Project description

Video-SRS is an interdisciplinary project that focuses on supporting video learning in mathematics by exploring and improving self-regulation. It combines insights from cognitive and educational psychology, mathematics education, and computer science to develop innovative approaches to this type of learning. The use of responsible machine learning algorithms plays a significant role in this process.

Specifically, the collaborative project aims to identify and address self-regulation problems in video learning by automatically recognizing when such problems occur and providing appropriate and automated assistance. Similarly, the project aims to recognize suboptimal characteristics of instructional videos that could assist learners in selection and creators in the production of better videos. On the educational side, the project focuses on the study of learning for derivation, as there is a great need for learning in this area among German students.
Methodologically, the project not only utilizes machine learning techniques, but also analyzes video materials, log file data from video platforms, and multimodal sensor data from individual video learners, such as eye movement data. Where possible, these analyses are triangulated to make the best possible statements about self-regulation problems and their solutions.
The goal of the project is both to deepen theoretical insights into the role of self-regulation in video learning and to obtain practical approaches for optimizing learning.

Cooperations

• Prof. Dr. Enkelejda Kasneci, Computer Science Department, University of Tübingen 

• Prof. Dr. Nico Pfeifer, Computer Science Department, University of Tübingen 

• Prof. Dr. Ulrich Trautwein, Hector Research Institute of Education Sciences and Psychology, University of Tübingen 

• Prof. Dr. Ralph Ewerth, Leibniz Information Centre for Science and Technology, Hannover 

• Prof. Dr. Aiso Heinze, Leibniz-Institute for Science and Mathematics Education, Kiel