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The huge quantity of data, media, applications, and services - in one word, resources - that are accumulating day after day on the Web makes it more and more difficult to search the network in an effective and helpful way. We usually spend a lot of the time trying to "filter out" what we consider "noise" added to the "good information" for which we are looking. Whatever the search domain, this messy and discouraging situation cannot be handled by general search engine, such as Google. We also face similar problems in the instructional domain. In this paper we focus on the need for creating tools that can help classifying and retrieving digital Web resources for Computer-Aided Instruction processes. We propose a strong, but simple and flexible, classification scheme, which can be easily and profitably used by a Web community (of teachers, learners, and others) to create a database of references to digital instructional resources. Our classification scheme uses a blended top-down/bottom-up approach with a Delicious-like annotation and tagging system not fully free, but based upon a controlled and predefined and expandable set of metadata.

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How to Cite
Cofrancesco, P., Petrone, M., Bruni, F., & Caldirola, E. (2011). Web resources for Computer-Aided Instruction: a blended classification scheme. Journal of E-Learning and Knowledge Society, 7(3).