YouTube has become a global platform for learning and teaching. Its design as a social medium, its rapidly growing content and the obscurity of its search and recommendation system, however, frequently leave users with suboptimal results. Trying to give guidance, many professional websites started to publish ranked lists of educational channels. These lists, however, are highly non-conjoint and different in length, which challenges their general usefulness. This study first highlights some aspects and issues related to ranking YouTube’s educational channels by a qualitative and quantitative analysis of 193 lists collected from 101 websites. Then, an iterative multi-algorithm approach is proposed to derive aggregated ranked lists starting from these online lists for three categories: general education, science and history. The aggregated lists were then correlated with surface features of the channels including the channel’s lifetime and the total number of videos, views and subscribers. Also, an alternative rating-based ranking was established by analysing a total of 2900 videos from the different channels. The results show that the aggregated ranked list of science channels has strong correlation with surface channel features. In contrast, the aggregated ranks of history channels are more correlated with viewers’ positive ratings. The aggregated ranks of general education channels neither relate to channel features nor to viewers’ ratings. Based on these findings several remarks and recommendations for the generation, usage, and research on ranked lists and rank aggregation of YouTube’s educational channels are given.