Learning Paths in a Non-Personalizing e-Learning Environment 1 Agathe Merceron, Sebastian Schwarzrock Beuth Hochschule für Technik Berlin Amrumer Straße 10 1353 Berlin, Germany {merceron, sschwarzrock}@beuth- hochschule.de Margarita Elkina, Andreas Pursian Hochschule für Wirtschaft und Recht Alt Friedrichsfelde 60 10315 Berlin, Germany {margarita.elkina, andreas.pursian}@hwr- berlin.de Liane Beuster, Albrecht Fortenbacher, Leonard Kappe, Boris Wenzlaff Hochschule für Technik und Wirtschaft Wilhelminenhofstraße 75 12459 Berlin, Germany {liane.beuster, albrecht.fortenbacher, kappe, boris.wenzlaff}@htw-berlin.de ABSTRACT The project LeMo (monitoring of learning processes on personalizing and non-personalizing e-Learning environments) aims to develop a prototype of a web based Educational Data Mining application, which shall provide detailed information on user pattern within e-Learning environments and identify needs of enhancement and revision of the learning offer. The poster presents a case study of analysis of learning paths in a non-personalizing e- Learning environment. Research data have been obtained on the base of log-files during three arbitrarily chosen days. Keywords Learning paths, non personalizing, e-Learning environment. 1. INTRODUCTION The LeMo project [1] is an interdisciplinary research project situated in the field of learning analytics, information science, psychology and data privacy. In order to obtain information about user patterns, as well as about the quality and optimization of e- Learning offers, we integrate in our tool several methods of data mining: association, sequential patterns, regression analysis etc. The tool will be used by eLearning providers, lecturers that use eLearning in different ways, writers of eLearning content and scientists in this field. The main goals for the prototype development are: a data source agnostic back-end, a set of analysis components and a dynamic and adaptive graphical user interface with strong emphasis on an intuitive and easy usability of the application. The prototype focuses on the e-Learning provider role. 2. THE TOOL Being agnostic, the prototype will support different major e- Learning environments rather than a specific one. e-Learning environments can be classical learning management systems like Moodle, where a login personalizing the user is required for access, or online encyclopedias like ChemgaPedia [2] that are non- personalizing environments, where neither login nor registration is needed to access content. To the best of our knowledge, this feature is unique. Connectors import user data from a specific e-Learning platform into a common data base used for analysis (Fig. 1). Currently two prototype connectors have been implemented, one for Moodle and another one for ChemgaPedia. Connectors for non-personalizing environments or online encyclopedias like ChemgaPedia have to remove fake user data that has been generated by web robots. Currently we have taken a quite cautious approach that might result in suppressing more user data than necessary. Figure 1. System architecture The methodical guideline for the analysis components and for the adaptive graphical user interface is a catalog of more than eighty questions and research hypotheses collected from our university and business partners. These hypotheses and questions express the information that their authors would like to get from the users' data. The questions of this catalog can be divided into six groups assigned to six topics of analysis: 1. the learning environment, 2. usage of the learning environment, 3. user and groups of users, 4. learning performance, 5. learning paths through the learning environment and 6.communication tools. Currently we are developing analysis components for topics 2 and 5. In particular we have implemented a component to extract learning paths of learners through the resources of courses. A path is a sequence of resources or learning objects ordered by acces time. Each resource on the path is labeled by its name, a time stamp and a duration. The duration is simply the difference between the time stamp of the present resource and of the following one. The last resource of a path does not have any 1 This work is partially supported by IFAF and the European Regional Development Fund for the Berlin state.