New Perspectives in Science Education

Edition 13

Accepted Abstracts

Learning Analytics for Student’s Motivation

Margarita Elkina, HWR Berlin (Germany)


Methods of Learning Analytics are gaining ever broader usage and significance in the interpretation of big volumes of studies-related data. Such data, recovered from backlogs of digital learning media, can assist monitoring the behavior of students in online platforms. Continuous analysis of this information allows the teaching stuff to evaluate the efficiency of existing teaching techniques and to formulate the requirements to new didactic methods that should improve the learning process and ensure the learning success of students. Therefore the patterns of studies in learning analytics are usually specially tailored to serve the teaching stuff. However, for the students it can be interesting as well to learn how, on the base of monitoring and interpretation of their own behavior and activities in online platforms, they can gain more information about the pace of their own learning, compared to their classmates, and what they can and should do in order to raise their motivation and in that way to enhance their own learn success. In this paper we have studied the interest of the students regarding the introduction of the methods of learning analytics into the teaching process. We present the results of the survey conducted among the students of the HWR Berlin as well as extensions of the software for the Learning Management System Moodle that were elaborated and implemented by the group of students, basing on the results of the survey. We also discuss the aspects of privacy protection in the context of the personal information employed. 


[1] Graf. S., Ives, C., Rahman, N. and Ferri, A. AAT – A Tool for Accessing and Analysing Students’Behaviour Data in Learning Systems, in Proceedings of the 1st International Conference on Learning Analytics and Knowledge (Banff, Alberta, Canada, February 27 – March 01, 2011), LAK 2011, ACM New York, 174-179.
[2] MacFayden, L.P., Sorenson P. Using LiMS (the Learner Interaction Monitoring System) to Track Online Learner Engagement and Evaluate Course Design. In Proceedings of the 3rd International Conference on Educational Data Mining (June 2010), CMU, Pittsburgh, USA, 301-302.
[3] Margarita Elkina, Albrecht Fortenbacher, Agathe Merceron: The Learning Analytics Application Lemo – Rationals And First Results, International Journal of Computing, 12(3) 2013, 226-234.

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