Department of Informatics and Digital Education

Technology Enhanced Learning

Identifying and validating the fundamentals of technology-enhanced teaching and learning environments.

Focus areas

The TEL research working group investigates the effectiveness of technology-supported teaching and learning environments, analyses, by means of video-based reflection, how prospective teachers are supported in learning, and validates tools for recording and promoting computational thinking.

• Empirical educational research, learning, and instruction,
• Basic research in computational thinking,
• Educational pattern mining,
• Video-based reflection.

Lead: Dr. Nadine Schlomske-Bodenstein

Teaching and Learning Lab for Informatics Education

Research-based and innovative teaching methodology in the field of Informatics and explorative testing of digital artefacts.

Focus areas

• Pupils' areas of interest and teaching: Determining areas and levels of interest, designing methods to address main areas of interest
• Misconceptions of basic programming concepts such as loops/variables: Investigating common misconceptions in block-based coding
• Mathematical and theoretical foundations of Informatics
• Fundamentals of theoretical and applied Informatics at the intersection of Informatics, Mathematics, and Art
• Creative coding as a field of application for computing lessons
• Extension of existing didactic concepts and development of sustainable mental models and corresponding translation processes
• Algorithmic thinking and computational behaviour, semantic waves, notional machines, block-based programming languages (and text-based programming languages) also in the context of physical computing (robots, microcontrollers, robot arms).

Lead: Dr. Frauke Ritter

Virtually all forms of digital education result in at least some form of cognitive load. For instance, interactive simulations and serious games require learners to familiarize themselves with the controls and other aspects not directly related to the learning content. Influential theories of learning suggest that such demands would lead to diminished learning, while several studies suggest that cognitive load does not necessarily lead to negative effects. The approach of cognitive load alignment holds that introducing cognitive load in digital learning can be justified if the digital learning components are in alignment with the learning objective. For example, in order to learn a complex procedure digitally, it is likely to be worthwhile to risk the cognitive load resulting from learning the controls of the digital environment.

Current publications:
Dechamps, T., & Skulmowski, A. (2025). Learning with erroneous visualizations modulates retention depending on perceptual richness and test type. Trends in Neuroscience and Education, 40, 100256. https://doi.org/10.1016/j.tine.2025.100256
Dechamps, T., & Skulmowski, A. (2025). The effective design of tasks involving learning by drawing: Current trends and methodological progress in research on drawing to learn. Educational Psychology Review, 37, 50. https://doi.org/10.1007/s10648-025-10026-2
Skulmowski, A. (2024). Learning by doing or doing without learning? The potentials and challenges of activity-based learning. Educational Psychology Review, 36, 28. https://doi.org/10.1007/s10648-024-09869-y
Skulmowski, A., & Xu, K. M. (2022). Understanding cognitive load in digital and online learning: A new perspective on extraneous cognitive load. Educational Psychology Review, 34, 171–196. https://doi.org/10.1007/s10648-021-09624-7

Computer-generated visualizations, virtual reality, and augmented reality are becoming increasingly important components in education. A high level of visual detail does not necessarily help learners (and may even overwhelm them), but simplified styles do not provide the optimal level of realism for all types of learning objectives. In this research area, we investigate empirically how learners cognitively process realistic visualizations which implications for design we should draw from these results.

Current publications:
Skulmowski, A. (2024). No evidence for a negative effect of realism when learning about a process despite an increase in cognitive load. Applied Cognitive Psychology, 38, e70000. https://doi.org/10.1002/acp.70000
Skulmowski, A. (2024). Are realistic details important for learning with visualizations or can depth cues provide sufficient guidance?. Cognitive Processing,  25, 351–361. https://doi.org/10.1007/s10339-024-01183-3
Skulmowski, A. (2023). Shape distinctness and segmentation benefit learning from realistic visualizations, while dimensionality and perspective play a minor role. Computers & Education: X Reality, 2, 100015. https://doi.org/10.1016/j.cexr.2023.100015
Skulmowski, A., Nebel, S., Remmele, M., & Rey, G. D. (2022). Is a preference for realism really naive after all? A cognitive model of learning with realistic visualizations. Educational Psychology Review, 34, 649–675. https://doi.org/10.1007/s10648-021-09638-1