Open Access Open Access  Restricted Access Subscription Access


Margarita Elkina, Albrecht Fortenbacher, Agathe Merceron


LeMo is an open source application for learning analytics, which collects data about learners' activities from different platforms. This article describes design principles of LeMo in the context of creating an efficient tool for learning analytics. Focus is on the LeMo system architecture, user path analysis employing algorithms of sequential pattern mining, and visualization of learners' activities implemented in the current version. A case stud y shows first results.


Learning analytics; visual analytics; explorative visualization; sequential pattern mining; user path analysis.

Full Text:



L. Johnson et al., The 2011 Horizon Report, The New Media Consortium, Austin, 2011, pdf.

A. Fortenbacher, Learning analytics for higher education – perspectives and challenges, in Pratsi: Scientific, Science and Technology Collected Articles, Issue 1(40), Odessa National Polytechnic University, 2013, pp. 184–187.

1st International Conference on Learning Analytics and Knowledge, ACM Digital Library, 2011.

L. Beuster et al., Learning analytics und visualisierung mit dem LeMo-Tool, in Proceedings of Interaktive Vielfalt – DeLFI, Bremen, 2013.

G. Siemens, Connectivism: Learning Theory of Pasttime of the Selfamused, 2006.

A. Fortenbacher, L. Beuster, M. Elkina, L. Kappe, A. Merceron, A. Pursian, S. Schwarzrock, B. Wenzlaff, LeMo: a Learning Analytics Application Focussing on User Path Analysis and Interactive Visualization, The 7th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2013), 12-14 September 2013, Berlin, Germany, Vol. 2, pp. 748-753.

R. Ferguson, The state of learning analytics in 2012: a review and future challenges, Tech. rep. KMI-12-01, Knowledge Media Institute, The Open University, UK, 2012.

S. Schwarzrock, Analysis of learner navigation on web-based platforms using algorithms for sequential pattern mining, in Pratsi: Scientific, Science and Technology Collected Articles, Issue 1(40), Odessa National Polytechnic University, 2013, pp. 18–21.

V. Ciriani et al., K-Anonymity, in Secure Data Management in Decentralized Systems, Ed. by T. Yu and S. Jajodia, Vol. 33, Advances in Information Security, Springer, Berlin Heidelberg, 2007, pp. 323–353.

J. Wang and J. Han, Efficient mining of frequent closed sequences, in Proceedings of the 20th International Conference on Data Engineering, Boston, MA, USA. 2004.

P. Fournier-Viger, R. Nkambou, and E. Mephu Nguifo, A knowledge discovery framework for learning task models from user interactions in intelligent tutoring systems, in Proceedings of MICAI, Ed. by A. Gelbukh and E. F. Morales, Lecture Notes in Artificial Intelligence, Vol. 5317. Springer-Verlag Berlin Heidelberg, 2008, pp. 765–778.

Jian Pei et al., Mining sequential patterns by pattern-growth: the prefixspan approach, IEEE Transactions on Knowledge and Data Engineering, Vol. 16, Issue 11, 2004, pp. 1424-1440.

B. Shneiderman, The eyes have it: a task by data type taxonomy for information visualizations, in Proceedings of the IEEE Symposium on Visual Languages, 1996, pp. 336–343.

A. Merceron et al., Visual exploration of interactions and performance with LeMo, in Proceedings of the 6th International Conference on Educational Data Mining, Memphis, USA, 2013.

L. Beuster et al., Prototyp einer plattformunabhangigen Learning Analytics Applikation – fokussiert auf Nutzungsanalyse und Pfadanalyse, in E-Learning Symposium 2012 Aktuelle Anwendungen, innovative Prozesse und neueste Ergebnisse aus der E-Learning-Praxis, Ed. by U. Lucke, Universitatsverlag Potsdam, 2012, pp. 69–72.


  • There are currently no refbacks.