Data-Driven Digital Innovation Lab

Type of event: Seminar
Institution: Faculty of Mathematics, Informatics and Natural Sciences
Funding period: 01.04.2023 to 31.03.2024
Short title: D3 Innovation Lab

Extract from the funding application: "In the D³ Innovation Lab, students are guided through all phases of the data life cycle in data-driven innovation projects. In addition to planning projects, they also learn to apply data practices and manage collected data. To ensure that this knowledge can also be used in the long term beyond the teaching lab project, a physical and virtual exploration space is created that is co-designed by the students. Teachers can use this space to organise their own courses with the spaces created."

Orientation of the D3 Innovation Lab

In an increasingly digitalised world, handling data is becoming a key skill in almost all professional fields. In view of this development, it is essential that students from all disciplines develop the ability to understand, analyse and interpret data. For this reason, the teaching project was developed to guide students along the data life cycle, guided by selected or self-determined data projects. Based on current developments, the thematic focus was also expanded to include generative artificial intelligence both as a tool for data-driven projects and as an object of investigation for explorative questions. Some of the reasons for the development of the teaching project are explained in more detail below:

Interdisciplinary skills development: By including students from all disciplines in the groups, the data-driven projects not only improve their data-related skills, but also strengthen their ability to collaborate across disciplinary boundaries. This brings together different perspectives, which can broaden reflection on their own disciplines.
Strengthening digital sovereignty: At a time when data is playing an increasingly important role in our lives, it is important that students develop basic digital sovereignty. By learning how to use, analyse and interpret data in a meaningful way, they will be empowered to make informed decisions and navigate a data-driven world.
Encouraging creativity and innovation: Working on mostly exploratory data projects will encourage students to be creative and develop innovative solutions. With the freedom to choose their own questions and apply their own methods of analysis, they are encouraged to think outside the box and independently find ways to work on and solve problems.
Promotion of problem-solving skills: Working on their own data projects requires students to be able to identify and analyse complex problems and develop solutions. By going through the data life cycle - from data collection to data analysis to interpretation and presentation of results - they have to deal with specific questions at each stage and their skills are honed in a practical way.

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Review and results

The key outcomes of the teaching project are aligned with the original objectives and include strengthening data-related skills and encouraging creative and innovative thinking. Here are some of the results:

Improved data literacy: the aim was to improve students' data literacy. By working on self-selected data projects along the data lifecycle and utilising generative artificial intelligence, they developed a deeper understanding of data analytics. Students acquired skills in data collection, cleansing, analysis and interpretation. The topics covered ranged from data democratisation in organisations and the influence of generative AI in a political context to the use of no- and low-code development environments.
Interdisciplinary collaboration: The project encouraged collaboration between students from different disciplines such as computer science, linguistics and humanities. By working in mixed teams, students benefited from different perspectives and expertise. This led to diversified solutions to complex problems and stimulating discussions in the final presentations.
Development of problem-solving skills: The project strengthened the students' problem-solving skills. They learned to systematically analyse and solve complex problems in a structured way, supported by a prototypical wiki that maps the data life cycle. These skills are valuable in professional life.
Fostering creativity and innovation: The freedom to choose their own questions and apply analytical methods encouraged students to go beyond traditional ways of thinking and develop innovative solutions. The necessary creative mindset is crucial in a dynamic world.

Tips from lecturers for lecturers

Teaching skills can be enhanced in many ways through dialogue with students, who in turn have made new discoveries in their findings. The development of a wiki has also provided many insights into its structure and how to present content according to the motto "less is more". At a didactic level, it is essential to convey complex concepts and techniques relating to data skills and generative artificial intelligence as comprehensibly as possible and to support learners in applying their knowledge in practice. There are currently few scripts and approaches for teaching the latter topic in particular. These findings will help to design new innovative teaching concepts in the future, particularly with the inclusion of new technologies.

In addition, important insights were gained regarding team constellations. For example, some groups proved to be particularly effective with their previous study experience and their associated disciplines. However, it should be noted that the individual with his or her characteristics is the biggest influencing factor here.