LEAD:FUH – Learning Empowerment through Analytics and Data

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LEAD:FUH is a practical and research project run by FernUniversität and CATALPA, funded by the Foundation for Innovation in Higher Education.

The LEAD:FUH (Learning Empowerment through Analytics and Data) project aims to develop a transferable change management concept for a university-wide teaching architecture that enables the systematic use of learning analytics (LA) in degree programs with different subject-specific requirements.

The FernUniversität in Hagen is predestined for the implementation of learning analytics procedures (LAV). With over 75,000 students, it is the largest state university in Germany. LEAD:FUH offers the opportunity to sustainably develop the FUH into a learning analytics cooperation university in the German education sector, thereby effectively strengthening learning success, equal opportunities, and diversity.

Project goals and research questions

The overarching goal of LEAD:FUH is to close the gap between theoretical framework models for learning analytics and their practical, interdisciplinary implementation at universities—especially in degree programs with little affinity for data-based methods. From a scientific perspective, this raises three research questions that will be explored by CATALPA in the research part of the project.

Key research questions

• How do learning analytics methods affect academic success, dropout rates, and equal opportunity in heterogeneous student groups?
• Which subject-specific cultural factors promote or hinder the implementation of learning analytics?
• How can data protection, ethics, and acceptance be ensured when using learning analytics methods?
• What business effects—such as cost efficiency and return on investment—can be achieved through learning analytics methods?

The project team aims to contribute to the overall goals of the project with these findings, among others.

Specifically, the project has four target areas

1. Development of an LA teaching architecture

• Systematic integration of LAV into the initial phase of study (e.g., dropout predictions, personalized learning with AI, dashboards for self-regulated learning).
• Adaptation to the different subject cultures of the five faculties.

2. Participatory change management

• Strengthening LA readiness through organizational and personnel development.
• Training, promoting acceptance, and ensuring data protection and compliance.
• Expansion of the SHEILA framework to include the dimensions of governance, technology, law, acceptance, and science.

3. Gradual implementation and evaluation

• Introduction of three LAV types: dropout prediction with recommendations, AI-supported learning support, dashboards for self-regulated learning.
• Multimethod evaluation of the effects on students, teachers, student counseling, and administration (e.g., dropout rates, user satisfaction, return on investment).

4. Transfer and sustainability

• Development of the LEAD:FUH 360° showroom as an open-access manual for other universities.
• Development of a LAV toolkit with methods and software for curricular integration, acceptance promotion, and evaluation.
• Promotion of inter-university exchange through workshops, conferences, and networks.

News about LEAD:FUH