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- Author: Robin Richner
- Publisher: Springer Gabler
- ISBN-10: 3658392029
- ISBN-13: 9783658392024
- Format: 14.4 x 20.8 x 0.8 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
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Description
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.
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- Author: Robin Richner
- Publisher: Springer Gabler
- ISBN-10: 3658392029
- ISBN-13: 9783658392024
- Format: 14.4 x 20.8 x 0.8 cm, minkšti viršeliai
- Language: English English
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.
Reviews