| --- |
| 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} |
| } |
| ``` |
|
|
