Richner Auto-Grader - Auto-Grading Free Text Answers

1. Auflage 2022
ISBN: 978-3-658-39203-1
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 96 Seiten

Reihe: BestMasters

Teachers spend a great amount of time grading free text answer type questions. To encounter this challenge an auto-grader system is proposed. The thesis illustrates that the auto-grader can be approached with simple, recurrent, and Transformer-based neural networks. Hereby, the Transformer-based models has the best performance. It is further demonstrated that geometric representation of question-answer pairs is a worthwhile strategy for an auto-grader. Finally, it is indicated that while the auto-grader could potentially assist teachers in saving time with grading, it is not yet on a level to fully replace teachers for this task.

Zielgruppe

Research

Autoren/Hrsg.

Richner, Robin

Inhaltsverzeichnis

Introduction.- Research design.- Research background.- Data.- Model development.- Evaluation.- Discussion, limitations and further research.- Conclusion.

Über Autor(innen)

Robin Richner was working as a Machine Learning Engineer in the edtech industry exploring ways to help teachers in their daily life. He now moved on to the web3 industry.