Auto-Grader - Auto-Grading Free Text Answers
ISBN:
978-3-658-39202-4
Auflage:
1st ed. 2022
Verlag:
Springer Fachmedien Wiesbaden GmbH, Springer Gabler
Land des Verlags:
Deutschland
Erscheinungsdatum:
15.10.2022
Reihe:
BestMasters
Format:
Softcover
Seitenanzahl:
96
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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.
Schlagwörter
Biografische Anmerkung
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.
Eine projektkulturbewusste Management-Methode für interorganisationale F&E-Projekte
Erscheinungsjahr:
2024
