Why do people fail to see simple solutions? Using think-aloud protocols to uncover the mechanism behind the Einstellung (mental set) effect

Virtual Reality design-build-test games with physics simulation: opportunities for researching design cognition

“…However, detailed critical comparisons are difficult because of substantial variation in how individual methods are applied and how they are tailored to specific research questions. For illustrations of this, see discussions about the validity of protocol studies in design and problem solving (e.g., Blech et al, 2019;Chiu & Shu, 2010); experiments in design and engineering (e.g., Cash et al, 2016;Panchal & Szajnfarber, 2017) and case studies in design and cognition (e.g., Crilly, 2019a;Wallace & Gruber, 1989). However, whatever strengths and weaknesses are identified with existing methods, the development of alternative approaches should not be seen as an attack: methodological diversity is beneficial for exploring different aspects of the phenomena of interest and providing crossmethod checks (for such arguments applied to design research see Crilly, 2019b; for wider arguments see Greene et al, 1989;Mingers, 1997;van Peer et al, 2012).…”

Studying Conceptual Modeling Processes: A Modeling Tool, Research Observatory, and Multimodal Observation Setup

“…However, to ask subjects to think aloud is a second-best approach, warranted only because it is not possible to directly access and capture cognitive processes and, thus, modeler reasoning while modeling. Modelers may have difficulties verbalizing their reasoning while modeling (Blech et al 2019) on principle accounts (because verbalizing one's own thoughts can be difficult) or on modeling-related accounts (e.g., because of the difficulty of finding the right words to express oneself). Nonetheless, among all possible alternative modes of observation, think-aloud verbalization promises the richest insight into quintessential non-directly observable cognitive processes of an individual modeling process.…”

“…2): (1) an editor for a didactically adapted, simplified variant of the Entity-Relationship model (ERM) for data modeling and (2) an editor for a subset of the Business Process Model and Notation (BPMN 2) for business process modeling (Frank and Strecker 2021). For studying individual modeling processes under laboratory conditions, in the field, and in online settings, TOOL integrates a research observatory adapted to the multimodal observation setup (Ternes et al 2020a;c). The modeling observatory presently features five modes of observation (as in-browser features):…”

“…From the outset run as a design science research project, two essential requirements drive the development of the TOOL research prototype: (1) platform independence and (2) ease of use and usability to the greatest extent economically viable. Further design considerations, requirements, and operating principles are outlined in Ternes (2017), Strecker (2018), andTernes et al (2019;c). In a nutshell, TOOL is designed and implemented as a web application with a JavaScript-driven web browser frontend and a Java EE (Enterprise Edition)-based server backend and, as a design artifact, also serves as a research laboratory for studying (web technology) software stacks and toolchains (see Fig.…”

“…The majority of the difficulties encountered by the participants relate to modeling relationship types, i.e., deciding between modeling an entity type or a relationship type, developing sensible identifiers for relationship types, and determining cardinalities Strecker 2019, Rosenthal et al 2020a). To overcome these modeling difficulties when formulating sensible identifiers (Breuker et al 2016), we presently research and develop extensions to TOOL for automated modeler assistance based on natural language processing (NLP) (Manning et al 2014) to provide tool-generated suggestions on labeling model elements at modeling time. To the best of our knowledge, this design science research is among the first to integrate a web-based data modeling tool with NLP to automatically process an arbitrary natural language description of a modeling task (in terms of its morphological structure) to identify words and phrases as suggestions for labels for model elements (Ternes et al 2020b).…”