Cheetah Experimental Platform Web 1.0: Cleaning Pupillary Data

Recently, researchers started using cognitive load in various settings, e.g., educational psychology, cognitive load theory, or human-computer interaction. Cognitive load characterizes a tasks' demand on the limited information processing capacity of the brain. The widespread adoption of eye-tracking devices led to increased attention for objectively measuring cognitive load via pupil dilation. However, this approach requires a standardized data processing routine to reliably measure cognitive load. This technical report presents CEP-Web, an open source platform to providing state of the art data processing routines for cleaning pupillary data combined with a graphical user interface, enabling the management of studies and subjects. Future developments will include the support for analyzing the cleaned data as well as support for Task-Evoked Pupillary Response (TEPR) studies

Investigating expressiveness and understandability of hierarchy in declarative business process models

Hierarchy has widely been recognized as a viable approach to deal with the complexity of conceptual models. For instance, in declarative business process models, hierarchy is realized by sub-processes. While technical implementations of declarative sub-processes exist, their application, semantics, and the resulting impact on understandability are less understood yet—this research gap is addressed in this work. More specifically, we discuss the semantics and the application of hierarchy and show how subprocesses enhance the expressiveness of declarative modeling languages. Then, we turn to the influence of hierarchy on the understandability of declarative process models. In particular, we present a cognitive-psychology-based framework that allows to assess the impact of hierarchy on the understandability of a declarative process model. To empirically test the proposed framework, a combination of quantitative and qualitative research methods is followed. While statistical tests provide numerical evidence, think-aloud protocols give insights into the reasoning processes taking place when reading declarative process models

A visual analysis of the process of process modeling

The construction of business process models has become an important requisite in the analysis and optimization of processes. The success of the analysis and optimization efforts heavily depends on the quality of the models. Therefore, a research domain emerged that studies the process of process modeling. This paper contributes to this research by presenting a way of visualizing the different steps a modeler undertakes to construct a process model, in a so-called process of process modeling Chart. The graphical representation lowers the cognitive efforts to discover properties of the modeling process, which facilitates the research and the development of theory, training and tool support for improving model quality. The paper contains an extensive overview of applications of the tool that demonstrate its usefulness for research and practice and discusses the observations from the visualization in relation to other work. The visualization was evaluated through a qualitative study that confirmed its usefulness and added value compared to the Dotted Chart on which the visualization was inspired

The impact of working memory and the "process of process modelling" on model quality: Investigating experienced versus inexperienced modellers

A process model (PM) represents the graphical depiction of a business process, for instance, the entire process from online ordering a book until the parcel is delivered to the customer. Knowledge about relevant factors for creating PMs of high quality is lacking. The present study investigated the role of cognitive processes as well as modelling processes in creating a PM in experienced and inexperienced modellers. Specifically, two working memory (WM) functions (holding and processing of information and relational integration) and three process of process modelling phases (comprehension, modelling, and reconciliation) were related to PM quality. Our results show that the WM function of relational integration was positively related to PM quality in both modelling groups. The ratio of comprehension phases was negatively related to PM quality in inexperienced modellers and the ratio of reconciliation phases was positively related to PM quality in experienced modellers. Our research reveals central cognitive mechanisms in process modelling and has potential practical implications for the development of modelling software and teaching the craft of process modelling