Datasets:
Tasks:
Text Classification
Sub-tasks:
multi-class-classification
Languages:
English
Size:
10K<n<100K
License:
| 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 | |