In this digital age, there is an abundance of online educational materials in public and proprietary platforms. To allow effective retrieval of educational resources, it is a necessity to build keyword-based search engines over these collections. In modern Web search engines, high-quality rankings are obtained by applying machine learning techniques, known as learning to rank (LTR). In this article, our focus is on constructing machine-learned ranking models to be employed in a search engine in the education domain. Our contributions are threefold. First, we identify and analyze a rich set of features (including click-based and domain-specific ones) to be employed in educational search. LTR models trained on these features outperform various baselines based on ad-hoc retrieval functions and two neural models. As our second contribution, we utilize domain knowledge to build query-dependent ranking models specialized for certain courses or education levels. Our experiments reveal that query-dependent models outperform both the general ranking model and other baselines. Finally, given well-known importance of user clicks in LTR, our third contribution is for handling singleton queries without any click information. To this end, we propose a new strategy to "propagate" click information from the other, similar, queries to the singleton queries. The proposed click propagation approach yields a better ranking performance than the general ranking model and another baseline from the literature. Overall, these findings reveal that both the general and query-dependent ranking models, trained using LTR approaches, yield high effectiveness in educational search, which may ultimately lead to a better learning experience.