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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.
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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