In education we can find millions of video, audio and text educational materials in different formats and languages. This variety and multimodality can impose difficulty on both students and teachers since it is hard to find the right materials that match their learning preferences. This paper presents an approach for retrieving and recommending items of different modalities. The main focus is on the retrieving and preprocessing pipeline, while the recommendation engine is based on the k-nearest neighbor method. We focus on educational materials, which can be text, audio or video, but the proposed procedure can be generalized on any type of multi-modal data.