Adaptive Recommendations to foster Social Media Skills in Teaching and Learning Scenarios

Christina Di Valentin; Andreas Emrich; Johannes Lahann; Michael Schmidt; Uta Schwertel; Dirk Werth; Peter Loos

In: Proceedings of the International Conference on Knowledge Technologies and Data-driven Business. International Conference on Knowledge Technologies and Data-driven Business (i-know-14), 14th, September 16-19, Graz, Austria, ACM, 9/2014.


The use of social media in the private context is increasing. However, when it comes to integrate social media into everyday vocational life many persons still lack the necessary skills. One reason is that the training of social media skills is not yet suffciently integrated into the processes of vocational education. Both, students and teachers would therefore benefit from tailored access to online educational resources (e.g. concepts, training materials, lesson plans, tools etc.) which support their professional use of social media. This paper presents a web-based recommender system, called Social Navigator, providing access to respective educational resources for teachers, trainers and students of vocational education. The system adapts to the social media skills of all stakeholders and recommends appropriate online resources. In doing so, the system helps to individually foster the social media skills of all stakeholders which are classified into the ability to search, select, manage, create, communicate and comment information in the social web. First, the concept of the Social Navigator is explained followed by the filters and rankers that are needed to adjust search and recommendations results according to each individual's social media skill level.


Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence