Dataset for Generation of multiple true false questions

Generation of multiple true-false questions

This project provides a Natural Language Pipeline for processing German Textbook sections as an input generating Multiple True-False Questions using GPT2.

Assessments are an important part of the learning cycle and enable the development and promotion of competencies. However, the manual creation of assessments is very time-consuming. Therefore, the number of tasks in learning systems is often limited. In this repository, we provide an algorithm that can automatically generate an arbitrary number of German True False statements from a textbook using the GPT-2 model. The algorithm was evaluated with a selection of textbook chapters from four academic disciplines (see `data` folder) and rated by individual domain experts. One-third of the generated MTF Questions are suitable for learning. The algorithm provides instructors with an easier way to create assessments on chapters of textbooks to test factual knowledge.

As a type of Multiple-Choice question, Multiple True False (MTF) Questions are, among other question types, a simple and efficient way to objectively test factual knowledge. The learner is challenged to distinguish between true and false statements. MTF questions can be presented differently, e.g. by locating a true statement from a series of false statements, identifying false statements among a list of true statements, or separately evaluating each statement as either true or false. Learners must evaluate each statement individually because a question stem can contain both incorrect and correct statements. Thus, MTF Questions as a machine-gradable format have the potential to identify learners’ misconceptions and knowledge gaps.

Example MTF question:

Check the correct statements:

[  ] All trees have green leafs.

[  ] Trees grow towards the sky.

[  ] Leafes can fall from a tree.

 

Features

- generation of false statements

- automatic selection of true statements

- selection of an arbitrary similarity for true and false statements as well as the number of false statements

- generating false statements by adding or deleting negations as well as using a german gpt2

Setup

Installation

1. Create a new environment: `conda create -n mtfenv python=3.9`

2. Activate the environment: `conda activate mtfenv`

3. Install dependencies using anaconda:

```

conda install -y -c conda-forge pdfplumber

conda install -y -c conda-forge nltk

conda install -y -c conda-forge pypdf2

conda install -y -c conda-forge pylatexenc

conda install -y -c conda-forge packaging

conda install -y -c conda-forge transformers

conda install -y -c conda-forge essential_generators

conda install -y -c conda-forge xlsxwriter

```

3. Download spacy: `python3.9 -m spacy download de_core_news_lg`

Getting started

After installation, you can execute the bash script `bash run.sh` in the terminal to compile MTF questions for the provided textbook chapters.

To create MTF questions for your own texts use the following command:

`python3 main.py --answers 1 --similarity 0.66 --input ./<path>/<to>/<your>/<textbook>.txt`

The parameter `answers` indicates how many false answers should be generated.

By configuring the parameter `similarity` you can determine what portion of a sentence should remain the same. The remaining portion will be extracted and used to generate a false part of the sentence.

## History and roadmap

* Outlook third iteration: Automatic augmentation of text chapters with generated questions

* Second iteration: Generation of multiple true-false questions with improved text summarizer and German GPT2 sentence generator

* First iteration: Generation of multiple true false questions in the Bachelor thesis of Mirjam Wiemeler

Publications, citations, license

Publications

• Kasakowskij, R., Kasakowskij, T. & Seidel, N., (2022). Generation of Multiple True False Questions. In: Henning, P. A., Striewe, M. & Wölfel, M. (Hrsg.), 20. Fachtagung Bildungstechnologien (DELFI). Bonn: Gesellschaft für Informatik e.V.. (S. 147-152). DOI: [10.18420/delfi2022-026](https://dl.gi.de/handle/20.500.12116/38826)

Citation of the Dataset

• Kasakowskij, R., Kasakowskij, T., & Seidel, N. (2022). Dataset for Generation of multiple true false questions. Zenodo. https://doi.org/10.5281/zenodo.7303300

The source code and data are maintained at GitHub: https://github.com/D2L2/multiple-true-false-question-generation

Contact

• Regina Kasakowskij (M.A.) - regina.kasakowskij@fernuni-hagen.de
• Dr. Niels Seidel - niels.seidel@fernuni-hagen.de

License Distributed under the MIT License. See [LICENSE.txt](https://gitlab.pi6.fernuni-hagen.de/la-diva/adaptive-assessment/generationofmultipletruefalsequestions/-/blob/master/LICENSE.txt) for more information.

Acknowledgments This research was supported by CATALPA - Center of Advanced Technology for Assisted Learning and Predictive Analytics of the FernUniversität in Hagen, Germany.

This project was carried out as part of research in the CATALPA project [LA DIVA](https://www.fernuni-hagen.de/forschung/schwerpunkte/catalpa/forschung/projekte/la-diva.shtml)