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. About the author Robin 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.

Autorentext

Robin 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.