Didactics, Ethics, and Technology of Learning Analytics and AI in Higher Education
Software for learning analytics, as well as software in the sense of artificial intelligence, is seen to have great potential to make a valuable contribution to improving teaching and the individual support of students. However, universities have hardly been prepared for this so far and corresponding approaches have only been tested to a limited extent. In the AI:edu.nrw project, funded with 1.9 million euros by the Ministry of Culture and Science, the project partners Ruhr-University Bochum and RWTH Aachen are exploring, designing, and testing possibilities in this area for the higher education sector, focusing on three different levels: the level of the course, the level of the institution of higher education, and the level of cross-university cooperation. In this context, several activities mentioned below are planned.
At the course level, different fields of application of learning analytics scenarios are to be tested within the framework of specific teaching projects. When selecting the projects, care was taken to ensure that they came from different subject groups.
At the central university level, the aim is to develop a framework for the use of learning analytics and AI that complies with data protection regulations. In addition, a sub-project will focus on how the use of these tools can be evaluated from an ethical perspective. With a view to questions of science didactics, it will be determined how meaningful didactic scenarios can be implemented with analytics data. Last but not least, from an IT perspective, it is a question of how and in which systems data can be made available at all. Overall, in addition to working on specific issues, the project is intended to initiate and moderate an institution-wide dialog on what approach to the aforementioned topics appears desirable for the project partners as an institution.
Beyond the project partners, the NRW-wide dialog on the use of learning analytics and AI for university development is to be initiated and moderated under the umbrella of DH.NRW as part of the project work. A survey in the DH.NRW universities on this topic is planned as well as the networking of experts in this field. It is also important to network the project with the online state portal ORCA.nrw.
For the broad approach, a group of people from RUB and RWTH with broad expertise work together: included are, among others, specialists for the topics educational data mining, machine learning, educational psychology, ethics of artificial intelligence, science didactics, as well as learning management systems; involved are the deans of different study programs as well as the Center for Science Didactics, the Center for Medical Teaching, the Center for Higher Education Didactics of Mathematics, as well as the Central Student Advisory Service. The Center for Teaching and Learning Services at RWTH Aachen University is involved as an external cooperation partner. In addition, close cooperation is planned with the Online State Portal for Studying and Teaching ORCA.nrw as well as other cross-university projects under the umbrella of DH.NRW.
The aim of the project is to determine how rules, concepts, processes, and technology can be designed for the use of learning analytics both at the Ruhr University, which is funded in the focus area, and at RWTH Aachen University, which is linked in partnership. It contributes to the development of a "data culture" at the universities and to the clarification of what can or cannot be achieved with data in the field of teaching development in order to better advise students, offer them more individual learning settings, and design teaching.
The further development of technology, especially in the area of LMS, will be made available to all NRW universities. The same applies to the project experiences at the different levels, for which a final project publication is planned. In addition, the expertise at the NRW universities is to be bundled and further developed through the targeted networking activities in order to be able to transfer the solutions found after the conclusion of the pilot project.
- Learning – Further development of individual learning settings and advising of students.
- Teaching – Conception of a data protection-compliant framework for the use of learning analytics and AI in teaching. With a view to science didactic issues, it will be determined how meaningful didactic scenarios can be implemented with analytics data.
- Apply – Elaborate rules, concepts, processes, and technology for the use of learning analytics so that a "data culture" can develop in the higher education landscape.
- Network – Pooling of university-wide expertise and exchange in dealing with data in the field of teaching development.