. The emergence of learning analytics afforded for the analysis of digital traces of user interaction with technology. This analysis offers many opportunities to advance understanding and enhance learning and the environments in which learning occurs.
Presentation used by Tinne De Laet, KU Leuven, for a keynote presentation during an event: organised by Leiden University, Erasmus University Rotterdam, and Delft University of Technology.
The presentations presents the results of two case studies from the Erasmus+ project ABLE and STELA, and provides 9 recommendations regarding learning analytics
K. Fleszar, M. Mnich, and J. Spoerhase. 24th Europ. Symp. Algorithms (ESA'16), volume 57 of Leibniz International Proceedings in Informatics (LIPIcs), page 42:1--42:17. Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, (2016)
N. Goldbaum, M. Krumholz, and J. Forbes. (2016)cite arxiv:1605.00646Comment: 16 pages, 13 figures. Resubmitted to the Astrophysical Journal after responding to referee comments. 6 TB of simulation data and processed derived data are available at http://dx.doi.org/10.13012/J85Q4T1T. An interactive data exploration widget is available at https://demo.use.yt (click "galaxy_visualization.ipynb"; once the Jupyter notebook launches, click "Cell -> Run All").
K. Sokal, K. Johnson, P. Massey, and R. Indebetouw. (2015)cite arxiv:1508.00572Comment: To appear in the conference proceedings of the June 2015 Potsdam Wolf-Rayet workshop, edited by W.-R. Hamann, A. Sander, and H. Todt. 3 pages, 3 figures.
M. Jeon, V. Bromm, A. Pawlik, and M. Milosavljevic. (2015)cite arxiv:1501.01002Comment: 18 pages 14 figures, Submitted to MNRAS, "for associated simulation movies, visit http://www.as.utexas.edu/~myjeon/".