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Domain and competency modelling (VoLL-KI)

Third Party Funds Group - Sub project

Acronym: VoLL-KI

Start date : 01.12.2021

End date : 30.11.2025

Website: https://www.voll-ki.fau.de/

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• Overview

   

• Publications

  (11)

• External partners

  (2)

Overall project details

Overall project

Project details

Short description

The joint project VoLL-KI further develops higher education on three levels: study programs, individual study planning and learning progress within a course. AI-based systems provide support at all three levels. For this purpose, data from the Computer-based Decision Support System for Higher Education in Bavaria (CEUS) will also be used. At the same time, CEUS can be extended with data from the VoLL-KI project. The analysis combines individual and group-specific data. This should help to make university teaching non-discriminatory in the long term. Based on the data, individual recommendations for the course of study can be created. Students can then view reasons for the recommendations, look at alternatives, and also provide feedback on the suggestions. In a pseudonomous form, those responsible for the study program can also access this data. The project is evaluated by means of surveys and data analysis. Scientists from the fields of AI, computer science, computer science didactics and educational research from three neighboring universities are involved in the project. The initial focus is on study programs in computer science that differ at the three locations: a large, engineering-oriented computer science, a medium-sized, interdisciplinary-oriented computer science, and a small, application-oriented computer science. The results will then be transferred to other study programs at the participating universities.

Scientific Abstract

The joint project VoLL-KI is developing higher education on three levels: on the macro level, evidence-based study programs are being further developed; on the meso level, context-adaptive recommendations for individual study planning are being created; and on the micro level, learner-specific diagnoses and support are being developed. For this purpose, 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 will be developed for AI introductory courses. Course data will be made available via the CEUS data warehouse system and systematically expanded during the course of the project. Data on  competencies of individual students will be combined with data on specific groups - for example, related to gender and educational biographies. As a result, tailored 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 dataset by monitoring learning and performance trajectories on an individual as well as on a group-specific level will be integrated into the data warehouse system and made available to study program managers. The developed offers will be evaluated during the course of the project by means of surveys and log file analyses in order to optimize them formatively. Scientists from the fields of AI, AI-related areas of computer science, computer science didactics and educational research from three neighboring universities are cooperating on the project. The focus of the project is on the computer science programs at the three locations: a large, engineering-oriented computer science, a medium-sized, interdisciplinary computer science, and a small, application-oriented computer science. Towards the end of the project and thereafter, the successful components will be extended to other study programs and the project results will be integrated into the quality management processes of the participating universities.

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Funding Source