Auto-Grader - Auto-Grading Free Text Answers
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.
Introduction.- Research design.- Research background.- Data.- Model development
Evaluation.- Discussion, limitations and further research.- Conclusion.Richner, Robin
| ISBN | 978-3-658-39202-4 |
|---|---|
| Medientyp | Buch |
| Auflage | 1st ed. 2022 |
| Copyrightjahr | 2022 |
| Verlag | Springer, Berlin |
| Umfang | XIII, 96 Seiten |
| Sprache | Englisch |
