Tübingen Center for Digital Education

Supporting distance learners' self-regulation via video: A study on the responsible use of machine learning approaches in education (Video SRS)

Leibniz Institute for Knowledge Media, Department of Computer Science, Hector Institute for Empirical Educational Research, Leibniz Institute for Science and Mathematics Education

The transdisciplinary approach of Video-SRS combines expertise from different scientific disciplines to develop innovative solutions to support distance learning via video. This approach is based on the collaboration of experts from the fields of cognitive and educational psychology, mathematics didactics and computer science. Here, the main concepts and goals of Video-SRS are described in detail:

  1. Analysis of self-regulation (SR) in distance video learning: A central focus of video-SRS is the study of learners' self-regulatory abilities when consuming learning content in the form of videos. This includes research into how learners can manage their own learning processes, exercise self-control and optimise their learning.
  2. Using psychological models: Research in cognitive and educational psychology provides valuable insights into the human psychology of learning. Video-SRS uses these insights to better understand and support learners' self-regulation.
  3. Use of machine learning (ML) technologies: The integration of machine learning technologies makes it possible to analyse large amounts of learning data and identify patterns related to learner self-regulation. This can help identify potential difficulties or challenges in distance learning.
  4. Triangulation of different data sets: Video-SRS uses triangulation of different data sources to provide a comprehensive picture of the distance learning process. These can be, for example, learning analytics, surveys or interviews.
  5. Improving video distance learning: Based on the findings from the analysis of self-regulation, Video-SRS aims to improve video distance learning. This is done by developing innovative instructional and support measures that help learners to learn more effectively.
  6. Automatic detection of SR problems: An important aspect is the automatic detection of problems in learners' self-regulation. When these problems are identified, targeted actions can be taken to support learners.
  7. Improving instructional videos: Video SRS also aims to identify suboptimal features and instructional videos that can interfere with learning. This can help both learners and video producers to improve the quality of instructional videos.

Overall, video SRS aims to increase the effectiveness of distance learning via video by focusing on both learner self-regulation and the design of instructional videos themselves. This transdisciplinary approach allows different perspectives and expertise to be used to develop innovative solutions for distance learning via video.

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