Cognitive Foundations of the Process of Process Modeling
Before investigating the process of process modeling, a discussion of its cognitive foundations is required. We introduces a basic model for understanding information processing within the human mind. In particular, the concept of “chunking” is introduced. The diffferent phases of this process are described, namely comprehension, modeling, and reconciliation.
A Model of the Mind
A central insight from cognitive research is that the human brain contains specialized regions that contribute dierent functionality to the process of solving complex problems. The modal model describes the mind as being separated into different types of memory, the most important for our research being working memory, the place where comparing, computing and reasoning takes place . Although working memory is the main working area of the brain, it can store only a limited amount of information, which is forgotten after 20-30 seconds if not refreshed . The working memory’s span is measured in chunks, being able to store a more or less constant number of items . Although this capacity is reduced while performing difficult tasks, the span of working memory can be increased by suitable organization of information . For example, when asked to repeat the sequence “U N O C B S N F L”, most people miss a character or two as the number of characters exceeds the working memory’s span. However, people being familiar with acronyms might recognize and remember the sequence “UNO CBS NFL”, effectively reducing the working memory’s load from nine to three so-called “chunks” [1, 4, 5]. As modeling is related to problem solving , modelers with a better understanding of the modeling tool, the notation, or a superior ability of extracting information from requirements can utilize their working memory more efficiently when creating process models .
Process of Process Modeling—An Iterative Process
During the formalization phase process modelers are working on creating a syntactically correct process model re ecting a given domain description by interacting with the process modeling tool . This modeling process can be described as an iterative and highly exible process [4, 7], dependent on the individual modeler and the modeling task at hand . At an operational level, the modeler’s interactions with the tool would typically consist of a cycle of the three successive phases of comprehension, modeling, and reconciliation.
Comprehension. In the comprehension phase modelers try to understand the requirements to be modeled as well as the model that has been created so far. Consequently, working memory is filled with knowledge extracted from requirements and, if available, from the process model itself. The amount of information stored in working memory depends on the modeler’s abilities and her knowledge organization.
Modeling. The modeler uses the information acquired and stored in working memory during the previous comprehension phase for changing the process model. The process modeler’s utilization of working memory influences the number of modeling steps executed during the modeling phase before forcing the modeler to revisit the requirements for acquiring more information.
Reconciliation. After the modeling phase, modelers reorganize the process model (e.g., renaming of activities) and utilize the process model’s secondary notation (e.g., notation of layout, typographic cues) to enhance the process model’s understandability [9, 10]. However, the number of reconciliation phases in the process of process modeling is influenced by a modeler’s ability of placing elements correctly when creating them, alleviating the need for additional layouting. Furthermore, the factual use of secondary notation is subject to the modeler’s personal style . The improved understandability supports the comprehension phase of the subsequent iteration, as the process model becomes more comprehensible for the modeler when coming back to it . In particular, during the subsequent comprehension phase the modeler has to identify the part of the model to work on next. A better laid out model helps identifying a suitable area of the model, causing less distraction and therefore enables the modeler to store more information in working memory that can be incorporated in the process model.
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