IPN-Kolloquium zum Modellieren und Simulieren im Studium: Wie lassen sich Studierende wirksam unterstützen?

Am 26. Juni begrüßt die Arbeitsgruppe Didaktik der Informatik am IPN Dr. Alejandra J. Magana, Professorin für Computer and Information Technology sowie für Engineering Education an der Purdue University (USA). In ihrem Vortrag „Scaffolding Undergraduate Students’ Individual and Team-based Modeling and Simulation Practices“ gibt Dr. Magana im Rahmen des IPN-Kolloquiums spannende Einblicke in aktuelle Forschung zur Förderung von Modellierungs- und Simulationskompetenzen im ingenieurwissenschaftlichen Studium.

Moderne Arbeitswelten in den MINT-Berufen erfordern zunehmend den souveränen Umgang mit Rechenverfahren, Datenanalyse sowie Modellierung und Simulation. Doch wie können Studierende diese komplexen Fähigkeiten wirksam erlernen? Und welche Rolle spielen dabei individuelle und teambezogene Lernprozesse?

Dr. Maganas Forschung geht diesen Fragen in designbasierten Studien nach. Sie untersucht, wie Lernumgebungen gestaltet sein müssen, damit Studierende modellbasiertes Denken und „adaptive Expertise“ im Umgang mit Simulationen entwickeln können. Im Vortrag stellt sie unter anderem ein „computational cognitive apprenticeship“-Modell vor, das Lehrende darin unterstützt, Studierende beim Aufbau dieser Kompetenzen gezielt zu begleiten.

Das Kolloquium richtet sich an alle Interessierten, auch außerhalb des IPN.

Das Wichtigste in Kürze:

Termin: 26. Juni, 14 Uhr
Ort: IPN-Hörsaal, Olshausenstr. 62

Mehr erfahren: Im Kurzinterview haben wir mit Dr. Magana über zentrale Themen ihres kommenden Vortrags sowie über die Schwerpunkte und Ziele ihrer Forschung gesprochen. Darin erfahren Sie mehr über ihre Perspektiven rund um das Lehren und Lernen von Modellierungs- und Simulationspraktiken (auf Englisch):

IPN: Dr. Magana, in your talk you discuss the concept of "adaptive expertise" in modeling and simulation. How does this type of expertise differ from basic factual knowledge—and why is it so important in today’s STEM education?

Ale Magana: Adaptive expertise differs significantly from basic factual knowledge in that it goes beyond simply knowing facts or procedures to having the ability to transfer knowledge to new and unfamiliar situations. This often involves students having conceptual understanding.

In the context of modeling and simulation, adaptive expertise is crucial because learners must be able to integrate disciplinary concepts with mathematical equations and algorithmic thinking.  For example, while routine experts may efficiently run simulations using known parameters, adaptive experts can recognize when existing models fall short, conceptualize new approaches, and even develop entirely new simulation methods. For this, students need to be able to understand the behavior of a model by connecting its components (e.g., variables) to scientific principles.  It also involves students being able to identify the proper equations or methods to model the behavior of certain systems.

IPN: In your research, you emphasize the importance of both individual and team-based learning processes in modeling and simulation. What are some key differences or dynamics between these two approaches—and how can educators support both effectively?

Ale Magana: When students engage in modeling and simulation projects, both individual and team-based learning offer unique benefits and pose different challenges. Individually, students must make sense of the relationships between disciplinary concepts, equations, and algorithms on their own, which can foster deep personal understanding but also requires significant cognitive effort. In contrast, working in teams allows students to share their knowledge and expertise, enabling them to collaboratively interpret concepts and computational methods. Working with others can also result in collaborative learning. However, team-based learning introduces additional dynamics, as students must also navigate coordination, communication, and role management within the group.

To support both forms of learning effectively, educators can draw on the concept of scaffolding. Scaffolding refers to the temporary support provided to learners to help them accomplish tasks they cannot yet complete independently but can achieve with guidance. Scaffolding may come from instructors, peers, or technology. For example, according to the literature on technology-enhanced learning environments, scaffolding can be used for supporting sense-making, articulation, process management, and reflection.  Sense-making scaffolding can help students connect real-world phenomena with appropriate representations, such as simulations or visual models. Articulation scaffolding can prompt learners to explain their reasoning and make their thinking explicit. Process management scaffolding can guide learners through explicit stages of the problem-solving process, ensuring, for example, that the students fully test their solutions before jumping to a conclusion. Finally, reflection scaffolding helps students evaluate their outcomes, their methods, and their learning processes, strengthening both self-regulation and team functioning.

IPN: In your view, what are currently the biggest challenges and opportunities in preparing STEM students to engage effectively with modeling and simulation?

Ale Magana: One of the biggest challenges facing STEM education is the rise of generative AI and its integration into the teaching and learning process.  Its application for teaching and learning in the context of modeling and simulation is not the exception. While these tools offer new opportunities for supporting students, they also pose risks when used uncritically or in ways that bypass the development of essential conceptual and analytical skills.

In modeling and simulation, students can use generative AI reproductively, for example, by asking it to generate code or equations without fully understanding the underlying models or scientific principles. This kind of use may lead to surface-level engagement, where students focus on producing outputs rather than making sense of the modeling process itself. However, there is also significant productive potential. Generative AI can support deeper learning when students use it to test their ideas, debug their models, or explore alternative formulations and assumptions. In these cases, AI acts as a cognitive partner, helping students reflect on their reasoning, clarify concepts, and iterate on their solutions.

The challenge for educators is to guide students in using these tools not as shortcuts, but as aids in developing adaptive expertise so that they learn to understand, critique, and innovate with models rather than just reproduce them.