TRR 419 SHARP – A03: Personalized Support for Task-Based Diagnosing and Intervening in Mathematics and Physics Based on Eye Movements and Log Data

Teachers need pedagogical content knowledge (PCK) to act professionally in diagnostic and intervention situations. This includes analyzing students’ solutions, selecting suitable follow-up tasks, and addressing specific learning difficulties. The aim of this project is to investigate how pre-service teachers in mathematics or physics can be supported in applying their PCK for diagnosing and intervening effectively. At the core of the project is a computer-based simulation in which pre-service teachers diagnose the learning status of virtual students based on their task solutions and select appropriate instructional responses. This creates realistic decision-making scenarios that resemble those encountered in professional teaching practice.
To analyze learning processes within the simulation, participant input is complemented by log data and eye-tracking. These data serve as indicators of diagnostic activity and provide insights into underlying cognitive and professional processes. Using machine learning techniques, individual process profiles are identified. These profiles form the basis for personalized support, in the form of prompts that activate relevant PCK.