Automatic classification of didactic functions of e-learning resources

Re-use of digital resources is an important issue in e-Learning scenarios, because only intensive re-use can make e-Learning cost efficient. Besides reusing whole courses, authors often desire to re-use fine grained parts of courses for creating new Learning Resources. The granularity which appears to be most promising for this kind of re-use is the level of information objects. Information objects each have a dedicated didactic function; a set of information objects with different didactic functions are combined into Learning Objects. This paper analyzes how didactic functions of existing information objects can be automatically classified using machine learning technology. The results of such classification methods on a set of Learning Resources from medical science are discussed.