|
--- |
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features: |
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dtype: string |
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dtype: string |
|
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|
dtype: string |
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dtype: string |
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|
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|
download_size: 41873 |
|
dataset_size: 36386 |
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|
features: |
|
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|
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|
--- |
|
# Dataset Card for intro_prog |
|
|
|
## Dataset Description |
|
|
|
### Dataset Summary |
|
|
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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. |
|
|
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#### 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). |
|
|
|
|
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### 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] |
