A Semantically Enriched Context-Aware OER Recommendation Strategy and Its Application to a Computer Science OER Repository.

This paper describes a knowledge-based strategy for recommending educational resources—worked problems, exercises, quiz questions, and lecture notes—to learners in the first two courses in the introductory sequence of a computer science major (CS1 and CS2). The goal of the recommendation strategy is to provide support for personalized access to the resources that exist in open educational repositories. The strategy uses: 1) a description of the resources based on metadata standards enriched by ontology-based semantic indexing, and 2) contextual information about the user (her knowledge of that particular field of learning). The results of an experimental analysis of the strategy's performance are presented. These demonstrate that the proposed strategy offers a high level of personalization and can be adapted to the user. An application of the strategy to a repository of computer science open educational resources was well received by both educators and students and had promising effects on the student performance and dropout rates. [ABSTRACT FROM AUTHOR]