one training example.
- Images are JPEG, typically 1024×1024 px; about 460 MB labelled and 4.7 GB unlabelled.

The unlabelled pool holds 50,000 further workings from the same practice app, with no labels and no
transcriptions. No student in it appears in the test set, and nothing in it is a labelled row.

## Citation

CoLearn. Misconception Detection in K-12 Student Workings. https://kaggle.com/competitions/misconception-detection-in-k-12-student-workings, 2026. Kaggle.

CoLearn Lab (2026). [Teaching open-weight models to spot where students actually go wrong](https://colearnlab.com/training-llms-to-identify-student-misconceptions-from-their-written-work/).

	---
	pretty_name: Misconception Detection in K-12 Student Workings
	license: other
	license_name: colearn-competition-use-only
	license_link: https://www.kaggle.com/competitions/misconception-detection-in-k-12-student-workings/rules
	language:
	- id
	- en
	task_categories:
	- image-classification
	- image-to-text
	tags:
	- education
	- mathematics
	- handwriting
	- misconceptions
	- k-12
	- kaggle-competition
	size_categories:
	- 10K<n<100K
	extra_gated_heading: Access to the CoLearn competition data
	extra_gated_prompt: >-
	This dataset is provided by CoLearn (IQ Edu Pte Ltd) solely for participation in the Kaggle competition
	"Misconception Detection in K-12 Student Workings"
	(https://www.kaggle.com/competitions/misconception-detection-in-k-12-student-workings) and for discussion
	on its Kaggle forums. It is a mirror of the competition data, offered because some participants found the
	Kaggle download slow. By requesting access you confirm that you have joined the competition and accepted
	its rules, and you agree that you will not redistribute the data, and will not use it for any other
	purpose, including any commercial purpose, without CoLearn's prior written agreement. The same applies to
	models trained on it. The data contains images of children's schoolwork: you must not attempt to identify
	any student. To discuss any other use, contact engineering@colearnlab.com.
	extra_gated_fields:
	Kaggle username: text
	I have joined the competition and accepted its rules on Kaggle: checkbox
	I will use this data only for the competition and will not redistribute it: checkbox
	---

	# Misconception Detection in K-12 Student Workings

	**Mirror of the competition data for the Kaggle competition
	[Misconception Detection in K-12 Student Workings](https://www.kaggle.com/competitions/misconception-detection-in-k-12-student-workings),
	hosted and sponsored by CoLearn (IQ Edu Pte Ltd, Singapore).**

	This repository exists only because some participants found the Kaggle download slow. The files are identical to
	data version 2 on Kaggle (topic names in English). Submissions, the leaderboard, discussion and the rules all
	remain on Kaggle; nothing here is accepted or scored.

	## Terms of use

	The data may be used only to take part in the competition and to discuss it on the Kaggle forums, under the
	[competition rules](https://www.kaggle.com/competitions/misconception-detection-in-k-12-student-workings/rules).
	You may not redistribute it or use it for any other purpose, including any commercial purpose, without CoLearn's
	prior written agreement; the same applies to models trained on it. To discuss other uses, contact
	engineering@colearnlab.com.

	The data contains images of children's schoolwork. Every image was screened for personally identifying
	information, and images showing a name, face, signature, school identifier or contact detail were removed. No
	student names, account details or source URLs are included. You must not attempt to identify any student.

	## The task

	Each row is one trace: a single student's handwritten attempt at a single practice question, photographed with
	a phone. Students are in Indonesian grades 4–12 (ages roughly 9–18). The questions and the handwriting are in Bahasa
	Indonesia; question texts contain LaTeX for mathematical notation. The 499 questions span 61 curriculum topics:
	whole-number and integer operations, fractions and decimals, LCM/GCD, powers and surds, algebra, functions,
	measurement, geometry, statistics and probability.

	What you are predicting: `root_code`, the taxonomy code of the root misconception in the working, or `NONE` if
	the working has no major misconception. The root is the misconception the rest of the working follows from, not
	necessarily the first error written on the page. A root is always a major misconception (one that changes the
	reasoning or the result). `NONE` means the working is fully correct or carries only minor slips; about a third of
	rows are `NONE`.

