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Beschreibung
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.
Details
| Verlag | Springer Fachmedien Wiesbaden GmbH |
| Ersterscheinung | Oktober 2022 |
| Maße | 21 cm x 14.8 cm |
| Gewicht | 194 Gramm |
| Format | Softcover |
| ISBN-13 | 9783658392024 |
| Auflage | 1st ed. 2022 |
| Seiten | 96 |
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