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Master Thesis Process Mining for Learning Analytics

In this master thesis, the student should analyze a data set of learner data from a massive open online course provided by an industry partner. The aim is to investigate potential insights, learning paths in text and log data and thus contribute to new perspectives of learning analytics by using data science and process mining techniques.

Traditional large-scale lectures or the growing field of distance learning scenarios such as massive open online courses (MOOCs, Seaman et al. 2018) are the reality for many students. Learning Analytics are used to better understand learning paths of students and help them to receive a more individual learning experience based on scalable techniques such as Machine Learning or Natural Language Processing.


In this master thesis, the student should analyze a data set of learner data from a massive open online course provided by an industry partner. The aim is to investigate potential insights, learning paths in text and log data and thus contribute to new perspectives of learning analytics by using data science and process mining techniques.

You should…

- have a solid understanding of data analytics
- have an understanding of Python (or at least be willing to learn and work with it)
- work in an independent and accurate way
- have scientific writing skills in English
- be passionate about digitalization, data science and algorithms

We offer you a close supervision and the opportunity to develop practical, as well as theoretical skills in the area of data science. You will work in cooperation with the leading process mining company. If you are interested, send your CV, transcript of records and a brief description of your motivation to thiemo.wambsganss@unisg.ch.



Publiziert von thiemo.wambsganss@unisg.ch

Kontakt: Thiemo Wambsganss