Online education supported by digital courseware will radically alter higher education in ways that we cannot predict. New technologies such as MOOCs and Khan Academy have generated interest in new models for knowledge delivery. The nature of Computer Science content provides special opportunities for computer-supported delivery in both traditional and online classes. Traditional CS textbooks are likely to be replaced by online materials that tightly integrate content with visualizations and automatically assessed exercises. We refer to these new textbook-like artifacts as icseBooks (pronounced "ice books"), for interactive computer science electronic books. IcseBook technology will in turn impact the pedagogy used in CS courses. This report surveys the state of the field, addresses new use cases for CS pedagogy with icseBooks, and lays out a series of research questions for future study.
Recommender systems using collaborative filtering are a popular technique for reducing information overload and finding products to purchase. One limitation of current recommenders is that they are not portable. They can only run on large computers connected to the Internet. A second limitation is that they require the user to trust the owner of the recommender with personal preference data. Personal recommenders hold the promise of delivering high quality recommendations on palmtop computers, even when disconnected from the Internet. Further, they can protect the user's privacy by storing personal information locally, or by sharing it in encrypted form. In this article we present the new PocketLens collaborative filtering algorithm along with five peer-topeer architectures for finding neighbors. We evaluate the architectures and algorithms in a series of offline experiments. These experiments show that Pocketlens can run on connected servers, on usually connected workstations, or on occasionally connected portable devices, and produce recommendations that are as good as the best published algorithms to date.
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