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  num_examples: 5
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  download_size: 6139
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  dataset_size: 5577
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  num_examples: 5
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  download_size: 6139
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  dataset_size: 5577
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+ license: cc
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+ task_categories:
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+ - text-generation
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+ language:
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+ - en
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+ tags:
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+ - code
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+ pretty_name: inpro
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+ size_categories:
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+ - 10K<n<100K
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  ---
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+
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+ # Dataset Card for intro_prog
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+
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+ ## Dataset Description
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+
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+ ### Dataset Summary
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+
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+ IntroProg is a collection of students' submissions to assignments in various introductory programming courses offered at different universities.
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+ Currently, the dataset contains submissions collected from Dublin City University, and the University of Singapore.
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+
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+ #### Dublin
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+
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+ The Dublin programming dataset is a dataset composed of students' submissions to introductory programming assignments at the University of Dublin.
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+ Students submitted these programs for multiple programming courses over the duration of three academic years.
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+
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+ #### Singapore
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+
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+ The Singapore dataset contains 2442 correct and 1783 buggy program attempts by 361 undergraduate students
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+ crediting an introduction to Python programming course at NUS (National University of Singapore).
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+
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+
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+ ### Supported Tasks and Leaderboards
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+
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+ #### "Metadata": Program synthesis
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+
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+ Similarly to the [Most Basic Python Programs](https://huggingface.co/datasets/mbpp) (mbpp), the data split can be used to evaluate
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+ code generations models.
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+
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+ #### "Data"
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+
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+ The data configuration contains all the submissions as well as an indicator of whether these passed the required test.
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+
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+ #### "repair": Program refinement/repair
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+
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+ The "repair" configuration of each dataset is a subset of the "data" configuration
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+ augmented with educators' annotations on the corrections to the buggy programs.
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+ This configuration can be used for the task of program refinement. In [Computing Education Research](https://faculty.washington.edu/ajko/cer/) (CER),
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+ methods for automatically repairing student programs are used to provide students with feedback and help them debug their code.
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+
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+ #### "bug": Bug classification
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+
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+ [Coming soon]
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+
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+ ### Languages
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+
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+ The assignments were written in Python.
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+
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+ ## Dataset Structure
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+
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+ One configuration is defined by one source dataset *dublin* or *singapore* and one subconfiguration ("metadata", "data", or "repair"):
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+
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+ * "dublin_metadata"
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+ * "dublin_data"
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+ * "dublin_repair"
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+ * "singapore_metadata"
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+ * "singapore_data"
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+ * "singapore_repair"
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+
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+
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+ ### Data Instances
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+
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+ [More Information Needed]
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+
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+ ### Data Fields
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+
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+ [More Information Needed]
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+
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+ Some of the fields are configuration specific
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+
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+ * submission_id: a unique number identifying the submission
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+ * user: a unique string identifying the (anonymized) student who submitted the solution
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+ * date: the timestamp at which the grading server received the submission
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+ * func_code: the cleaned code submitted
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+ * func_name: the name of the function that had to be implemented
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+ * assingment_id: the unique (string) identifier of the assignment that had to be completed
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+ * academic_year: the starting year of the academic year (e.g. 2015 for the academic year 2015-2016)
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+ * module: the course/module
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+ * test: a human eval-style string which can be used to execute the submitted solution on the provided test cases
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+ * Description: a description of what the function is supposed to achieve
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+ * correct: whether the solution passed all tests or not
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+
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+
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+ ### Data Splits
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+
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+ #### Dublin
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+
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+ The Dublin dataset is split into a training and validation set. The training set contains the submissions to the assignments
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+ written during the academic years 2015-2016, and 2016-2017, while the test set contains programs written during the academic year 2017-2018.
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+
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+ #### Singapore
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+
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+ The Singapore dataset only contains a training split, which can be used as a test split for evaluating how your feedback
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+ methods perform on an unseen dataset (if, for instance, you train your methods on the Dublin Dataset).
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+
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+ ## Dataset Creation
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+
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+ ### Curation Rationale
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+
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+ [More Information Needed]
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+
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+ ### Source Data
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+
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+ #### Initial Data Collection and Normalization
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+
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+ [More Information Needed]
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+
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+ #### Who are the source language producers?
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+
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+ [More Information Needed]
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+
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+ ### Annotations
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+
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+ #### Annotation process
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+
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+ [More Information Needed]
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+
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+ #### Who are the annotators?
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+
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+ [More Information Needed]
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+
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+ ### Personal and Sensitive Information
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+
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+ [More Information Needed]
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+
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+ ## Considerations for Using the Data
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+
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+ ### Social Impact of Dataset
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+
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+ [More Information Needed]
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+ ### Discussion of Biases
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+ [More Information Needed]
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+ ### Other Known Limitations
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+
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+ [More Information Needed]
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+
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+ ## Additional Information
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+
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+ ### Dataset Curators
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+
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+ [More Information Needed]
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+
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+ ### Licensing Information
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+
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+
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+ #### Dublin
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+
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+ #### Singapore
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+
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+ The data was released under a [GNU Lesser General Public License v3.0](https://github.com/githubhuyang/refactory/blob/master/LICENSE) license
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+
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+
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+ ### Citation Information
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+
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+ ```
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+ @inproceedings{azcona2019user2code2vec,
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+ title={user2code2vec: Embeddings for Profiling Students Based on Distributional Representations of Source Code},
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+ author={Azcona, David and Arora, Piyush and Hsiao, I-Han and Smeaton, Alan},
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+ booktitle={Proceedings of the 9th International Learning Analytics & Knowledge Conference (LAK’19)},
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+ year={2019},
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+ organization={ACM}
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+ }
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+ @inproceedings{DBLP:conf/edm/CleuziouF21,
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+ author = {Guillaume Cleuziou and
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+ Fr{\'{e}}d{\'{e}}ric Flouvat},
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+ editor = {Sharon I{-}Han Hsiao and
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+ Shaghayegh (Sherry) Sahebi and
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+ Fran{\c{c}}ois Bouchet and
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+ Jill{-}J{\^{e}}nn Vie},
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+ title = {Learning student program embeddings using abstract execution traces},
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+ booktitle = {Proceedings of the 14th International Conference on Educational Data
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+ Mining, {EDM} 2021, virtual, June 29 - July 2, 2021},
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+ publisher = {International Educational Data Mining Society},
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+ year = {2021},
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+ timestamp = {Wed, 09 Mar 2022 16:47:22 +0100},
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+ biburl = {https://dblp.org/rec/conf/edm/CleuziouF21.bib},
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+ bibsource = {dblp computer science bibliography, https://dblp.org}
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+ }
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+ ```
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+
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+ ### Contributions
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+
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+ [More Information Needed]