| | ---
|
| | pretty_name: Mohler ASAG
|
| | license: cc-by-4.0
|
| | language:
|
| | - en
|
| | task_categories:
|
| | - text-classification
|
| | - sentence-similarity
|
| | - question-answering
|
| | size_categories:
|
| | - 1K<n<10K
|
| | tags:
|
| | - ASAG
|
| | - NLP
|
| | - Automatic Short Answer Grading
|
| | - Student Responses
|
| | - Computer Science
|
| | - Data Structure
|
| | - Educational Data
|
| | - Semantic Similarity
|
| | - Question-Answering
|
| | - Text Classification
|
| | dataset_info:
|
| | features:
|
| | - name: id
|
| | dtype: string
|
| | - name: question
|
| | dtype: string
|
| | - name: instructor_answer
|
| | dtype: string
|
| | - name: student_answer
|
| | dtype: string
|
| | - name: score_grader_1
|
| | dtype: float32
|
| | - name: score_grader_2
|
| | dtype: float32
|
| | - name: score_avg
|
| | dtype: float32
|
| | splits:
|
| | - name: open_ended
|
| | num_bytes: 153600
|
| | num_examples: 2273
|
| | - name: close_ended
|
| | num_bytes: 11776
|
| | num_examples: 169
|
| | dataset_size: 953344
|
| | configs:
|
| | - config_name: raw
|
| | default: true
|
| | data_files:
|
| | - split: open_ended
|
| | path: data/raw-oe-*
|
| | - split: close_ended
|
| | path: data/raw-ce-*
|
| | - config_name: cleaned
|
| | data_files:
|
| | - split: open_ended
|
| | path: data/cleaned-oe-*
|
| | - split: close_ended
|
| | path: data/cleaned-ce-*
|
| | - config_name: parsed
|
| | data_files:
|
| | - split: open_ended
|
| | path: data/parsed-oe-*
|
| | - split: close_ended
|
| | path: data/parsed-ce-*
|
| | - config_name: annotations
|
| | data_files:
|
| | - split: annotations
|
| | path: data/annotations-*
|
| | ---
|
| |
|
| | <style>
|
| | .callout {
|
| | background-color: #cff4fc;
|
| | border-left: 0.25rem solid #9eeaf9;
|
| | padding: 1rem;
|
| | }
|
| |
|
| | .readme-table-container table {
|
| | font-family:monospace;
|
| | margin: 0;
|
| | }
|
| | </style>
|
| |
|
| | # Dataset Card for "Mohler ASAG"
|
| |
|
| | The **Mohler ASAG** dataset is recognized as one of the first publicly
|
| | available and widely used benchmark datasets for Automatic Short
|
| | Answer Grading (ASAG). It was first introduced by Michael Mohler and
|
| | Rada Mihalcea in 2009. An extended version of the dataset with
|
| | additional questions and corresponding student answers was released in
|
| | 2011. This repository presents the 2011 dataset along with a code
|
| | snippet to extract the 2009 subset.
|
| |
|
| | The dataset was collected from an introductory data structures course
|
| | at the University of North Texas. It covers 87 assessment questions in
|
| | total, including 81 open-ended and 6 closed-ended selection or
|
| | ordering questions. These questions are distributed across 10
|
| | assignments and 2 examinations. Altogether, the dataset contains 2,442
|
| | student responses, with 2,273 corresponding to open-ended questions
|
| | and 169 to closed-ended questions.
|
| |
|
| | - **Authors:** Michael Mohler, Razvan Bunescu, and Rada Mihalcea.
|
| | - **Paper:** [Learning to Grade Short Answer Questions using Semantic
|
| | Similarity Measures and Dependency Graph Alignments](https://aclanthology.org/P11-1076/)
|
| |
|
| | <div class="callout">
|
| | A curated version of the dataset is available on Hugging Face at
|
| | <a href="https://huggingface.co/datasets/nkazi/MohlerASAG-Curated">
|
| | <code>nkazi/MohlerASAG-Curated</code>
|
| | </a>,
|
| | created to improve its quality and usability for NLP research,
|
| | particularly for LLM-based approaches.
|
| | </div>
|
| |
|
| | ## Known Errata
|
| |
|
| | 1. The 2009 paper reports 30 student answers per question for each
|
| | assignment. In reality, assignment 1 contains 29 answers per
|
| | question, assignment 2 contains 30 answers per question, and
|
| | assignment 3 contains 31 answers per question.
|
| | 2. The 2011 paper states that the dataset contains student answers for
|
| | 80 questions. According to the README file included with the data,
|
| | it actually includes answers for 81 open-ended questions.
