Department of Informatics and Digital Education

The research focus of the Department of Informatics and Digital Education is on the innovative use of technology in educational contexts. While the Chair of Informatics Education, headed by Professor Bernhard Standl, focuses on the modelling and empirical investigation of teaching concepts as didactic design patterns, the Junior Professorship for Digital Education, led by Junior Professor Alexander Skulmowski, conducts empirical research into the design of digital learning and investigates the effects of digital learning media.

The Chair of Informatics Education focuses, in particular, on identifying effective teaching and learning scenarios for computing lessons, while examining the topic from various perspectives.
Thanks to integration of the Teaching and Learning Lab (TLL) for Informatics Education into the programme in Teacher Education, Informatics, students' and pupils' IT skills are promoted in a research-based manner. In addition, methodological approaches are further developed within the context of the mixed-methods paradigm.

The Chair of Informatics Education comprises three research working groups: TLL, EIS and TEL.

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).

Promoting the use of information systems in teacher education.

Focus areas

The EIS group investigates how digital media can be used to enhance teaching and learning processes. The aim is to sensitise and qualify future teachers for the use and integration of digital tools in their future working environment. The focus is on providing them with the necessary means, methods, and skills to efficiently digitalise their teaching, provide high-quality education, and improve pupils' learning processes.

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.

The Junior Professorship for Digital Education investigates teaching and learning processes involving digital technology, mainly using methods of experimental cognitive psychology. The effect of different design parameters of digital technology on cognitive, metacognitive, and motivational variables is investigated. The focus is on examining which design principles can be used to optimise the learning outcomes achieved through technologies such as virtual reality, eye-tracking, and interactive learning applications.

The ever increasing use of three-dimensional learning environments, virtual reality, and augmented reality raises the question of what influence visual realism has on learning processes. While a higher level of detail is not always conducive to learning, and may even demand too much of learners, research findings show that simplified representations do not ensure an optimum level of realism for every learning objective either.

In this key research area, we empirically investigate how realistic visualisations are processed on a cognitive level and what consequences can be drawn from these findings for visual design. The design of both static visualisations and augmented reality environments is being researched. This is done applying fundamental methods as well as using application-orientated contexts.

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, pp. 649–675. https://doi.org/10.1007/s10648-021-09638-1

 

Digital learning formats usually generate a specific type of cognitive load. Interactive learning environments and learning games, for example, require learners to learn how to operate the game and to acquire other information that is not directly related to the learning content. Common theoretical models suggest that this might impair learning, however, a large number of studies have proven that the additional cognitive load caused by digital environments is not detrimental to the overall learning process.

It can be deduced from the findings that the inclusion of several types of cognitive load is justifiable if it is well suited to a specific learning objective. For example, in order to familiarise oneself with a complex process using digital means, the cognitive load involved in learning to control this digital environment has to be tolerated. This fit between cognitive load, processes beneficial to learning, and learning objectives is analysed both empirically and theoretically in the cognitive load alignment model.

Skulmowski, A. (2023). Guidelines for choosing cognitive load measures in perceptually rich environments. Mind, Brain, and Education, 17, pp. 20–28. https://doi.org/10.1111/mbe.12342

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, pp. 171–196. https://doi.org/10.1007/s10648-021-09624-7