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.
| ISBN: | 9783658392024 |
| Publication date: | 15th October 2022 |
| Author: | Robin Richner |
| Publisher: | Springer Gabler an imprint of Springer Fachmedien Wiesbaden |
| Format: | Paperback |
| Pagination: | 88 pages |
| Series: | BestMasters |
| Genres: |
Business innovation Teacher training |
Teachers spend a great amount of time grading free text answer type questions. To encounter this challenge an auto-grader system is proposed.
Auto-Grader features in the following genres: Business innovation, Teacher training
Paperback. £40.49, down from the £44.99 cover price. Not Available.
Auto-Grader was written by Robin Richner and published by Springer Gabler an imprint of Springer Fachmedien Wiesbaden
Auto-Grader has 88 pages
Yes it is part of BestMasters series
£40.49, reduced from £44.99. Not Available.