Learning Progression Analytics – Analyse und Förderung von Lernverläufen zur Entwicklung von Kompetenzen (AFLEK)

Duration: 1/2020 – 10/2023
Funded by: BMBF (Bundesministerium für Bildung und Forschung)
Researchers: Prof. Dr. Nikol Rummel, Dr. Sebastian Strauß, Lena Borgards


  •  Prof. Knut Neumann (IPN Kiel)
  •  Prof. Hendrik Drachsler (Leibniz Institute for Research and Information in Education, Frankfurt)
  •  Prof. Dr. Maren Scheffel (RUB Bochum)

 Project description:

Digital technologies are playing an increasingly important role in educational processes, which has become more important due to the Corona crisis, as learning and the review of learning processes would not have been possible without the new digital possibilities. One goal is to enable personalised learning and the best possible acquisition of competences for students.

The aim of the AFLEK project is to gain insights into learning progressions analytics (LPA) and to identify unproductive learning progressions in a timely manner in order to be able to transform them into productive progressions. For this purpose, learning processes are to be automatically evaluated and the causes of unproductive learning processes are to be identified. Last but not least, an assistance system for teachers will be created, which will show them the individual progress of each students to make personalised learning possible.

The three main research questions of the projects are:

1) Which (process) data from the learning environment are needed to what extent in order to allow reliable and valid conclusions about learning processes and underlying learning difficulties, and how can the data be integrated in order to allow more reliable and valid conclusions about learning and learning difficulties?

2) Which learning trajectories lead to competence development (i.e. are productive) or not (i.e. are unproductive) and which typical learning difficulties can be identified as the cause of unproductive learning?

3) How can learning pathways identified as unproductive and be changed into productive learning pathways through targeted instruction?

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