--- 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](https://github.com/EduMUNI/reflection-classification) for more details and the [corresponding paper](https://rdcu.be/cUWGY) 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. 3. 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](https://github.com/EduMUNI/reflection-classification/blob/master/data/annotation_manual.pdf). # Citation For the data collection methodology: ```bibtex @Article{Nehyba2023applications, 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} } ``` APA style: ``` Nehyba, Jan & Štefánik, Michal. (2022). Applications of deep language models for reflective writings. Education and Information Technologies. 28. 1-39. 10.1007/s10639-022-11254-7. ``` For the dataset itself: ```bibtex @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} } ``` APA style: ``` Štefánik, M. & Nehyba, J. (2021). Czech-English Reflective Dataset (CEReD). (Version V1) [Data set]. GitHub. doi:11372/LRT-3573 ```