Despite an abundance of educational resources on the Web, there exists a gap between teachers and the efficient utilization of these resources. A fundamental component of teaching is the preparation of a lesson plan - an organized sequence of educational content - and for the most part, the task of generating lesson plans today is manual and laborious. To address this gap, we present CollectiveTeach, a platform that enables educators to generate lesson plans. CollectiveTeach has two main facets: (i) an information retrieval engine that gathers relevant documents pertaining to a topic, and (ii) a framework to sequence the retrieved documents into coherent lesson plans. We present a novel architecture that leverages information retrieval algorithms, data mining techniques, and user feedback to generate automated lesson plans. We built and deployed CollectiveTeach for 3 popular undergraduate Computer Science subjects: Algorithms, Operating Systems, and Machine Learning, on a corpus of ∼100,000 web pages. Further, we evaluated the platform in 3 phases: (1) computing the precision of the documents retrieved, (2) a user study with 10 participants who assessed lesson plans returned by CollectiveTeach based on appropriateness, quality, and coverage and (3) benchmarking our sequencing approach against the Beam-Search approach. Our results show that CollectiveTeach achieves high precision in retrieving content relevant to a user's query, users are satisfied with the appropriateness, coverage, and reliability of the generated lesson plans and that our sequencing approach is effective. These results indicate that CollectiveTeach is a promising platform that could enrich the lesson plan generation process and encourage collaboration amongst the community of educators and learners.