Promoting car mechatronics apprentices' diagnostic strategy with modeling examples: Development and evaluation of a simulation-based learning environment

Research output: Contribution to journalResearch articleContributedpeer-review



Crucial for training automotive mechatronics technicians (AMTs) is enabling them to diagnose car malfunctions. AMTs are particularly successful when they base their diagnostic process on a mental model of the affected automotive system. Still, only few AMT apprentices master such diagnoses after their apprenticeship. Therefore, we created a simulation-based learning environment with modeling examples to teach AMT apprentices a diagnostic strategy that builds on mental models. Following design-based research guidelines, we formatively evaluated our learning and testing materials by expert judgments and a small study during the development of the learning environment. Finally, an evaluation study showed that the learning environment promoted apprentices' knowledge about the diagnostic strategy. However, they could not transfer their knowledge to diagnostic problem-solving. Overall, the apprentices evaluated the learning environment positively, except it was considered too long and repetitive. Reasons for the outcomes as well as possible further developments of the learning environment are discussed.


Original languageEnglish
JournalStudies in Educational Evaluation
Issue number72
Publication statusPublished - Mar 2022

External IDs

Scopus 85120174928
Mendeley 48836db0-ac30-33ab-857a-b1c432a69814
ORCID /0000-0002-3689-8428/work/142235930
ORCID /0000-0002-5182-577X/work/142249610


Research priority areas of TU Dresden

DFG Classification of Subject Areas according to Review Boards

Subject groups, research areas, subject areas according to Destatis

ASJC Scopus subject areas


  • Diagnosis of car malfunctions, Mental models, Modeling examples, Simulation-based learning, Learning environment, Diagnosis of car malfunctions, Learning environment, Mental models, Modeling examples, Simulation-based learning

Library keywords