since 2016

Learning Analytics deals with the analysis of data generated during learning processes to support these processes. In LISA, the data come from e-learning systems (online learning magazine, classical learning management system, serious games), and are merged with data from a sensory hardware developed in the project. It collects bio-data of the user (heart activity and skin conductance) and data of the physical environment (light intensity, volume, etc.). Affective Computing techniques allow to derive information about the user`s mental and emotional state (stress, concentration, etc.) from the bio-data. The analytics results are made accessible to users enabling them to monitor and optimize their learning. Also, the e-learning systems will be made adaptable, reacting to the individual user. Because of the highly sensitive data, ELSI issues (ethical, legal and social issues) such as data protection are also part of the project. Other project partners apart from Humboldt-Universität are Hochschule für Technik und Wirtschaft Berlin, the Leibnitz-Institut für Wissensmedien in Tübingen and the companies Promotion Software GmbH, NEOCOMO GmbH and SGM GmbH. LISA is funded by the Federal Ministry of Education and Research (BMBF).




[1] H. Yun, M. Domanska, A. Fortenbacher, N. Pinkwart, M. Ghomi (2016). Sensor Data for Learning Support: Achievements, Open Questions & Opportunities. In R. Zender, ed., Proceedings der Pre-Conference-Workshops der 14. E-Learning Fachtagung Informatik co-located with 14th e-Learning Conference of the German Computer Society (DeLFI 2016) (pp. 28--39). CEUR Workshop Proceedings (