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

识别设计中的认知要求较高的情况并对其进行测量，以便对方法开发中的实证研究进行半自动分析：AutoCodIng

• 批准号: 460444004
• 负责人: Professor Dr.-Ing. Sven Matthiesen
• 金额: --
• 依托单位: Institut für Produktentwicklung (IPEK)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/460444004?language=en
• 关键词: Recognizing; cognitively; demanding; situations; design

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.

为了能够开发出合适的设计方法，实证研究被用来识别产品开发中的困难。为了评价这些研究，使用了数据收集方法，这些方法通常要求在数据准备过程中进行高度的解释。一方面，由于所使用的方法，这种类型的数据收集强烈地干预了设计师的工作流程，另一方面，只提供了复杂评估后出现的困难的信息。此外，由于评估的复杂性，调查的间隔受到很大限制。该研究项目的目的是调查可持续衡量的困难指标，并将其转化为算法。该算法旨在自动化功能分析中的设计难点，从而为大量参与者客观地识别它们。在功能分析的设计任务的处理过程中的参与者的眼动跟踪注视数据作为数据基础。使用凝视数据而不是定性数据采集（例如，观察协议，成绩单）旨在提高评估期间的客观性。对于半自动记录，有必要确定注视数据中的困难的指标。在眼动数据的基础上，建立了三种不同的评价方法，并进行了比较。为此目的（1.）个体眼动跟踪度量，（2.）基于机器学习的眼睛跟踪度量的组合，以及（3.）在算法中开发和操作特定于上下文的度量。所开发的算法的准确性进行了比较，回顾性地指出的参与者的困难。随着开发的算法，应该有可能客观地检查大量参与者的功能分析过程。因此，可以确定方法支持需求，并在方法研究的框架内提出或制定个人培训内容。