Smart learning: Time-dependent context-aware learning object recommendations

In a digital classroom, analysis of students' interactions with the learning media provides important information about users' behavior, which can lead to a better understanding and thus optimizes teaching and learning. However, over the period of a course, students tend to forget the lessons learned in class. Learning predictions can be used to recommend learning objects users need most, as well as to give an overview of current knowledge and the learning level. The representation of time based data in such a format is difficult since the knowledge level of a user with a learning object changes continuously depending on various factors. This paper presents work in progress for a doctoral approach to extend the traditional user-item-matrix of a recommendation engine by a third dimension - the time value. Moreover, in this approach the learning need consists of different context factors each influencing the relevance score of a learning object.