README.md

---
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.