Recognizing cognitively demanding situations in design and measuring them for the semi-automated analysis of empirical studies in method development: AutoCodIng

• Applicant: Professor Dr.-Ing. Sven Matthiesen
• Subject Area: Engineering Design, Machine Elements, Product Development
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 460444004

In order to be able to develop suitable design methods, empirical studies are used to identify difficulties in product development. For the evaluation of these studies, data collection methods are used which usually require a high degree of interpretation in the data preparation. On the one hand, this type of data collection intervenes strongly in the workflows of designers due to the methods used and, on the other hand, only provides information on difficulties that arise after a complex evaluation. Besides, the investigated intervals are strongly limited due to the complexity of the evaluation. The aim of this research project is to investigate continuously measurable indicators for difficulties and to convert them into an algorithm. The algorithm is intended to automate design difficulties in functional analysis and thus objectively identify them for large numbers of participants. Eye tracking gaze data of participants during the processing of design tasks for functional analysis serve as a data basis. The use of gaze data instead of qualitative data acquisition (e.g. observation protocols, transcripts) is intended to increase the objectivity during evaluation. For the semi-automated recording, it is necessary to determine indicators for difficulties in the gaze data. On the basis of the eye-tracking data, three different evaluation approaches are built up and compared with each other. For this purpose (1.) individual eye tracking metrics, (2.) a combination of eye tracking metrics based on machine learning and (3.) context-specific metrics are developed and operationalized in algorithms. The accuracy of the developed algorithms is compared with the difficulties retrospectively indicated by the participants. With the developed algorithms it should be possible to objectively examine the procedure in functional analysis for large numbers of participants. Thus, methodological support needs can be identified and individual training content can be proposed or developed within the framework of method research.

• DFG Programme: Research Grants