@inproceedings{Schoen_2014a,
          volume = {1227},
          author = {Daniel Sch{\"o}n and Steffen Sikora and Stephan Kopf and Wolfgang Effelsberg},
         address = {Aachen},
           title = {GLA: A Generic Analytics Tool for e-Learning},
       publisher = {RWTH},
         journal = {CEUR workshop proceedings},
           pages = {112--115},
            year = {2014},
             url = {https://ub-madoc.bib.uni-mannheim.de/37747/},
        abstract = {Several software applications are used at the University of Mannheim for learning and teaching purposes. The majority of them, like lecture feedback, quizzes, forums, and wikis, are hosted within our learning management system ILIAS. In addition, we run several prototypes of serious games and mobile feedback systems. While the data generated by students and teachers is mainly used for current courses, it could be further used for Learning Analytics if it was stored in an adequate format. Considering the variable and fast-moving nature of our learning applications, we invented a concept for a generic database structure, that can handle analyses on a variety of original tools. This paper presents the prototype application GLA (Generic Learning Analytics), which tries to provide a step in the right direction. Data from wikis, forums, quizzes and serious games transformed into one homogeneous format that can be used to do comparable analyzes. Beside comparing several semesters and courses of one application, we can also match related data sets e. g. user behavior between a wiki and a file upload.}
}