Entwicklung eines Prototypen für die wissensbasierte und vertraulichkeitsbewahrende Aufgabenzuteilung in intelligenten tutoriellen Systemen

In today’s society it is hard to imagine learning without the digital world. In order to support students in the learning process, learning platforms are used in many areas to provide learning materials and to monitor the learning progress. However, since not all students learn in the same way, more and more attempts are being made to adapt learning to the needs of the students by means of individual task lists. Intelligent tutorial systems are used for this purpose. In order to individualise the task lists, a large amount of student data is collected. Therefore privacy plays a special role in this area. In this master thesis an experimental standalone prototype was developed, which realises a knowledge-based and privacy-preserving assignment of tasks in intelligent tutorial systems. In doing so, the privacy of the students was preserved by ensuring that information about the students is only accessible by the students themselves and that only the minimum necessary information is shared with the teachers. In addition, various configuration options have been developed for the students, with which they can adapt the learning process to their individual needs.