A folksonomy-based recommender system for learning material prediction Benedikt Engelbert 1 . Karsten Morisse 1 , Oliver Vornberger 2 1 Faculty of Engineering and Computer Science University of Applied Sciences Osnabrück Germany {b.engelbert, k.morisse}@hs-osnabrueck.de 2 University of Osnabrück Department of Mathematics/Computer Science Germany oliver@uos.de Abstract: The Internet is a network where information, data and services can be accessed rapidly. Also in the area of eLearning it is common to access your learning material online to speed up the distribution and keep the retrieve of documents easy. Thus, the variety of learning material increases, since teachers provide scripts and slides, but also further online materials and services like lecture recordings or audio podcasts. To choose from a wide- ranging pool of material seems to be an advantage at first, but can also lead to disorganization, mental overload, ignorance of unknown material and misunderstanding of content. Many Internet services provide assistive systems so called Recommender Systems (RS), which help users to find the most important or interesting information and to overcome the mental overload. Those services may also be useful in the area of eLearning to counteract those reasons given above. In this paper we present the development of such a RS on the basis of a folksonomy approach to predict learning material in higher education classes and to optimize students learning processes. Introduction The Internet helps us to access information, data and services immediately. Thereby, the offer of content is unlimited and often you’ll find the information you need. This seems to be an advantage at first, but in many cases the Internet user is overwhelmed by the amount of the content, which the user needs to browse through over and over again. Assistive systems are needed, to present the most important/interesting information a user needs. Many Internet services provide so called Recommender Systems (RS) for such purposes. Recommender Systems help users to find new information on the basis of personal information. Which information a user is looking for depends to the application he or she is using. One of the most popular services providing a RS is Amazon. Amazon is an online distributor for a variety of products like books, CDs or DVDs. On the basis of bought items, the RS of Amazon is able to offer new articles within the online shop the user may like. This situation is similar in the area of eLearning. Many higher educational institutions apply Learning Management Systems (LMS) to provide course relevant learning material. Digital learning material is getting more and more important, since the fast and easy distribution simplifies the organization for a teacher, but also simplifies the participation of a course for the students. However, in most cases teachers provide a huge variety of learning materials. The variety is not limited to classical materials like scripts, bibliographies or slides. Also multimedia content like podcasts, video lectures or online tests is common these days. Especially the choice of suitable material is one of the main problems for students. Figure 1 illustrates the variety of learning material in a common LMS.