Pre-selecting Text Snippets to provide formative Feedback in Online Learning

In this paper, a proof of concept is shown to generate formative textual feedback in an online course. The concept is designed to be suitable for teachers with low technical skill levels. As state-of-the-art technology still does not provide high-quality results, the teacher is always held in the loop as the domain expert who is supported by a tool, and not replaced. The paper presents results of our proposed approach for semi-automatic feedback generation using a real-world university seminar, where students create sample micro-learning units as online courses, for which they get feedback for. A supervised machine learning approach is trained based on learner submissions features, and the feedback, that was chosen by teachers in former submissions. The results are promising.