A large amount of data about learning processes, progress, outcomes, and learners themselves is created due to increasing digitization in educational institutions and the use of digital learning platforms. New techniques enable the automatic evaluation of data, also known as Learning Analytics (LA). LA is the analysis of large amounts of data about learners, teachers and learning processes to promote more effective learning and teaching. However, discrimination may occur if, for example, learners with lower social status are predicted to have lower learning outcomes, or if the assessment is based not only on the outcome but also on the documented learning process. This project deals with how discrimination according to gender, age, origin or learning type can be promoted or prevented through the use of algorithmic evaluations in digital learning systems and processes. Existing real data will be analyzed with typical algorithms to investigate the extent to which the algorithms are susceptible to discrimination. Besides, it will be experimentally investigated how the abundance of data on learners affects teachers and to what extent this creates an additional potential for discrimination. The perception of the students will also be examined.