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Online educational platforms are playing a primary role in mediating thesuccess of individuals' careers. Therefore, while building overlying contentrecommendation services, it becomes essential to guarantee that learners areprovided with equal recommended learning opportunities, according to theplatform values, context, and pedagogy. Though the importance of ensuringequality of learning opportunities has been well investigated in traditionalinstitutions, how this equality can be operationalized in online learningecosystems through recommender systems is still under-explored. In this paper,we formalize educational principles that model recommendations' learningproperties, and a novel fairness metric that combines them in order to monitorthe equality of recommended learning opportunities among learners. Then, weenvision a scenario wherein an educational platform should be arranged in sucha way that the generated recommendations meet each principle to a certaindegree for all learners, constrained to their individual preferences. Underthis view, we explore the learning opportunities provided by recommendersystems in a large-scale course platform, uncovering systematic inequalities.To reduce this effect, we propose a novel post-processing approach thatbalances personalization and equality of recommended opportunities. Experimentsshow that our approach leads to higher equality, with a negligible loss inpersonalization. Our study moves a step forward in operationalizing the ethicsof human learning in recommendations, a core unit of intelligent educationalsystems.

Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations