Many courses suffer from low cognitive activation of the students. This can be addressed with assignments that students complete in and/or between sessions. However, especially in larger courses it is hardly possible to give all students adaptive, elaborated and process-related feedback on their work. Therefore, a generic Stud.IP plugin will be developed to enable structured forms of feedback by tutors (incl. lecturers) and peers. Based on findings from feedback research, instructors should be able to use the plugin to determine (a) who gives feedback to whom on which task, (b) which criteria should be taken into account, and (c) how the feedback is processed by the recipients. Subject-specific catalogs of effective feedback are developed as text modules that enable efficient feedback presentation, which is also conducive to a favorable error culture. Initially, the Faculty of Applied Computer Science, the Faculty of Mathematics, Natural Sciences and Technology, and the Faculty of Economics are involved, since it is precisely here that large lectures are offered that benefit from an adaptive and at the same time efficient feedback system. The transfer in the third year of the project will take place to the Faculty of Philological-Historical Sciences, the Faculty of Philosophical-Social Sciences, the Faculty of Law and the Faculty of Medicine. In this way, the project is creating a feedback tool that can be used flexibly and integrated into Stud.IP, along with a large number of discipline-specific and customizable use cases.

• ​​​​​​ Chair of Psychology
• Chair of Psychology with special emphasis on Educational Psychology
• Prof. Dr. Jennifer Kunz: Controlling
• Professorship for Geoinformatics
• Chair of Materials Engineering
• DigiLLab - Center for Digital Teaching and Learning