  Online educational platforms are playing a primary role in mediating the
success of individuals' careers. Therefore, while building overlying content
recommendation services, it becomes essential to guarantee that learners are
provided with equal recommended learning opportunities, according to the
platform values, context, and pedagogy. Though the importance of ensuring
equality of learning opportunities has been well investigated in traditional
institutions, how this equality can be operationalized in online learning
ecosystems through recommender systems is still under-explored. In this paper,
we formalize educational principles that model recommendations' learning
properties, and a novel fairness metric that combines them in order to monitor
the equality of recommended learning opportunities among learners. Then, we
envision a scenario wherein an educational platform should be arranged in such
a way that the generated recommendations meet each principle to a certain
degree for all learners, constrained to their individual preferences. Under
this view, we explore the learning opportunities provided by recommender
systems in a large-scale course platform, uncovering systematic inequalities.
To reduce this effect, we propose a novel post-processing approach that
balances personalization and equality of recommended opportunities. Experiments
show that our approach leads to higher equality, with a negligible loss in
personalization. Our study moves a step forward in operationalizing the ethics
of human learning in recommendations, a core unit of intelligent educational
systems.
