| --- |
| dataset_info: |
| - config_name: dublin_metadata |
| features: |
| - name: assignment_id |
| dtype: string |
| - name: func_name |
| dtype: string |
| - name: reference_solution |
| dtype: string |
| - name: description |
| dtype: string |
| - name: test |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 18983 |
| num_examples: 36 |
| - name: test |
| num_bytes: 17403 |
| num_examples: 35 |
| download_size: 41873 |
| dataset_size: 36386 |
| - config_name: singapore_metadata |
| features: |
| - name: assignment_id |
| dtype: string |
| - name: func_name |
| dtype: string |
| - name: reference_solution |
| dtype: string |
| - name: description |
| dtype: string |
| - name: test |
| dtype: string |
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| - name: train |
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| num_examples: 5 |
| download_size: 6139 |
| dataset_size: 5577 |
| - config_name: dublin_data |
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| - name: submission_id |
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| - name: func_code |
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| - name: assignment_id |
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| - name: description |
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| - name: test |
| dtype: string |
| - name: correct |
| dtype: bool |
| - name: user |
| dtype: string |
| - name: academic_year |
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| - name: train |
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| num_examples: 7486 |
| - name: test |
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| num_examples: 14259 |
| download_size: 15756562 |
| dataset_size: 12149653 |
| - config_name: singapore_data |
| features: |
| - name: submission_id |
| dtype: int32 |
| - name: func_code |
| dtype: string |
| - name: assignment_id |
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| dtype: string |
| - name: correct |
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| splits: |
| - name: train |
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| num_examples: 4394 |
| download_size: 5705043 |
| dataset_size: 5098928 |
| - config_name: dublin_repair |
| features: |
| - name: submission_id |
| dtype: int32 |
| - name: func_code |
| dtype: string |
| - name: assignment_id |
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| - name: func_name |
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| - name: test |
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| - name: annotation |
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| - name: user |
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| - name: academic_year |
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| - name: train |
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| num_examples: 307 |
| - name: test |
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| num_examples: 1698 |
| download_size: 1929518 |
| dataset_size: 1681503 |
| - config_name: singapore_repair |
| features: |
| - name: submission_id |
| dtype: int32 |
| - name: func_code |
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| - name: assignment_id |
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| dtype: string |
| - name: annotation |
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| - name: train |
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| num_examples: 18 |
| download_size: 21737 |
| dataset_size: 18979 |
| - config_name: newcaledonia_metadata |
| features: |
| - name: assignment_id |
| dtype: string |
| - name: func_name |
| dtype: string |
| - name: reference_solution |
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| - name: description |
| dtype: string |
| - name: test |
| dtype: string |
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| - name: train |
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| num_examples: 9 |
| download_size: 9760 |
| dataset_size: 9053 |
| - config_name: newcaledonia_data |
| features: |
| - name: submission_id |
| dtype: int32 |
| - name: func_code |
| dtype: string |
| - name: assignment_id |
| dtype: string |
| - name: func_name |
| dtype: string |
| - name: description |
| dtype: string |
| - name: test |
| dtype: string |
| - name: correct |
| dtype: bool |
| splits: |
| - name: train |
| num_bytes: 932024 |
| num_examples: 1201 |
| download_size: 1198518 |
| dataset_size: 932024 |
| --- |
| # Dataset Card for intro_prog |
| |
| ## Dataset Description |
| |
| ### Dataset Summary |
| |
| IntroProg is a collection of students' submissions to assignments in various introductory programming courses offered at different universities. |
| Currently, the dataset contains submissions collected from Dublin City University, and the University of Singapore. |
| |
| #### Dublin |
| |
| The Dublin programming dataset is a dataset composed of students' submissions to introductory programming assignments at the University of Dublin. |
| Students submitted these programs for multiple programming courses over the duration of three academic years. |
| |
| #### Singapore |
| |
| The Singapore dataset contains 2442 correct and 1783 buggy program attempts by 361 undergraduate students |
| crediting an introduction to Python programming course at NUS (National University of Singapore). |
