Modern digital world has enormous amount of data on the Web easily accessible anywhere and anytime. This ease of access also creates new paradigms of education and learning. The modernday learners have access to lot many and in fact one of the best learning materials created in any part of the world. However, despite abundant availability of material, we still lack appropriate systems that can automatically identify learning needs of a user and present them with the most relevant (and

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... ty) material to pursue. This paper presents our algorithmic design towards this goal. We propose a text processing-based system that works in three phases: (a) identifying learning needs of a learner; (b) retrieving relevant materials and ranking them; and (c) presenting material to learner and monitoring the learning process. We use know-how of text processing, information retrieval, recommender systems and educational psychology and presents useful and relevant learning material (including slides, videos, articles etc.) to a learner in a focused subject domain. Our initial experiments have produced promising results. We are working towards a Web-scale deployment of the system.