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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.

Grading and marking (Students) --- Data processing. --- Graded schools --- Marking (Students) --- Students --- Educational tests and measurements --- Examinations --- School reports --- Grading and marking --- Interpretation --- Rating of --- Technological innovations. --- Teachers --- Innovation and Technology Management. --- Teaching and Teacher Education. --- Training of. --- Teacher education --- Teacher training --- Teachers, Training of --- Breakthroughs, Technological --- Innovations, Industrial --- Innovations, Technological --- Technical innovations --- Technological breakthroughs --- Technological change --- Creative ability in technology --- Inventions --- Domestication of technology --- Innovation relay centers --- Research, Industrial --- Technology transfer