| |
| |
| ### Supported Tasks and Leaderboards |
| |
| #### "Metadata": Program synthesis |
| |
| Similarly to the [Most Basic Python Programs](https://huggingface.co/datasets/mbpp) (mbpp), the data split can be used to evaluate |
| code generations models. |
| |
| #### "Data" |
| |
| The data configuration contains all the submissions as well as an indicator of whether these passed the required test. |
| |
| #### "repair": Program refinement/repair |
| |
| The "repair" configuration of each dataset is a subset of the "data" configuration |
| augmented with educators' annotations on the corrections to the buggy programs. |
| This configuration can be used for the task of program refinement. In [Computing Education Research](https://faculty.washington.edu/ajko/cer/) (CER), |
| methods for automatically repairing student programs are used to provide students with feedback and help them debug their code. |
| |
| #### "bug": Bug classification |
| |
| [Coming soon] |
| |
| ### Languages |
| |
| The assignments were written in Python. |
| |
| ## Dataset Structure |
| |
| One configuration is defined by one source dataset *dublin* or *singapore* and one subconfiguration ("metadata", "data", or "repair"): |
| |
| * "dublin_metadata" |
| * "dublin_data" |
| * "dublin_repair" |
| * "singapore_metadata" |
| * "singapore_data" |
| * "singapore_repair" |
| |
| |
| ### Data Instances |
| |
| [More Information Needed] |
| |
| ### Data Fields |
| |
| [More Information Needed] |
| |
| Some of the fields are configuration specific |
| |
| * submission_id: a unique number identifying the submission |
| * user: a unique string identifying the (anonymized) student who submitted the solution |
| * date: the timestamp at which the grading server received the submission |
| * func_code: the cleaned code submitted |
| * func_name: the name of the function that had to be implemented |
| * assingment_id: the unique (string) identifier of the assignment that had to be completed |
| * academic_year: the starting year of the academic year (e.g. 2015 for the academic year 2015-2016) |
| * module: the course/module |
| * test: a human eval-style string which can be used to execute the submitted solution on the provided test cases |
| * Description: a description of what the function is supposed to achieve |
| * correct: whether the solution passed all tests or not |
|
|
|
|
| ### Data Splits |
|
|
| #### Dublin |
|
|
| The Dublin dataset is split into a training and validation set. The training set contains the submissions to the assignments |
| written during the academic years 2015-2016, and 2016-2017, while the test set contains programs written during the academic year 2017-2018. |
|
|
| #### Singapore |
|
|
| The Singapore dataset only contains a training split, which can be used as a test split for evaluating how your feedback |
| methods perform on an unseen dataset (if, for instance, you train your methods on the Dublin Dataset). |
|
|
| ## Dataset Creation |
|
|
| ### Curation Rationale |
|
|
| [More Information Needed] |
|
|
| ### Source Data |
|
|
| #### Initial Data Collection and Normalization |
|
|
| [More Information Needed] |
|
|
| #### Who are the source language producers? |
|
|
| [More Information Needed] |
|
|
| ### Annotations |
|
|
| #### Annotation process |
|
|
| [More Information Needed] |
|
|
| #### Who are the annotators? |
|
|
| [More Information Needed] |
|
|
| ### Personal and Sensitive Information |
|
|
| [More Information Needed] |
|
|
| ## Considerations for Using the Data |
|
|
| ### Social Impact of Dataset |
|
|
| [More Information Needed] |
|
|
| ### Discussion of Biases |
|
|
| [More Information Needed] |
|
|
| ### Other Known Limitations |
|
|
| [More Information Needed] |
|
|
| ## Additional Information |
|
|
| ### Dataset Curators |
|
|
| [More Information Needed] |
|
|
| ### Licensing Information |
|
|
|
|
| #### Dublin |
|
|
| #### Singapore |
|
|
| The data was released under a [GNU Lesser General Public License v3.0](https://github.com/githubhuyang/refactory/blob/master/LICENSE) license |
|
|
|
|
| ### Citation Information |
|
|
| ``` |
| @inproceedings{azcona2019user2code2vec, |
| title={user2code2vec: Embeddings for Profiling Students Based on Distributional Representations of Source Code}, |
| author={Azcona, David and Arora, Piyush and Hsiao, I-Han and Smeaton, Alan}, |
| booktitle={Proceedings of the 9th International Learning Analytics & Knowledge Conference (LAK’19)}, |
| year={2019}, |
| organization={ACM} |
| } |
| @inproceedings{DBLP:conf/edm/CleuziouF21, |
| author = {Guillaume Cleuziou and |
| Fr{\'{e}}d{\'{e}}ric Flouvat}, |
| editor = {Sharon I{-}Han Hsiao and |
| Shaghayegh (Sherry) Sahebi and |
| Fran{\c{c}}ois Bouchet and |
| Jill{-}J{\^{e}}nn Vie}, |
| title = {Learning student program embeddings using abstract execution traces}, |
| booktitle = {Proceedings of the 14th International Conference on Educational Data |
| Mining, {EDM} 2021, virtual, June 29 - July 2, 2021}, |
| publisher = {International Educational Data Mining Society}, |
| year = {2021}, |
| timestamp = {Wed, 09 Mar 2022 16:47:22 +0100}, |
| biburl = {https://dblp.org/rec/conf/edm/CleuziouF21.bib}, |
| bibsource = {dblp computer science bibliography, https://dblp.org} |
| } |
| ``` |
|
|
| ### Contributions |
|
|
| [More Information Needed] |
