---
pretty_name: HighSchoolBench-OEQ
language:
- en
language_creators:
- found
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- question-answering
- visual-question-answering
- text-generation
size_categories:
- 1K<n<10K
tags:
- highschoolbench
- benchmark
- education
- high-school
- exam
- stem
- science
- mathematics
- multimodal
- image
- text
- open-ended
- free-response
configs:
- config_name: context_image_only__text_response
  default: true
  data_files:
  - split: train
    path: data/context_image_only__text_response/train-*.parquet
  - split: validation
    path: data/context_image_only__text_response/validation-*.parquet
  - split: test
    path: data/context_image_only__text_response/test-*.parquet
- config_name: context_table_image__text_response
  data_files:
  - split: train
    path: data/context_table_image__text_response/train-*.parquet
  - split: validation
    path: data/context_table_image__text_response/validation-*.parquet
  - split: test
    path: data/context_table_image__text_response/test-*.parquet
- config_name: context_table_only__text_response
  data_files:
  - split: train
    path: data/context_table_only__text_response/train-*.parquet
  - split: validation
    path: data/context_table_only__text_response/validation-*.parquet
  - split: test
    path: data/context_table_only__text_response/test-*.parquet
- config_name: context_text_contextualized_image__text_response
  data_files:
  - split: train
    path: data/context_text_contextualized_image__text_response/train-*.parquet
  - split: test
    path: data/context_text_contextualized_image__text_response/test-*.parquet
- config_name: context_text_only__text_response
  data_files:
  - split: train
    path: data/context_text_only__text_response/train-*.parquet
  - split: validation
    path: data/context_text_only__text_response/validation-*.parquet
  - split: test
    path: data/context_text_only__text_response/test-*.parquet
dataset_info:
- config_name: context_image_only__text_response
  features:
  - name: id
    dtype: int64
  - name: assessment_name
    dtype: string
  - name: assessment_type
    dtype: string
  - name: subject
    dtype: string
  - name: domain
    dtype: string
  - name: context
    dtype: string
  - name: image_context
    dtype: string
  - name: image
    list: image
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: split
    dtype: string
  - name: answer_type
    dtype: string
  - name: context_policy
    dtype: string
- config_name: context_table_image__text_response
  features:
  - name: id
    dtype: int64
  - name: assessment_name
    dtype: string
  - name: assessment_type
    dtype: string
  - name: subject
    dtype: string
  - name: domain
    dtype: string
  - name: context
    dtype: string
  - name: image_context
    dtype: string
  - name: image
    list: image
  - name: table_context
    dtype: string
  - name: table
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: split
    dtype: string
  - name: answer_type
    dtype: string
  - name: context_policy
    dtype: string
- config_name: context_table_only__text_response
  features:
  - name: id
    dtype: int64
  - name: assessment_name
    dtype: string
  - name: assessment_type
    dtype: string
  - name: subject
    dtype: string
  - name: domain
    dtype: string
  - name: context
    dtype: string
  - name: table_context
    dtype: string
  - name: table
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: split
    dtype: string
  - name: answer_type
    dtype: string
  - name: context_policy
    dtype: string
- config_name: context_text_contextualized_image__text_response
  features:
  - name: id
    dtype: int64
  - name: assessment_name
    dtype: string
  - name: assessment_type
    dtype: string
  - name: subject
    dtype: string
  - name: domain
    dtype: string
  - name: context
    dtype: string
  - name: image_context_1
    dtype: string
  - name: image_1
    dtype: image
  - name: image_context_2
    dtype: string
  - name: image_2
    dtype: image
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: split
    dtype: string
  - name: answer_type
    dtype: string
  - name: context_policy
    dtype: string
- config_name: context_text_only__text_response
  features:
  - name: id
    dtype: int64
  - name: assessment_name
    dtype: string
  - name: assessment_type
    dtype: string
  - name: subject
    dtype: string
  - name: domain
    dtype: string
  - name: context
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: split
    dtype: string
  - name: answer_type
    dtype: string
  - name: context_policy
    dtype: string
---

# HighSchoolBench-OEQ

[GitHub](https://github.com/sciknoworg/high-school-science) • [Master’s thesis](https://doi.org/10.15488/22429)

## Dataset description

**HighSchoolBench-OEQ** contains **2,952 open-ended questions** from high-school standardized assessments and science competitions. It is the open-ended component of **HighSchoolBench**, a multimodal benchmark for evaluating language and vision-language models on high-school assessment questions.

The questions come from assessments such as **AP, IB, SAT, PSAT, NAEP, and PISA**, U.S. state examinations, and U.S. science olympiads. They include text-based questions and questions involving images, diagrams, and tables.

Each question includes a free-text reference answer and metadata identifying its source assessment, subject, domain, and expected answer type. Answers range from numerical values and mathematical expressions to explanations and worked solutions. The multiple-choice component is available separately as [HighSchoolBench-MCQ](https://huggingface.co/datasets/SciKnowOrg/HighSchoolBench-MCQ).

