Explainable & Interactive Learning Simulations
The mini-projects we showcase here were carried out at colaps as part bachelor’s and master’s theses, praxisprojects or small-scale research.
Yannick Haußmann designed a web-based simulation for teaching supervised machine learning student models (AFM, PFA, IFM) in intelligent tutoring contexts. The system offers interactive model training, step-by-step simulation with live formulas, and analytic visualizations to make underlying mechanisms transparent. A mixed-methods evaluation using questionnaires, eye-tracking, interaction logs, and interviews assessed usability, usefulness, attention patterns, and conceptual understanding.
Yannick Haußmann, “Design and implementation of interactive simulations for data-driven, machine-learning student models”, Master’s Thesis, December 2025.
This research investigates how learners interact with an XAI-ED simulation explaining three student models (AFM, PFM, IFM) through think-aloud protocols and interviews with master’s students. Qualitative analysis revealed two distinct learner profiles— Strategy-Deploying and Overwhelmed — showing that identical explanations produce very different processing patterns. Findings support “Staged Transparency” as a feasible design approach and highlight the need for adaptive scaffolding and early learner-type detection.
Raphael Stedler, “Design, Implementation and Evaluation of Human-Centered, Interactive Simulations for Explainable Student Models”, Master’s Thesis, December 2025.
