Forschungsprogramm: Bildungstechnologien und Künstliche Intelligenz

Im Bereich (1) Lernen mit Technologie & KI interessiert sich das Forschungsprogramm für individuelles Lernen und Lernen in kleinen Gruppen in verschiedenen Bildungsumgebungen wie Schulen, Hochschulen, Berufsbildung und nicht-formalen Lernumgebungen (z.B. Museen).

• Konversationelle KI in der Bildung (Alisa Maksimova) Förderung der Fähigkeit zur Zusammenarbeit in der Hochschulbildung (Sebastian Strauß)
• Lernverläufe verstehen und Kompetenzentwicklung unterstützen (Sebastian Strauß)
• Verbesserung der Überarbeitungsfähigkeiten bei der Überarbeitung von KI-generierten Texten (Anna Radtke)
• Kleingruppenarbeit in hybriden universitären Lehrveranstaltungen: Herausforderungen und effektive Unterstützungsmaßnahmen (Sabine Schermeier)

Im Bereich (2) Lehren mit Technologie und KI untersuchen die Forscher:innen die Kompetenzen und Bedürfnisse von Lehrern. Die Forscher:innen ermitteln die Chancen und Herausforderungen, denen Lehrkräfte beim Unterrichten in digitalen Umgebungen begegnen, und untersuchen, wie sie digitale Lernmaterialien anpassen. Insbesondere untersuchen die Forscher:innen die Rolle von Lehrer-Dashboards, wie sie den Lehrer:innen informieren können und wie Lehrer:innen sie nutzen können, um ihren Unterricht zu verbessern.

• Unterstützung von Lehrkräften beim Einsatz digitaler Lernsysteme im Bildungskontext (Ann-Christin Falhs)

An der Schnittstelle zwischen Lernen und Lehren untersuchen die Forscher:innen, wie Learning Analytics sowohl Lernende als auch Lehrende in Schulen, Universitäten und im außerschulischen Bereich unterstützen kann. Darüber hinaus untersucht das Team die mit KI-Tools verbundenen ethischen und datenschutzrechtlichen Implikationen.

• Lernen mit und über KI in Museen (Alisa Maksimova)

Analyzing Learning for Individualized Competence Development in Mathematics and Science Education (ALICE): understanding learning trajectories

(Cooperation project of Ruhr University Bochum, IPN Kiel & DIPF Frankfurt)

Through digital learning environments, the understanding and performance of learners can be modeled during the learning process, and predictions can be made about the potential progress of each learner. The interdisciplinary project „ALICE“ (Analyzing Learning for Individualized Competence Development in Mathematics and Science Education) addresses this challenge by creating theoretical and methodological foundations for adaptive support of learners in mathematics and science education. In this project, digital learning materials (units for learning in the classroom) are being developed and tested, and predictive models of learner competence development are created based on the data obtained. The reconstruction of the learning trajectories is followed by an investigation of the effectiveness of the different learning trajectories students took during their learning. Based on this, we aim to develop instructional support to help students traverse through the learning unit on an effective learning trajectory.

Research on Emerging Technologies for Teaching and Learning (RETTL): Supporting collaborative reflection by K-12 teachers with analytics from intelligent tutoring software

(Cooperation project of the CMU Pittsburgh, Ruhr University Bochum & Monash University)

In recent years, intelligent tutoring systems (ITS) have become increasingly advanced and the focus of researchers. These systems offer the potential to improve student learning. At the same time, the data generated can also be used to alert teachers to situations about student learning that would otherwise have remained hidden from them. The project aims to develop a tool (through participatory design with middle-school teachers in the US) that leverages multimodal analytics from classrooms using intelligent tutoring software. Its primary purpose of the tool is to facilitate meaningful reflection by teachers. Additionally, the project aims to contribute to the advancement of scientific understanding regarding the impact of such a tool on teachers. This includes exploring how it enhances teachers‘ awareness of their students‘ learning processes, as well as teachers beliefs about effective teaching and finally how it might lead to improved classroom practices by teachers.

KI:edu.nrw – Sub-project Educational Science

The Educational Science sub-project of the research project KI:edu.nrw aims to develop data-based learning support for promoting students’ self-regulated learning competencies. In modern society, the ability to autonomous, self-regulated learning is one of the key competences. The dynamic, ever-changing world of work and daily life requires a constant, self-directed updating of knowledge and skills. However, it is not uncommon for learners to lack self-regulation skills. Furthermore, when it comes to self-regulation competencies, learners often have different support needs, which should be addressed in an individualized way. Digital learning settings offer a good opportunity to provide adaptive support to several learners at the same time, based on their learning data.

In a field study, we supported university students participating in a statistics course by providing metacognitive or motivational prompts in the Moodle course. The study showed that the support we implemented was effective for facilitating self-regulation skills. The overall aim of this sub-project is to develop a practical concept and recommendations for the implementation of Learning Analytics-based support, which could then actually be used in higher education.

Link: https://learning-aid.blogs.ruhr-uni-bochum.de/kiedu-nrw/