Abstract
The proliferation of online teaching, learning, and assessment resources is hampering efforts to make finding relevant resources easy. Metadata, while valuable for curating digital collections, is difficult to keep current or, in some cases, to obtain in the first place. Social metadata, paradata, usage data, and contextualized attention metadata all refer to data about doing with digital resources that can be harnessed for recommendations. To centralize this data for aggregation and amplification, the Learning Registry, a store and forward, distributed, de-centralized network of nodes was created. The Learning Registry makes it possible for disparate sources to publish learning resource social/attention metadata—data about users of and activity around resources. We describe our experimentation with social metadata, including that which describes alignment of learning resources to U.S. teaching standards, as a means to generate relationships among resources and people, and how it can be used for recommendations.
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Acknowledgments
Walt Grata of the Advanced Distributed Learning Initiative (ADL, U.S Department of Defense) designed and implemented an early version of the learning resource relationship graph described in this chapter. Steve Midgley, US Department of Education, first suggested creating a graph representation of Learning Registry data using Neo4j. SRI International’s work on this project is supported by the US Department of Education (ED-04-CO-0040/0010).
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Bienkowski, M., Klo, J. (2014). The Learning Registry: Applying Social Metadata for Learning Resource Recommendations. In: Manouselis, N., Drachsler, H., Verbert, K., Santos, O. (eds) Recommender Systems for Technology Enhanced Learning. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0530-0_4
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