|
| |
|
| | ## Dataset Conversion Notebook
|
| |
|
| | The Python notebook I developed to convert the Mohler ASAG dataset
|
| | from its source files into a Hugging Face Dataset is available on my
|
| | GitHub profile. It exhibits the process of parsing questions,
|
| | instructor answers, student answers, scores, and annotations from
|
| | their respective source files for each stage, correcting mojibakes in
|
| | the raw data, structuring and organizing the information, dividing and
|
| | transforming the data into subsets and splits, and exporting the final
|
| | dataset in Parquet format for the Hugging Face repository. This
|
| | demonstration ensures transparency, reproducibility, and traceability
|
| | of the conversion process.
|
| |
|
| | <strong>GitHub Link:</strong>
|
| | <a href="https://github.com/nazmulkazi/ML-DL-NLP/blob/main/HF%20Dataset%20-%20Mohler%20ASAG.ipynb">
|
| | https://github.com/nazmulkazi/ML-DL-NLP/blob/main/HF%20Dataset%20-%20Mohler%20ASAG.ipynb
|
| | </a>
|
| |
|
| | ## Dataset Structure and Details
|
| |
|
| | The dataset underwent several processing stages, each represented as a
|
| | separate subset. The raw subset contains the original and unaltered
|
| | student answers exactly as written. In the cleaned subset, the authors
|
| | preprocessed the data by cleaning the text and tokenizing it into
|
| | sentences using the LingPipe toolkit, with sentence boundaries marked
|
| | by `<STOP>` tags. The parsed subset includes outputs from the Stanford
|
| | Dependency Parser with additional postprocessing performed by the
|
| | authors. The annotations subset contains manually annotated data.
|
| | However, only 32 student answers were randomly selected for
|
| | annotation.
|
| |
|
| | The authors ignored responses to the closed-ended questions in all of
|
| | their work. Therefore, the raw, cleaned, and parsed subsets are
|
| | divided into open-ended and closed-ended splits.
|
| |
|
| | Each sample in the raw, cleaned, and parsed subsets includes a unique
|
| | identifier, the question, the instructor's answer, the student's
|
| | answer, scores from two graders, and the average score. Samples in the
|
| | annotations subset contain a unique identifier and the corresponding
|
| | annotations. The unique identifiers are consistent across all subsets
|
| | and follow the format `EXX.QXX.AXX`, where each component corresponds
|
| | to its exercise (i.e., assignment), question, and answer, respectively,
|
| | and `XX` are zero-padded numbers. For consistency, reproducibility,
|
| | and traceability, the identifiers are constructed following the same
|
| | indexing scheme used by the authors, with 1-based numbering for
|
| | exercises and questions and 0-based numbering for student answers.
|
| |
|
| | Exercises E01 through E10 were graded on a 0-5 scale, while E11 and
|
| | E12 were graded on a 0-10 scale. The scores for E11 and E12 were
|
| | converted to a 0-5 scale before computing the average by the authors,
|
| | so all values in the score_avg column are in the 0-5 range. Grader 1
|
| | was the course teaching assistant, and Grader 2 was Michael Mohler.
|
| |
|
| | For further details, please refer to the [README](./README-Mohler.pdf)
|
| | (a formatted and styled version of the README provided by the authors)
|
| | and the associated publications.
|
| |
|
| | ## Student Answer Distribution
|
| |
|
| | Distribution of student answers in the raw, cleaned, and parsed subsets:
|
| |
|
| | <div class="readme-table-container">
|
| |
|
| | | | Q01 | Q02 | Q03 | Q04 | Q05 | Q06 | Q07 | Q08 | Q09 | Q10 | Total |
|
| | |:--------|----:|----:|----:|----:|----:|----:|----:|----:|----:|----:|------:|
|
| | | **E01** | 29 | 29 | 29 | 29 | 29 | 29 | 29 | - | - | - | 203 |
|
| | | **E02** | 30 | 30 | 30 | 30 | 30 | 30 | 30 | - | - | - | 210 |
|
| | | **E03** | 31 | 31 | 31 | 31 | 31 | 31 | 31 | - | - | - | 217 |
|
| | | **E04** | 30 | 30 | 30 | 30 | 30 | 30 | 30 | - | - | - | 210 |
|
| | | **E05** | 28 | 28 | 28 | 28 | - | - | - | - | - | - | 112 |
|
| | | **E06** | 26 | 26 | 26 | 26 | 26 | 26 | 26 | - | - | - | 182 |
|
