Prediction of teacher behavior based on large data collections

• Type of event: Seminar
• Institution: Faculty of Educational Science
• Funding period: 01.04.2023 to 31.03.2024
• Short title: VoLveD

Extract from the funding application: "Most students are vaguely aware that people's behaviour can be predicted if enough independent individual information is available. Through their own research project, which is based on the re-analysis of existing classroom observation data, students should build up data literacy in order to recognise possible applications, limitations, data protection and data security aspects and the significance of such predictions for their own professional and private lives."

The VoLveD project

The seminar is intended as a best-practice example of how subject-specific expertise can be developed through research-based learning together with interdisciplinary data literacy. In the course, students learn how to observe vocational lessons using various observation questions.

They have established that there are fixed structures that characterise every lesson and that teachers and students behave within these structures. If this is the case, it is of course also possible to make predictions about what future teacher behaviour might look like in response to student behaviour and how promising certain behaviours are. The tools for this are observation software, databases and statistics. Students will learn how to use these during the course using practical examples.

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Review and results

At the beginning of the course, many students were unsure whether a technical and quantitative-methodological approach to educational science was suitable for them. There were doubts as to whether their existing computer skills would be sufficient to achieve the challenging objectives of the course. In the course of the seminar, they were gradually taught the necessary tools, from the use of literature management and observation software to relational databases, in order to ultimately predict probabilities for the behaviour of teachers.

Even students without a high level of data literacy were able to follow the course of the seminar and worked with commitment. According to their own statements, their skills in using scientific software and handling quantitative observation data improved significantly. They were surprised at how structured qualitative observations can be quantified and statistically analysed.

When reflecting on the event, the students recognised how behavioural predictions of teachers are possible on the basis of statistical analyses. This made it possible to discuss how these methods can be transferred to other applications in professional and private life. This also achieved the goal of reflecting critically on one's own digital behaviour and deriving one's own digital options for action. Using the example of data practices in teaching research, students went through all the processes of contemporary data practices and built up a fundamental and critical understanding through an accompanying reflection and discussion.

Tips from lecturers for lecturers

It has been shown that the very detailed planning of each individual course was a good basis for avoiding existing uncertainties from the outset. The technical challenges of the demanding project also had to be fully simulated in the preparation of each course and checked for any difficulties that might arise. Overall, this proved to be good, albeit time-consuming, preparation.

Despite the very detailed preparation of the seminar events, it was possible to give the students freedom, for example with regard to the questions, observation aspects and evaluation methods. This aspect in particular, i.e. the harmonisation of detailed structure and necessary freedom, is a positive didactic experience.

Persons involved

Prof. Dr. Jens Siemon

Professor of Vocational Education

Main research areas:

• Teaching and learning research / teaching research with video
• Simulations for learning and teaching
• Language in vocational education and training
• Vocational education and training in the knowledge society

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