Online educational platforms are promising to play a primary role in mediating the success of individuals’ careers. Hence, while building overlying content recommendation services, it becomes essential to ensure that learners are provided with equal learning opportunities, according to the platform values, context, and pedagogy. Even though the importance of creating equality of learning opportunities has been well investigated in traditional institutions, how it can be operationalized scalably in online learning ecosystems through recommender systems is still under-explored. In this paper, we formalize principles, that aim to model a range of learning opportunity properties in recommendations, and a metric that combines them to quantify the equality of learning opportunities among learners. Then, we envision a scenario wherein platform owners seek to guarantee that the generated recommendations meet each principle, to a certain degree, for all learners, constrained to their individual preferences. Under this view, we provide observations on learning opportunities in a real-world online dataset, highlighting inequalities among learners. To mitigate this effect, we propose a post-processing approach that balances personalization and learning opportunity equality in recommendations. Experiments on a large-scale dataset demonstrate that our approach leads to higher equality of learning opportunity, with a small loss in personalization.