| | | **E07** | 26 | 26 | 26 | 26 | 26 | 26 | 26 | - | - | - | 182 |
|
| | | **E08** | 27 | 27 | 27 | 27 | 27 | 27 | 27 | - | - | - | 189 |
|
| | | **E09** | 27 | 27 | 27 | 27 | 27 | 27 | 27 | - | - | - | 189 |
|
| | | **E10** | 24 | 24 | 24 | 24 | 24 | 24 | 24 | - | - | - | 168 |
|
| | | **E11** | 30 | 30 | 30 | 30 | 30 | 30 | 30 | 30 | 30 | 30 | 300 |
|
| | | **E12** | 28 | 28 | 28 | 28 | 28 | 28 | 28 | 28 | 28 | 28 | 280 |
|
| |
|
| | </div>
|
| |
|
| | Distribution of student answers in the annotations subset/split:
|
| |
|
| | <div class="readme-table-container">
|
| |
|
| | | | Q01 | Q02 | Q03 | Q04 | Q05 | Q06 | Q07 | Total |
|
| | |:--------|----:|----:|----:|----:|----:|----:|----:|------:|
|
| | | **E01** | 3 | 3 | 3 | 3 | 2 | 1 | 1 | 16 |
|
| | | **E02** | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 8 |
|
| | | **E03** | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 8 |
|
| |
|
| | </div>
|
| |
|
| | ## Code Snippets
|
| | ### Extracting 2009 Dataset
|
| |
|
| | Exercises 1-3 are inherited from the 2009 dataset. The following code
|
| | extracts the raw samples of the 2009 dataset from the raw subset:
|
| |
|
| | ```python
|
| | from datasets import load_dataset
|
| |
|
| | ds = load_dataset('nkazi/MohlerASAG', name='raw', split='open_ended')
|
| | ds_2009 = ds.filter(lambda row: row['id'].split('.')[0] in ['E01', 'E02', 'E03'])
|
| | ```
|
| |
|
| | ### Concatenating Splits
|
| |
|
| | The following code creates a new dataset with rows from both
|
| | open-ended and close-ended splits from the raw subset:
|
| |
|
| | ```python
|
| | from datasets import load_dataset
|
| | from datasets import concatenate_datasets
|
| |
|
| | ds = load_dataset('nkazi/MohlerASAG', name='raw')
|
| | ds_all = concatenate_datasets([ds['open_ended'], ds['close_ended']]).sort('id')
|
| | ```
|
| |
|
| | ### Joining Open-Ended Raw Data with Annotations
|
| |
|
| | The following code joins the annotations with their corresponding
|
| | samples from the raw subset.
|
| |
|
| | ```python
|
| | from datasets import load_dataset
|
| |
|
| | # Load the annotations split and create a mapping
|
| | # from IDs to their annotations.
|
| | ds_ann = load_dataset('nkazi/MohlerASAG', name='annotations', split='annotations')
|
| | ann_map = {row['id']: row['annotations'] for row in ds_ann}
|
| |
|
| | # Load the raw open-ended subset and keep only rows
|
| | # with IDs present in the annotations set.
|
| | ds_raw = load_dataset('nkazi/MohlerASAG', name='raw', split='open_ended') \
|
| | .filter(lambda row: row['id'] in ann_map)
|
| |
|
| | # Collect annotations in the same order as the IDs in
|
| | # the filtered raw dataset.
|
| | ann_list = [ann_map.get(row_id, None) for row_id in ds_raw['id']]
|
| |
|
| | # Add an annotations column to the filtered raw dataset,
|
| | # using the annotations list and feature specification
|
| | # from the annotations subset.
|
| | ds_joined = ds_raw.add_column(
|
| | name = 'annotations',
|
| | column = ann_list,
|
| | feature = ds_ann.features['annotations']
|
| | )
|
| | ```
|
| |
|
| | ## Citation
|
| |
|
| | In addition to citing **Mohler et al. (2011)**, we kindly request that
|
| | a footnote be included referencing the Hugging Face page of this dataset
|
| | ([https://huggingface.co/datasets/nkazi/MohlerASAG](https://huggingface.co/datasets/nkazi/MohlerASAG))
|
| | in order to inform the community of this readily usable version.
|
| |
|
| | ```tex
|
| | @inproceedings{mohler2011learning,
|
| | title = {Learning to Grade Short Answer Questions using Semantic
|
| | Similarity Measures and Dependency Graph Alignments},
|
| | author = {Mohler, Michael and Bunescu, Razvan and Mihalcea, Rada},
|
| | year = 2011,
|
| | month = jun,
|
| | booktitle = {Proceedings of the 49th Annual Meeting of the Association
|
| | for Computational Linguistics: Human Language Technologies},
|
| | pages = {752--762},
|
| | editor = {Lin, Dekang and Matsumoto, Yuji and Mihalcea, Rada},
|
| | publisher = {Association for Computational Linguistics},
|
| | address = {Portland, Oregon, USA},
|
| | url = {https://aclanthology.org/P11-1076},
|
| | }
|
| | ``` |
