ReMashed - An Usability Study of a Recommender System for Mash-Ups for Learning

Abstract

The following article presents a Mash-up Personal Learning Environment called ReMashed that recommends items from the emerging information of a Learning Network. In ReMashed users can specify certain Web 2.0 services and combine them in a Mash-Up Personal Learning Environment. The users can rate information from an emerging amount of Web 2.0 information of a Learning Network and train a recommender system for their particular needs. ReMashed therefore has three main goals: 1. to provide a recommender system for Mash-up Personal Learning Environments to learners, 2. to offer an environment for testing new recommendation approaches and methods for researchers, and 3. to create informal user-generated-content data sets that are needed to evaluate new recommendation algorithms for learners in informal Learning Networks.