Forschungsprojekte

Laufende Projekte

• SHARP – Simulation-based Learning in Higher Education: Advancing Research on Process Diagnostics and Personalized Interventions, 2025-2029, 
  Collaborative Research Centre SHARP (SFB-Transregio 419) funded by the Deutsche Forschungsgemeinschaft (DFG). 
  The Collaborative Research Centre SHARP (SFB-Transregio 419) is a joint project conducted by Ludwig-Maximilians-Universität München (LMU), Technical University of Munich (TUM), the University of Augsburg, and the Leibniz-Rechenzentrum. Its aim is to advance theory-building on AI-supported personalization in simulation-based learning in higher education. As a shared research framework, SHARP focuses on diagnosing and intervening—two professional practices that are highly relevant across many academic disciplines.
  Our Chair is primarily involved in three projects focusing on self-regulated learning with simulations:

• A04: Promoting diagnostic and intervention skills in programming and debugging through personalised representational and learning process scaffolds.
  Debugging represents a complex challenge for computer science teachers, involving both the technical process of identifying and resolving programming errors and the pedagogical task of supporting students’ problem-solving strategies. Preservice teachers must develop not only technical expertise but also diagnostic competence, the ability to interpret students’ reasoning, identify misconceptions, and decide on appropriate instructional interventions. 
  Simulation-based learning environments offer a promising approach to fostering these diagnostic skills by providing authentic yet controlled scenarios in which preservice teachers can observe, interpret, and evaluate students’ debugging behavior. However, the effectiveness of these simulations depends on how information is represented and scaffolded. Research has shown that adapting the representational complexity and cue salience to the learner’s needs can enhance learning outcomes. This project investigates personalized representational scaffolding through variations in cue salience within video-based debugging simulations. Specifically, we examine how adaptive scaffolds can guide preservice CS teachers’ attention toward diagnostically relevant aspects of students’ behavior and reasoning.
  Beyond perceptual support, effective diagnostic reasoning also requires metacognition of one’s own diagnostic process. Prior studies suggest that expert diagnosticians adjust their engagement with evidence depending on the context and their diagnostic goals. Therefore, this project further explores how metacognition integrates into the diagnostic process and how it contributes to diagnostic quality in simulation-based learning environments. 
  Ultimately, the project aims to deepen our understanding of how personalized scaffolds and metacognitive support can jointly enhance preservice teachers’ diagnostic reasoning and professional vision in computer science education.
  
  Collaboration partners: Prof. Dr. Tilman Michaeli, Prof. Dr. Jürgen Pfeffer, Prof. Dr. Tina Seidel

  Contact persons: Prof. Dr. Maria Bannert, Annabel Wolf