The LAPS Project: Using Machine Learning Techniques for Early Student Support

  • Mathias HinkelmannEmail author
  • Tobias Jordine


This chapter introduces the LAPS project, which is able to analyze progressions of former students and to make statements on possible risks for currently enrolled students by using machine learning techniques. It is also possible to get information about courses, lectures, and student cohorts. The project does not only include a web-based software, it also consists of a student consultation process which respects students’ self-responsibility, individuality, confidentiality, and anonymity. This chapter provides insights how the project is technically developed at the Hochschule der Medien, Stuttgart, Germany, and how it can be used in consultation situations.


Early student support Machine learning Risk calculation Student consultation process Privacy 



The LAPS team likes to thank the developers of S-Beat,2 namely, Dominik Herbst, Niclas Steigelmann, and Annkristin Stratmann, which served as the technical foundation for the feasibility study. Additionally, we like to thank the authors of the “Softwaregestützte Studienverlaufsanalyse zur frühzeitigen gezielten Studienberatung” article, namely, Prof. Dr. Johannes Maucher and Prof. Dr. Tobias Seidl, which served as a foundation for this book chapter. Finally, we like to thank the “Digital Innovations for Smart Teaching—Better Learning” promotional program for allowing us to further improve the project.


  1. 1st International Conference on Learning Analytics and Knowledge 2011. (2010, July 22). Retrieved February 26, 2018, from
  2. Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In ACM sigmod record (Vol. 22, pp. 207–216). New York: ACM.Google Scholar
  3. Christensen, B., & Meier, J.-H. (2014). Zur Frühidentifikation von Studienabbrüchen. Das Hochschulwesen, 6, 182–185.Google Scholar
  4. Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), 304. CrossRefGoogle Scholar
  5. General Data Protection Regulation (2018). (EU) 2016/679 §. Retrieved from
  6. Hermann, C., & Ottmann, T. (2006). StuVa–Werkzeugunterstützte Studienverlaufsanalyse zur Unterstützung der Studienberatung. In HDI (pp. 127–136).Google Scholar
  7. Jaeger, M., & Sanders, S. (2009). Kreditpunkte als Kennzahl für die Hochschulfinanzierung. In Hannover: HIS–Forum Hochschule.Google Scholar
  8. Siemens, G. (2010, August 25). What are learning analytics? Retrieved February 25, 2018, from
  9. Trapmann, S., Hell, B., Weigand, S., & Schuler, H. (2007). Die Validität von Schulnoten zur Vorhersage des Studienerfolgs - eine Metaanalyse. Zeitschrift für Pädagogische Psychologie, 21(1), 11–27. CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and MediaHochschule der MedienStuttgartGermany

Personalised recommendations