README.md

metadata

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.