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Ein Drittel der Mitarbeiter der Saint-Gobain Glass Deutschland
GmbH hat drei Jahre lang regelmäßig seinen Rücken trainiert.
Mit Erfolg, wie eine abschließende Evaluation in Zusammenarbeit
mit der FH Aachen zeigt. Die Fehltage der Trainingsteilnehmer
sind enorm zurückgegangen, während die untrainierten Kollegen
weiterhin unter Rückenbeschwerden leiden.
Domain experts regularly teach novice students how to perform a task. This often requires them to adjust their behavior to the less knowledgeable audience and, hence, to behave in a more didactic manner. Eye movement modeling examples (EMMEs) are a contemporary educational tool for displaying experts’ (natural or didactic) problem-solving behavior as well as their eye movements to learners. While research on expert-novice communication mainly focused on experts’ changes in explicit, verbal communication behavior, it is as yet unclear whether and how exactly experts adjust their nonverbal behavior. This study first investigated whether and how experts change their eye movements and mouse clicks (that are displayed in EMMEs) when they perform a task naturally versus teach a task didactically. Programming experts and novices initially debugged short computer codes in a natural manner. We first characterized experts’ natural problem-solving behavior by contrasting it with that of novices. Then, we explored the changes in experts’ behavior when being subsequently instructed to model their task solution didactically. Experts became more similar to novices on measures associated with experts’ automatized processes (i.e., shorter fixation durations, fewer transitions between code and output per click on the run button when behaving didactically). This adaptation might make it easier for novices to follow or imitate the expert behavior. In contrast, experts became less similar to novices for measures associated with more strategic behavior (i.e., code reading linearity, clicks on run button) when behaving didactically.
Researching the field of business intelligence and analytics (BI & A) has a long tradition within information systems research. Thereby, in each decade the rapid development of technologies opened new room for investigation. Since the early 1950s, the collection and analysis of structured data were the focus of interest, followed by unstructured data since the early 1990s. The third wave of BI & A comprises unstructured and sensor data of mobile devices. The article at hand aims at drawing a comprehensive overview of the status quo in relevant BI & A research of the current decade, focusing on the third wave of BI & A. By this means, the paper’s contribution is fourfold. First, a systematically developed taxonomy for BI & A 3.0 research, containing seven dimensions and 40 characteristics, is presented. Second, the results of a structured literature review containing 75 full research papers are analyzed by applying the developed taxonomy. The analysis provides an overview on the status quo of BI & A 3.0. Third, the results foster discussions on the predicted and observed developments in BI & A research of the past decade. Fourth, research gaps of the third wave of BI & A research are disclosed and concluded in a research agenda.
The number of electronic vehicles increase steadily while the space for extending the charging infrastructure is limited. In particular in urban areas, where parking spaces in attractive areas are famous, opportunities to setup new charging stations is very limited. This leads to an overload of some very attractive charging stations and an underutilization of less attractive ones. Against this background, the paper at hand presents the design of an e-vehicle reservation system that aims at distributing the utilization of the charging infrastructure, particularly in urban areas. By applying a design science approach, the requirements for a reservation-based utilization approach are elicited and a model for a suitable distribution approach and its instantiation are developed. The artefact is evaluated by simulating the distribution effects based on data of real charging station utilizations.