README.md

metadata

dataset_info:
  - config_name: cs
    features:
      - name: idx
        dtype: int64
      - name: context
        dtype: string
      - name: sentence
        dtype: string
      - name: 'y'
        dtype: string
      - name: confidence
        dtype: string
      - name: y_requires_context
        dtype: string
    splits:
      - name: train
        num_bytes: 3069614
        num_examples: 6096
      - name: validation
        num_bytes: 173932
        num_examples: 339
      - name: test
        num_bytes: 168255
        num_examples: 339
    download_size: 2135425
    dataset_size: 3411801
  - config_name: cs-orig-diaries
    features:
      - name: id
        dtype: int64
      - name: person_id
        dtype: int64
      - name: subject
        dtype: string
      - name: ordering
        dtype: int64
      - name: Q1
        dtype: int64
      - name: Q2
        dtype: int64
      - name: Q3
        dtype: int64
      - name: Q4
        dtype: int64
      - name: Q5
        dtype: int64
      - name: Q6
        dtype: int64
      - name: Q7
        dtype: int64
      - name: diary
        dtype: string
    splits:
      - name: train
        num_bytes: 3071134
        num_examples: 950
    download_size: 1845241
    dataset_size: 3071134
  - config_name: en
    features:
      - name: idx
        dtype: int64
      - name: context
        dtype: string
      - name: sentence
        dtype: string
      - name: 'y'
        dtype: string
      - name: confidence
        dtype: string
      - name: y_requires_context
        dtype: string
    splits:
      - name: train
        num_bytes: 3011633
        num_examples: 6096
      - name: validation
        num_bytes: 170585
        num_examples: 339
      - name: test
        num_bytes: 169709
        num_examples: 339
    download_size: 1876865
    dataset_size: 3351927
configs:
  - config_name: cs
    data_files:
      - split: train
        path: cs/train-*
      - split: validation
        path: cs/validation-*
      - split: test
        path: cs/test-*
  - config_name: cs-orig-diaries
    data_files:
      - split: train
        path: cs-orig-diaries/train-*
  - config_name: en
    data_files:
      - split: train
        path: en/train-*
      - split: validation
        path: en/validation-*
      - split: test
        path: en/test-*
license: apache-2.0
task_categories:
  - text-classification
language:
  - en
  - cs
tags:
  - education
pretty_name: Czech-English Reflective Dataset (CEReD)

Dataset Card for Czech-English Reflective Dataset (CEReD)

This directory contains an anonymized data set of separated sentences and original reflective journals collected within the Reflection Classification project: https://github.com/EduMUNI/reflection-classification See the project repository for more details and the corresponding paper for more details on data curation methodology.

The data is available in in two types of subsets:

1. The cs-orig-diaries contains the full texts of the original reflection journals together with the authors' responses to our questionnaire. Entries in this split contain the following attributes:

   • id: unique reflective diary id
   • person_id: synthetic id of a creator of the diary
   • subject: subject that the reflective diary concern
   • ordering: relative rank of the diary relative to other diaries of the same author
   • Q1: Teacher evaluation: "Student treated the leading teacher with respect."
   • Q2: Teacher evaluation: "Student took responsibility in a preparation for practice."
   • Q3: Teacher evaluation: "Student discussed specific means of their further development."
   • Q4: Teacher evaluation: "Student actively asked me for a support, feedback, reflection."
   • Q5: Teacher evaluation: "Student actively reflected on their activity on practice."
   • Q6: Teacher evaluation: "Student recognized the situation of the class and reacted to it with selected stragegy."
   • Q7: Teacher evaluation: "Student shown interest in a situation in school, in general."
   • diary: Text of the reflective diary

   All questions Q[1-7] are part of the questionnaire filled by the supervising teacher on the relevant practice. The questionnaire concerned the performance evaluation of the candidate teacher student, that authored the reflective diary.

2. Subsets cs and en contain separate sentences that can be used for training a classifier, in selected language: original: Czech (cs) or translated: English (en). Sentences are divided into train, validation (val) and test set. This split can be used to evaluate the classifier on the same data, as we did, hence it allows for comparability of the results. Again, the tab-separated sentences.tsv files contain following attributes:

   • idx: unique sentence id
   • context: textual context surrounding the classified sentence
   • sentence: text of the classified sentence
   • y: target category of the sentence, that annotators agreed upon
   • confidence: confidence, or typicality of the sentence in its assigned category. Annotators were asked: "How typical is this sentence for the picked category?"
   • y_requires_context: whether annotators needed to look at the context, when selecting a category.

For details on the taxonomy of annotated classification, we also make available the annotation manual.

Citation

For the data collection methodology:

@Article{Nehyba2022applications,
  author={Nehyba, Jan and {\v{S}}tef{\'a}nik, Michal},
  title={Applications of deep language models for reflective writings},
  journal={Education and Information Technologies},
  year={2022},
  month={Sep},
  day={05},
  issn={1573-7608},
  doi={10.1007/s10639-022-11254-7},
  url={https://doi.org/10.1007/s10639-022-11254-7}
}

For the dataset itself:

 @misc{Stefanik2021CEReD,
   title = {Czech and English Reflective Dataset ({CEReD})},
   author = {{\v S}tef{\'a}nik, Michal and Nehyba, Jan},
   url = {http://hdl.handle.net/11372/LRT-3573},
   copyright = {Creative Commons - Attribution 4.0 International ({CC} {BY} 4.0)},
   year = {2021} 
 }