  Course selection is challenging for students in higher educational
institutions. Existing course recommendation systems make relevant suggestions
to the students and help them in exploring the available courses. The
recommended courses can influence students' choice of degree program, future
employment, and even their socioeconomic status. This paper focuses on
identifying and alleviating biases that might be present in a course
recommender system. We strive to promote balanced opportunities with our
suggestions to all groups of students. At the same time, we need to make
recommendations of good quality to all protected groups. We formulate our
approach as a multi-objective optimization problem and study the trade-offs
between equal opportunity and quality. We evaluate our methods using both
real-world and synthetic datasets. The results indicate that we can
considerably improve fairness regarding equality of opportunity, but we will
introduce some quality loss. Out of the four methods we tested, GHC-Inc and
GHC-Tabu are the best performing ones with different advantageous
characteristics.

Computers and Society;Machine Learning