Sentence examples for resources recommendation from inspiring English sources

Educational resources recommendation in BROAD-RSI proposal considers the relations between user profile features and metadata of educational resources.

In this work, our architecture includes, besides the BROAD Learning Objects LOO) Repository, user's profile features and educational context, through the semi-automatic information acquisition provided by Social Networks, enhancing personalized educational resources recommendation.

Design Individual or Group Schematic design with comments e.g. UML diagrams Images, Tutorials, White papers, Design Patterns Recommendations of resources, recommendation of experts, Drawing tool, UML editor, canvas designer etc. Coding Individual or Group Classes and methods of implementation, coding Design Patterns High Level Design, Low Level Design, Web Programming language IDEs.

The use of Semantic Web techniques and standards in education have brought improvements in some aspects, such as customized content and educational resources for students, educational resources recommendation, and data collection regarding the interaction of students with educational environments [34, 35].

These interests can enrich and diversify the educational resources recommendations.

Researchers' positive answers in relation to share educational resources recommendations through social networks showed the possibility of reaching people who are not users of BROAD-RSI.

Analyze BROAD-RSI educational resources recommendations for the purpose of evaluating their adherence from user's point of view in the context of Facebook users, which have interests in technology and express this interest through social network interactions.

Analyze BROAD-RSI educational resources recommendations for the purpose of evaluating their adherence from user's point of view in the context of Facebook users, which have interests in technology and express this interest through social network interactions.  .

The Resources recommendations identified publications or other experts to access.

Then, we propose a resource recommendation method for cloud computing system that integrates price utility, multi-attribute matching metric and group customer evaluation.

To use such a profile for adaptive learning and resource recommendation, we need to be able to compare competencies to help match the competencies of learners with those involved in other learning scenario components (actors, activities, resources).