Duplicate from nkazi/MohlerASAG

Co-authored-by: Nazmul Kazi <nkazi@users.noreply.huggingface.co>

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README.md

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+---
+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},
+}
+```

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