## Dataset statistics

The dataset contains **2,952 questions and 531 images**.

| Split | Questions |
|---|---:|
| `train` | 449 |
| `validation` | 141 |
| `test` | 2,362 |
| **Total** | **2,952** |

The source split `val` is exposed as `validation` on Hugging Face. The `split` field retains the original source label.

## Configurations

The dataset is organized into **five configurations** according to the supporting context. All configurations contain free-text reference answers.

| Configuration | Supporting context |
|---|---|
| `context_text_only__text_response` | Text |
| `context_image_only__text_response` | Images |
| `context_table_only__text_response` | Tables |
| `context_table_image__text_response` | Tables and images |
| `context_text_contextualized_image__text_response` | Images with associated passages |

The context category describes the supporting material accompanying the question; the question stem is stored separately in `question`.

In the contextualized configuration, individual images are paired with their associated passages. This configuration contains `train` and `test` splits; the other configurations also include `validation`.

## Data format

### Main fields

| Field | Description |
|---|---|
| `id` | Question identifier from the source file. |
| `assessment_name` | Source assessment or competition. |
| `assessment_type` | Assessment stage or exam type. |
| `subject` | Subject assigned to the question. |
| `domain` | Topic within the subject. |
| `context` | Supporting passage or shared context, when available. |
| `question` | Question stem. Mathematical expressions use LaTeX. |
| `answer` | Free-text reference answer. |
| `split` | Original split label: `train`, `val`, or `test`. |
| `answer_type` | Expected form of the answer. |
| `context_policy` | Whether the question relies on the provided context, domain knowledge, or both. |

### Context-specific fields

The following fields appear where relevant to the configuration:

| Field | Description |
|---|---|
| `image` | List of images accompanying the question. A single image is stored as a one-item list. |
| `image_context` | Text associated with the images. |
| `table` | Table content represented as a Markdown string. |
| `table_context` | Text associated with the table. |
| `image_N`, `image_context_N` | Individual image and its associated passage in the contextualized configuration. |

### Answer types

The `answer_type` field uses the following categories:

| Value | Answer format |
|---|---|
| `numerical_value` | Numerical answer |
| `numerical_value_with_units` | Numerical answer with units |
| `equation_or_expression` | Equation or mathematical expression |
| `short_answer` | Short textual answer |
| `brief_explanation` | Brief explanation |
| `long_explanation` | Extended explanation |
| `calculation_with_work` | Calculation with working steps |
| `multi_part_answer` | Answer containing multiple parts |

The `context_policy` values are `context_only`, `domain_knowledge_only`, and `context_first_then_domain_knowledge`.

### Available files

The dataset is provided in two formats:

- **Parquet:** `data/<configuration>/`, with typed columns and embedded images. These files are used by the Hugging Face dataset viewer and `load_dataset`.
- **JSON Lines and images:** `subsets/<configuration>/<split>/`, containing a `metadata.jsonl` file and the images referenced by its records.

## Usage

Load a configuration using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset(
    "SciKnowOrg/HighSchoolBench-OEQ",
    "context_image_only__text_response",
)

print(dataset)
```

Access a question and its reference answer:

```python
example = dataset["test"][0]

print("Context:", example["context"])
print("Question:", example["question"])
print("Answer type:", example["answer_type"])
print("Reference answer:", example["answer"])

# Question images are decoded as PIL images.
images = example["image"]
```

List the available configurations:

```python
from datasets import get_dataset_config_names

print(get_dataset_config_names("SciKnowOrg/HighSchoolBench-OEQ"))
```

To download the JSON Lines files and accompanying images:

```bash
hf download SciKnowOrg/HighSchoolBench-OEQ \
    --repo-type dataset \
    --include "subsets/*" \
    --local-dir HighSchoolBench-OEQ
```

## Citation

If you use HighSchoolBench, please cite:

```bibtex
@misc{rani2026highschoolbench,
  title        = {HighSchoolBench: A Multimodal Benchmark of High School Assessment Questions},
  author       = {Rani, Krishna and D'Souza, Jennifer},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/SciKnowOrg/HighSchoolBench-MCQ},
                  \url{https://huggingface.co/datasets/SciKnowOrg/HighSchoolBench-OEQ}}
}
```

HighSchoolBench was introduced in the following master’s thesis:

```bibtex
@mastersthesis{rani2026highschoolbench_thesis,
  title  = {Toward Robust LLM Benchmarking in High School Science Education:
            Constructing a QA Corpus from Standardized Tests},
  author = {Rani, Krishna},
  school = {Gottfried Wilhelm Leibniz Universit{\"a}t Hannover},
  year   = {2026},
  doi    = {10.15488/22429},
  url    = {https://doi.org/10.15488/22429}
}
```