Investigating Differences between Graphical and Textual Declarative Process Models

Declarative approaches to business process modeling are regarded as well suited for highly volatile environments, as they enable a high degree of flexibility. However, problems in understanding declarative process models often impede their adoption. Particularly, a study revealed that aspects that are present in both imperative and declarative process modeling languages at a graphical level-while having different semantics-cause considerable troubles. In this work we investigate whether a notation that does not contain graphical lookalikes, i.e., a textual notation, can help to avoid this problem. Even though a textual representation does not suffer from lookalikes, in our empirical study it performed worse in terms of error rate, duration and mental effort, as the textual representation forces the reader to mentally merge the textual information. Likewise, subjects themselves expressed that the graphical representation is easier to understand

The interaction of dietary carbohydrate and fat at rest and during exercise

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Seven Process Modeling Guidelines (7PMG)

When enterprises see their productivity, quality, or regulatory compliance challenged, they often set up process-aware information systems like ERP systems to bring about fundamental improvements. But without a proper understanding of the business processes that need to be supported, they are doomed to fail. Mapping business processes as graphical models is an important step in this kind of initiatives. It is estimated that organizations that had the best results spent more than 40 percent of the total project time on discovery and construction of their initial process model. Business process modeling software has greatly eased the standardization, storage, and sharing of diagrams. But despite existing tool support, there is a notable uncertainty among practitioners about how to create process models that analysts and business professionals can easily analyze and understand. Available modeling frameworks and guidelines provide insight into the major quality categories, but remain too abstract to be directly applicable in practice. Against this background, we propose a set of seven guidelines for process modeling, called 7PMG. Each of its elements is based on findings from sound quantitative research into the relationship between process modeling styles on the one hand and both model understanding and error-proneness on the other. In this way, 7PMG not only contrasts earlier work that has been criticized for its lack of empirical foundation. It also offers guidance that practitioners can apply in their business-process centered initiatives straightaway

Errores numéricos: ¿Cómo afectan a las personas con ansiedad matemática?

¿Cómo responde el cerebro de una persona con ansiedad a las matemáticas? Nuestro estudio muestra que los estudiantes con mucha ansiedad hacia las matemáticas presentan un componente llamado negatividad asociada al error (NAE) de mayor tamaño que aquellos con poca ansiedad. Esta diferencia emerge en errores en tareas numéricas, lo que sugiere que las personas con alta ansiedad son hipersensibles a la comisión de estos errores. Este hallazgo aporta nuevo conocimiento sobre las bases cerebrales de la ansiedad hacia las matemáticas y sugiere que esta hipersensibilidad al error numérico podría ser un factor determinante tanto en el origen como en el mantenimiento de esta ansiedad