  Effective human learning depends on a wide selection of educational materials
that align with the learner's current understanding of the topic. While the
Internet has revolutionized human learning or education, a substantial resource
accessibility barrier still exists. Namely, the excess of online information
can make it challenging to navigate and discover high-quality learning
materials. In this paper, we propose the educational resource discovery (ERD)
pipeline that automates web resource discovery for novel domains. The pipeline
consists of three main steps: data collection, feature extraction, and resource
classification. We start with a known source domain and conduct resource
discovery on two unseen target domains via transfer learning. We first collect
frequent queries from a set of seed documents and search on the web to obtain
candidate resources, such as lecture slides and introductory blog posts. Then
we introduce a novel pretrained information retrieval deep neural network
model, query-document masked language modeling (QD-MLM), to extract deep
features of these candidate resources. We apply a tree-based classifier to
decide whether the candidate is a positive learning resource. The pipeline
achieves F1 scores of 0.94 and 0.82 when evaluated on two similar but novel
target domains. Finally, we demonstrate how this pipeline can benefit an
application: leading paragraph generation for surveys. This is the first study
that considers various web resources for survey generation, to the best of our
knowledge. We also release a corpus of 39,728 manually labeled web resources
and 659 queries from NLP, Computer Vision (CV), and Statistics (STATS).
