• Evangelische Hochschule Nürnberg
  • Hochschule Ansbach
  • Hochschule Augsburg
  • Hochschule Hof
  • Hochschule Neu-Ulm
  • Technische Hochschule Ostwestfalen-Lippe
  • Hochschule Weihenstephan-Triesdorf
  • Open Resources Campus NRW (
  • Virtuelle Hochschule Bayern (vhb)
  • DiZ – Zentrum für Hochschuldidaktik


The Hochschul-Assistenz-System HAnS: An ML-Based Learning Experience Platform (2023)

Autoren: Thomas Ranzenberger, Tobias Bocklet, Steffen Freisinger, Lia Frischholz, Munir Georges, Kevin Glocker, Aaricia Herygers, René Peinl, Korbinian Riedhammer, Fabian Schneider, Christopher Simic, Khabbab Zakaria



The usage of e-learning platforms, online lectures and online meetings for academic teaching  increased during the Covid-19 pandemic. Lecturers created video lectures, screencasts, or audio podcasts for online learning. The Hochschul-Assistenz-System (HAnS) is a learning experience platform that uses machine learning (ML) methods to support students and lecturers in the online learning and teaching processes. HAnS is being developed in multiple iterations as an agile open-source collaborative project supported by multiple universities and partners. This paper presents the current state of the development of HAnS on German video lectures.

Unsupervised Multilingual Topic Segmentation of Video Lectures: What can Hierarchical Labels tell us about the Performance? (2023)

Autoren: Steffen Freisinger, Fabian Schneider, Aaricia Herygers, Munir Georges, Tobias Bocklet, Korbinian Riedhammer



The current shift from in-person to online education, e.g., through video lectures, requires novel techniques for quickly searching for and navigating through media content. At this point, an automatic segmentation of the videos into thematically coherent units can be beneficial. Like in a book, the topics in an educational video are often structured hierarchically. There are larger topics, which in turn are divided into different subtopics. We thus propose a metric that considers the hierarchical levels in the reference segmentation when evaluating segmentation algorithms. In addition, we propose a multilingual, unsupervised topic segmentation approach and evaluate it on three datasets with English, Portuguese and German lecture videos. We achieve WindowDiff scores of up to 0.373 and show the usefulness of our hierarchical metric.

THI Ansprechpartner

Gefördert vom