Auto-Grader - Auto-Grading Free Text Answers
(Sprache: Englisch)
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...
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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.
Inhaltsverzeichnis zu „Auto-Grader - Auto-Grading Free Text Answers “
Introduction.- Research design.- Research background.- Data.- Model development.- Evaluation.- Discussion, limitations and further research.- Conclusion.
Autoren-Porträt von Robin Richner
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.
Bibliographische Angaben
- Autor: Robin Richner
- 2022, 1st ed. 2022, XIII, 96 Seiten, 34 farbige Abbildungen, Maße: 14,8 x 21 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3658392029
- ISBN-13: 9783658392024
Sprache:
Englisch
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