Auto-Grader - Auto-Grading Free Text Answers / BestMasters (PDF)
(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.
About the author
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.
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, 96 Seiten, Englisch
- Verlag: Springer Fachmedien Wiesbaden
- ISBN-10: 3658392037
- ISBN-13: 9783658392031
- Erscheinungsdatum: 14.10.2022
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
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- Dateiformat: PDF
- Größe: 2.86 MB
- Ohne Kopierschutz
- Vorlesefunktion
Sprache:
Englisch
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