Datasets:
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---
language:
- en
- cs
license: mit
task_categories:
- text-classification
pretty_name: Edustories
dataset_info:
features:
- name: id
dtype: int64
- name: description
dtype: string
- name: anamnesis
dtype: string
- name: problems_annotated
dtype: string
- name: problems_possible_annotated
dtype: string
- name: solution
dtype: string
- name: solutions_annotated
dtype: string
- name: solutions_possible_annotated
dtype: string
- name: outcome
dtype: string
- name: implications_annotated
dtype: string
- name: implications_possible_annotated
dtype: string
- name: age, school year
dtype: string
- name: hobbies
dtype: string
- name: diagnoses
dtype: string
- name: disorders
dtype: string
- name: anamnesis_cs
dtype: string
- name: solution_cs
dtype: string
- name: outcome_cs
dtype: string
- name: annotator_id
dtype: int64
- name: description_cs
dtype: string
splits:
- name: train
num_bytes: 9156258
num_examples: 1492
download_size: 4826340
dataset_size: 9156258
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for Edustories dataset
This repository contains Edustories dataset (under review at ARR).
The data contains structured descriptions of situations from classes documented by candidate teachers.
Each of the entries, also called casuistics, is structured into a `description` of the background, `anamnesis` describing the situation,
a `solution` describing the intervention of the teacher in the situation, and `outcome` describing the final state of the intervention.
Each of the entries was semi-automatically parsed from the original, free-text journal and associated with additional information from our database.
All the entries were anonymised.
In addition, our annotators manually associated each entry with a set of multiple categories that best fit the described situation, intervention and outcome.
## About the dataset
The dataset comes from student teachers, who collect case studies from their supervising teachers at their teaching practicum. These data are collected through standardized forms that the student teachers complete with their accompanying teachers. The collection of the dataset runs between 2023-2026. All students involved in the collection are informed of the use of the data and have given written consent. Additional case studies will be collected on an ongoing basis from practising teachers that choose to publish their anonymous case studies. All data is subject to multiple stages of anonymisation, so it does not contain any real names of schools, school staff or students.
## Dataset format
The dataset contains the following attributes:
* **Identifier** `id`. Selected entries have duplicate annotations, allowing to evaluate uncertainty
* **Structured story**: `description`, `anamnesis`, `solution` and `outcome` that describe the situation, intervention and its outcome in a free text
* **Annotated labels**: `problems_annotated`, `solutions_annotated`, `implications_annotated`, associating each problem, solution and outcome into a set of pre-defined categories.
* **Uncertain labels**: `problems_possible_annotated`, `solutions_possible_annotated`, `implications_possible_annotated` containing assignments to the same categories but where the annotators were not sure of the correctness of their assignment.
* **Student attributes**: `age, school year`, `hobbies`, `diagnoses`, `disorders` detailing the profile of the student(s) acting in the entry
* **Teacher attributes**: `approbation` and `practice_years` of the teacher acting in the entry
* **Original free-text features in Czech**: `description_cs`, `anamnesis_cs`, `solution_cs` and `outcome_cs` containing structured parts of the story in the original, Czech language.
## Notes
Paper under review.
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