--- 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 splits: - name: train num_bytes: 5577 num_examples: 5 download_size: 6139 dataset_size: 5577 - config_name: dublin_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 - name: user dtype: string - name: academic_year dtype: int32 splits: - name: train num_bytes: 4412068 num_examples: 7486 - name: test num_bytes: 7737585 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 dtype: string - name: func_name dtype: string - name: description dtype: string - name: test dtype: string - name: correct dtype: bool splits: - name: train num_bytes: 5098928 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 dtype: string - name: func_name dtype: string - name: description dtype: string - name: test dtype: string - name: annotation dtype: string - name: user dtype: string - name: academic_year dtype: int32 splits: - name: train num_bytes: 234628 num_examples: 307 - name: test num_bytes: 1479344 num_examples: 1698 download_size: 2137031 dataset_size: 1713972 - config_name: singapore_repair 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: annotation dtype: string splits: - name: train num_bytes: 18979 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 dtype: string - name: description dtype: string - name: test dtype: string splits: - name: train num_bytes: 9053 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]