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