Hypotheses During Diagnostic Problem Solving in Technical Domains: Mental Basis, Process, and Outcome

• Applicants: Professor Dr. Stephan Abele; Professor Dr. Sebastian Pannasch
• Subject Area: General and Domain-Specific Teaching and Learning
• Term: since 2015
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 281441695

In diagnostic problem solving, hypotheses about the underlying fault causes play a central role and determine the quality of subsequent diagnoses. In a previous project, we have found that apprentices of car mechatronics use different strategies (case-based, computer-based, and model-based), and especially for difficult problems the reliance on mental models of the system is crucial. However, it is not sufficiently understood whether the strategies indeed have a causal effect on hypotheses generation, how the model-based strategy is applied to generate hypotheses, and whether this process differs between technical domains. Therefore, the aim of the present project is to study hypotheses generation and the reliance on mental models in depth. We hypothesize that (1) three strategies are used that differ with regard to their underlying mental activities and causally determine hypotheses generation processes and outcomes, (2) hypotheses generation depends on individual characteristics and mental models, and (3) the use of mental models for hypotheses generation differs between technical domains. Based on the method of our previous project, we suggest four studies. Apprentices of mechatronics specialized on either cars or packaging machines diagnose fault causes from their respective domain, either using a realistic car simulation or a real packaging machine. In subsequent phases, they first generate hypotheses and later test them to diagnose the cause of the fault. Log data, eye tracking data, and verbal protocols are used to characterize the process and outcomes of hypotheses generation. Moreover, in two studies participants are required to generate concept maps reflecting their mental models of the system, and the contents of these models are related to hypotheses generation behaviours. Study 1 serves as a replication of the findings from our previous project on the consequences of three spontaneously applied strategies in car mechatronics, and complements them with a detailed characterization of the hypotheses generation process. Study 2 manipulates strategies in car mechatronics experimentally to allow for an examination of causal relations between strategies and the quality of hypotheses generation. In Study 3, the hypotheses generation of experts is investigated in both domains and expert mental models of the car or packaging machine are elicited. In Study 4, these expert models are used to evaluate the mental models of apprentices. It is investigated how the quality of mental models affects hypotheses generation processes and outcomes. In combination, the studies enable a theoretically and empirically founded description of hypotheses generation processes and the role of mental models in different technical domains. Based on this description, the studies further provide implications for how to support hypotheses generation processes during vocational education.

• DFG Programme: Research Grants
• Co-Investigator: Dr. Romy Müller