Joint Project VoLL-KI
“Learning from Learners”, abbreviated as VoLL-KI, is a joint project of the Friedrich-Alexander University of Erlangen-Nürnberg (FAU) (coordination), the University of Bamberg and the Coburg University of Applied Sciences and is funded by the Bund-L?nder-F?rderinitiative “Künstliche Intelligenz in der Hochschulbildung“. Over four years, the participating institutions will receive funding to advance data-driven AI methods and their application in teaching.
Subprojects
University of Bamberg
VoLL-KI: Data-based analyses, explanations and aids
Friedrich-Alexander University of Erlangen-Nürnberg
VoLL-KI: Domain and competence modelling
Coburg University of Applied Sciences
VoLL-KI: Individualised learning navigation and learning situations
Shaping higher education:
Holistic, data-driven and knowledge-based
The joint project VoLL-KI advances higher education on three levels. To this end, data- and knowledge-based artificial intelligence (AI) approaches are combined. Based on preliminary work on knowledge graphs, error libraries for programming, intelligent tutoring systems, explainable and interactive machine learning, chatbots, virtual reality, as well as recommendation systems, intelligent support systems for selected courses as well as AI introductory courses for sub-disciplines are being developed.
Macro level
evidence-based advancement of degree programmes
Meso level
context-adaptive, correctable recommendations for individual study planning
Micro level
learner-specific diagnosis and support in course units
Study progress data is made available via an already established data warehouse system (CEUS) and systematically expanded in the course of the project. Data on the existing competences of individual students and those that need to be developed are combined with data on specific groups - for example, in relation to gender and educational biographies. In this way, customised recommendations for study planning are created. Students can request explanations for recommendations, explore alternatives and correct premises at any time. The expansion of the current data stock through the monitoring of learning and performance trajectories on an individual and group-specific level is integrated into the data warehouse and made available to those responsible for the study programme as a dashboard.
The offers developed will be evaluated over the course of the project by means of surveys and logfile analyses in order to optimise them formatively. Researchers from the fields of AI, AI-related areas of computer science, computer science didactics and educational research from three neighbouring universities are cooperating on the project. The focus of the project is on the computer science degree programmes at three locations - a large, strongly engineering-oriented computer science, a medium-sized, strongly interdisciplinary computer science and a small, strongly application-oriented computer science. Towards the end of the project and afterwards, the successful components will be extended to other study programmes and the project results will be integrated into the quality management processes of the participating universities.
Press release (German) – Eine nordbayerische KI-Allianz für die Hochschullehre
People in charge at the University of Bamberg
Prof. Dr. Ute Schmid, Cognitive Systems Group (Subproject lead) · Prof. Dr. Andreas Henrich, Chair of Media Informatics · Prof. Dr. Daniela Nicklas, Chair of Mobile Systems · Oliver Elsner, Head of Controlling/Reporting · Achim Ulbrich-vom Ende, Head of the CEUS Competence and Service Centre · Prof. Dr. Diedrich Wolter, Professor for Hybrid AI, University of Lübeck
