@inproceedings{2553bf6c26d54717a62fdc58d08c5aac,
title = "Use of contextualized attention metadata for ranking and recommending learning objects",
abstract = "The tools used to search and find Learning Objects in different systems do not provide a meaningful and scalable way to rank or recommend learning material. This work propose and detail the use of Contextual Attention Metadata, gathered from the different tools used in the lifecycle of the Learning Object, to create ranking and recommending metrics to improve the user experience. Four types of metrics are detailed: Link Analysis Ranking, Similarity Recommendation, Personalized Ranking and Contextual Recommendation. While designed for Learning Objects, it is shown that these metrics could also be applied to rank and recommend other types of reusable components like software libraries.",
keywords = "Attention metadata, Learning objects, Ranking, Recommending",
author = "Xavier Ochoa and Erik Duval",
year = "2006",
doi = "10.1145/1183604.1183608",
language = "English (US)",
isbn = "159593524X",
series = "Proceedings of CAMA 2006: 1st International ACM Workshop on Contextualized Attention Metadata: Collecting, Managing and Exploiting of Rich Usage Information",
pages = "9--15",
booktitle = "CIKM 2006 Workshop - Proceedings of CAMA 2006",
note = "CAMA 2006: International Workshop on Contextualized Attention Metadata: Collection, Management and Analysis of Rich Usage Information Conference on Information and Knowledge Management, held in conjunction with the ACM 15th Conference on Information ; Conference date: 10-11-2006 Through 11-11-2006",
}