Datasets:
Tasks:
Text Classification
Sub-tasks:
multi-class-classification
Languages:
English
Size:
10K<n<100K
License:
File size: 5,298 Bytes
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language:
- en
license: cc-by-4.0
size_categories:
- 10K<n<100K
task_categories:
- text-classification
task_ids:
- multi-class-classification
tags:
- education
- blooms-taxonomy
- question-generation
- cognitive-level
- benchmark
pretty_name: CogBench
dataset_info:
features:
- name: question_id
dtype: string
- name: question_text
dtype: string
- name: question_type
dtype: string
- name: subject
dtype: string
- name: source
dtype: string
- name: bloom_level
dtype: int64
- name: bloom_name
dtype: string
- name: label_source
dtype: string
- name: confidence
dtype: float64
splits:
- name: train
num_examples: 21827
- name: validation
num_examples: 2636
- name: test
num_examples: 2636
- name: human_annotated
num_examples: 739
---
# CogBench: A Benchmark for Evaluating Cognitive-Level Control in LLM Question Generation
## Dataset Description
CogBench is a benchmark dataset for evaluating whether large language models can generate educational questions at specific cognitive levels according to Bloom's Taxonomy (Anderson & Krathwohl, 2001).
### Dataset Summary
- **27,099 questions** across 16 academic subjects
- **6 cognitive levels** (Remember, Understand, Apply, Analyze, Evaluate, Create)
- **739 human-labeled questions** from peer-reviewed sources
- **26,360 silver-labeled questions** using the CCS classifier (82% accuracy)
- **4 data sources**: Yahya (2012), Mohammed & Omar (2020), OpenStax QA, OpenStax MCQ
### Supported Tasks
- **Bloom's Taxonomy Classification**: Predict the cognitive level (1-6) of a given question
- **Cognitive-Level-Controlled Question Generation**: Evaluate whether an LLM can generate questions at a specified Bloom's level
### Languages
English
## Dataset Structure
### Data Fields
| Field | Type | Description |
|-------|------|-------------|
| `question_id` | string | Unique identifier |
| `question_text` | string | The question text |
| `question_type` | string | `open_ended` or `mcq` |
| `subject` | string | Academic subject (e.g., biology, physics) |
| `source` | string | Data source identifier |
| `bloom_level` | int | Bloom's Taxonomy level (1-6) |
| `bloom_name` | string | Level name (Remember, Understand, Apply, Analyze, Evaluate, Create) |
| `label_source` | string | How the label was assigned (`original_author`, `taxonomy_mapping`, or `ccs_phase1_silver`) |
| `confidence` | float | CCS model confidence score (for silver labels) |
### Data Splits
| Split | N | Description |
|-------|---|-------------|
| `train` | ~21,800 | Silver-labeled + human-labeled (for training classifiers) |
| `validation` | ~2,600 | Silver-labeled (for tuning) |
| `test` | ~2,600 | Silver-labeled (for evaluation) |
| `human_annotated` | 739 | Human-labeled from peer-reviewed sources (gold standard) |
### Subject Distribution
| Subject | Count | Subject | Count |
|---------|-------|---------|-------|
| Physics | 5,294 | Economics | 1,257 |
| Mathematics | 4,665 | Computer Science | 1,001 |
| Biology | 3,421 | Political Science | 824 |
| Business | 2,653 | Astronomy | 781 |
| Chemistry | 2,326 | History | 749 |
| Psychology | 1,665 | Sociology | 736 |
| Nursing | 610 | Philosophy | 228 |
| Anthropology | 150 | General | 739 |
## Dataset Creation
### Source Data
1. **Yahya (2012)**: 600 questions manually classified by education researchers. Published in PLOS ONE.
2. **Mohammed & Omar (2020)**: 141 questions with expert Bloom's classifications. Published in PLOS ONE.
3. **OpenStax QA**: ~9,900 question-answer pairs from open-source textbooks via HuggingFace.
4. **OpenStax MCQ**: ~16,400 multiple-choice questions scraped from OpenStax interactive content.
### Annotations
- **Human labels (739 questions)**: Original Bloom's classifications from peer-reviewed publications
- **Silver labels (26,360 questions)**: Assigned by CCS (Cognitive Classification Score), a fine-tuned BERT-base-uncased model achieving 82.0% exact accuracy and 89.6% adjacent accuracy on 6-level Bloom's classification
### CCS Model Validation
| Method | Exact Accuracy | Adjacent (±1) | F1 Macro |
|--------|---------------|----------------|----------|
| Random | 16.8% | 44.5% | 0.168 |
| Verb Heuristic | 60.6% | 74.4% | 0.613 |
| TF-IDF + SVM | 77.7% | 85.3% | 0.776 |
| LLM Panel (4-vote) | 74.2% | 84.3% | 0.744 |
| **CCS (BERT)** | **82.0%** | **89.6%** | **0.819** |
## Considerations for Using the Data
### Ethical Considerations
- This dataset is intended for research in educational AI and benchmark evaluation
- Silver labels have ~82% accuracy; use with appropriate uncertainty quantification
- Questions are sourced from educational materials and do not contain sensitive content
### Limitations
- Silver labels may contain systematic biases from the CCS model
- Subject distribution is uneven (STEM-heavy due to OpenStax sources)
- Bloom's Taxonomy classification inherently involves subjectivity
## Citation
If you use CogBench in your research, please cite:
```bibtex
@misc{kunuku2026cogbench,
title={CogBench: A Benchmark for Evaluating Cognitive-Level Control in LLM Question Generation},
author={Kunuku, Mourya Teja},
year={2026},
url={https://huggingface.co/datasets/mouryat9/CogBench}
}
```
## License
CC-BY-4.0
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