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OverviewTeachers 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. Full Product DetailsAuthor: Robin RichnerPublisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Imprint: Springer Gabler Edition: 1st ed. 2022 Weight: 0.194kg ISBN: 9783658392024ISBN 10: 3658392029 Pages: 96 Publication Date: 15 October 2022 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: Manufactured on demand  We will order this item for you from a manufactured on demand supplier. Table of ContentsReviewsAuthor InformationRobin 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. Tab Content 6Author Website:Countries AvailableAll regions |
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