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Toward the Automatic Assessment of Text Exercises

Exercises are an essential part of learning. Manual assessment of exercises requires efforts from instructors and can also lead to quality problems and inconsistencies between assessments. Especially with growing student populations, this also leads to delayed grading, and it is more and more difficult to provide individual feedback. The goal is to provide timely responses to homework submissions in large classes. By reducing the required efforts for assessments, instructors can invest more time in supporting students and providing individual feedback. This paper argues that automated assessment provides more individual feedback for students, combined with quicker feedback and grading cycles. We introduce a concept for automatic assessment of text exercises using machine learning techniques. Also, we describe our plans to use this concept in a case study with 1900 students.