ReMashed – Recommendations for Mash-Up Personal Learning Environments

Abstract

Abstract.The following article presents a Mash-Up Personal Learning Environment called ReMashed that recommends learning resources from emerging information of a Learning Network. In ReMashed learners can specify certain Web2.0 services and combine them in a Mash-Up Personal Learning Environment. Learners can rate information from an emerging amount of Web2.0 information of a Learning Network and train a recommender system for their particular needs. ReMashed therefore has three main objectives: 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.

Supporting Collaborative Innovation Networks for New Concept Development Through Web Mashups

“…As supporting platforms for learning contexts, Web Mashups are capable to integrate and enable the learning functions that the learning process depends on (Boss and Krauss, 2007). In the scope of education, several studies (Drachsler et al, 2009) showed the benefits of Web Mashups for the construction of Personal Learning Environments. In the scope of KM, the literature shows practices of analysis (Weber et al, 2008) and attempts of use of Web mashups in the development of KM tools (Bitzer et al, 2009).…”

Towards a Social Trust-Aware Recommender for Teachers

“…So, we aim to support teachers to Find Novel Resources that are suitable for them based on their profile history. Most of the recommender systems in the educational domain have been designed to support this task (Drachsler et al 2009;Lemire et al 2005;Rafaeli et al;Recker et al 2003;Tang and McCalla 2003). For more examples, see the book by .…”

“…The majority of the recommender systems generate recommendations in the form of suggestions on content or people, or sometimes ratings (Beham et al 2010;Drachsler et al 2009;Recker et al 2003). Another common output of recommender systems is predictions of a rating value that a user would give to an item (Schafer et al 2007).…”

Trust-based rating prediction for recommendation in Web 2.0 collaborative learning social software

“…For the purpose of evaluating trust-based rating prediction approach, the proposed model is applied on the dataset of Web 2.0 collaborative learning social software, namely Remashed (remashed.ou.nl) [6]. Remashed is an informal learning environment that gathers the public items of users' Web 2.0 services such as SlideShare, Delicious, Flickr, or Twitter.…”