	Submissions are evaluated on Kaggle with MAP@3: up to three ranked codes per row, scoring 1, 1/2 or 1/3 by the
	rank of the correct code.

	## Files

	\| File \| Contents \|
	\|---\|---\|
	\| `train.csv` \| 1,500 labelled rows \|
	\| `test.csv` \| 3,498 rows without labels (public/private split hidden) \|
	\| `sample_submission.csv` \| a submission in the correct format, `NONE` for every row \|
	\| `taxonomy_v1.csv` \| the 75 misconception codes that occur in this dataset, with name, description, nature (conceptual / procedural / careless / meta), default severity, typical grade band and section \|
	\| `unlabelled.csv` \| 50,000 additional student workings without labels, for semi-supervised use \|
	\| `images/train/`, `images/test/`, `images/unlabelled/` \| one JPEG per row, named by `image_file` \|
	\| `images/questions/` \| 119 question diagrams, referenced by `question_image_file` \|

	## Columns

	In `train.csv`, `test.csv` and `unlabelled.csv`:

	- `trace_id`: unique identifier of the trace
	- `question_id`: identifier of the practice question; the same question appears in train and test, answered by different students
	- `grade`: school grade, 4–12
	- `topic_name`: curriculum topic of the question, in English
	- `question_text`: the question, with LaTeX for mathematical notation
	- `question_image_file`: filename of the question's diagram in `images/questions/`, or empty
	- `correct_answer`: the answer key, as given to the student
	- `image_file`: filename of the photo of the student's working

	In `train.csv` only:

	- `root_code`: the target; taxonomy code of the root misconception, or `NONE`
	- `transcription_steps`: line-by-line transcription of the handwriting (`L1 \| ...`, `L2 \| ...`); student text verbatim, annotator notes in [brackets]
	- `final_answer_correct`: whether the student's final answer matches the key
	- `clean`: true if the working has no finding at all, major or minor
	- `misconceptions`: all findings as JSON (step, span, category, severity, root flag, explanation in English); auxiliary supervision, only `root_code` is scored

	Test-time inputs. Test rows carry the image and the question context only; there is no transcription. Reading
	the handwriting is part of the task.

	## How the labels were made

	Every trace was annotated in three steps: the working was transcribed line by line; the question was solved
	independently of the answer key; each error was recorded as a finding with a taxonomy code and a severity, and one
	major finding was marked as the root. The annotations follow the protocol and the taxonomy defined by CoLearn's
	teachers, and the labelled set was reviewed by CoLearn before release. Naming a misconception involves judgement,
	and some codes have close neighbours on which even trained annotators sometimes differ; expect some label noise in
	both train and test.

	## Splits and statistics

	Splits are disjoint by student and stratified on root code and grade. Questions are shared across splits.

	\| Split \| Rows \|
	\|---\|---\|
	\| Train \| 1,500 \|
	\| Public leaderboard \| 1,000 \|
	\| Private leaderboard \| 2,498 \|

	- The most common categories (correct work, `ANS-01`, `CAL-01`, `WOP-01`, `FRM-01`, `OTH-00`) were capped at about 190 rows each, so the class distribution is flatter than in the raw data.
	- 13 codes have a single training example; every code that is an answer in the test set has at least one training example.
	- Images are JPEG, typically 1024×1024 px; about 460 MB labelled and 4.7 GB unlabelled.

	The unlabelled pool holds 50,000 further workings from the same practice app, with no labels and no
	transcriptions. No student in it appears in the test set, and nothing in it is a labelled row.

	## Citation

	CoLearn. Misconception Detection in K-12 Student Workings. https://kaggle.com/competitions/misconception-detection-in-k-12-student-workings, 2026. Kaggle.

	CoLearn Lab (2026). [Teaching open-weight models to spot where students actually go wrong](https://colearnlab.com/training-llms-to-identify-student-misconceptions-from-their-written-work/).