BestMasters

Richner, Robin

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

Published by: Springer Gabler

Publication Date: 2022-10-15

Format: Paperback

ISBN-13: 9783658392024

DOI: 10.1007/978-3-658-39203-1

Dimensions: 210cm x148cm

Pages: 96