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Culture, at least to some extent, is related to particular (individual and collective) experiences. In terms of education, this means that a learner, who experienced particular services in his/her past, might perceive such services as usual for educational culture and thus, expect them to be delivered in any kind of learning scenario. In German universities, education is meant to be a full-time job and usually is designed to provide a broad basis of theoretical and methodological knowledge. Achieving methodological competences is a core goal of German academic education: Once a student leaves the university, he/she is expected to decide about appropriate methods for any kind of problem (in the field of study and beyond) and how to modify the known methods in case of need. In contrast, in professional training, the learners have to study in extra-occupational situations (time is a serious issue) and might expect training that pointedly prepares them for very specific tasks. We assumed that scenarios of professional training have their own educational cultures. When designing learning contents and learning scenarios for professional training, it might be essential for the learning success to meet the learners’ expectations and contextual peculiarities.
We found remarkable differences between the results of the investigated enterprises, but even more significant diversity between the results of the German enterprises and the priory investigated German universities. As a general conclusion we can assume that generalizing research results that were solely achieved from national university students might lead to an inappropriate design of learning scenarios for particular professional contexts. Professional training for a particular enterprise should be developed according to its specific educational culture.
This work describes extensions to the well-known Distributed Coordination Function (DCF) model to account for IEEE802.11n point-to-point links. The developed extensions cover adaptions to the throughput and delay estimation for this type of link as well peculiarities of hardware and implementations within the Linux Kernel. Instead of using simulations, the approach was extensively verified on real-world deployments at various link distances. Additionally, trials were conducted to optimize the CWmin values and the number of retries to maximize throughput and minimize delay. The results of this work can be used to estimate the properties of long-distance 802.11 links beforehand, allowing the network to be planned more accurately.
Open educational resources (OERs) provide opportunities as enablers of societal development, but they also create new challenges. From the perspective of content providers and educational institutions, particularly, cultural and context-related challenges emerge. Even though barriers regarding large-scale adoption of OERs are widely discussed, empirical evidence for determining challenges in relation to particular contexts is still rare. Such context-specific barriers generally can jeopardize the acceptance of OERs and, in particular, social OER environments. We conducted a large-scale (N = 855) cross-European investigation in the school context to determine how teachers and learners perceive cultural distance as a barrier against the use of social OER environments. The findings indicate how nationality and age of the respondents are strong predictors of cultural distance barrier. The study concludes with identification of context-sensitive interventions for overcoming the related bar riers. These consequences are vital for OER initiatives and educational institutions for aligning their efforts on OER.
Application systems are often advertised with features, and features are used heavily for requirements man- agement. However, often software manufacturers only have incomplete information about the features of their software. The information is distributed over different sources, such as requirements documents, issue trackers, user manuals, and code. In this paper, we research the occurrence of feature information in open source software engineering data. We report on a case study with three open source systems. We analyze what information about features can be found in issue trackers and user documentation. Furthermore, we study the abstraction levels on which the features are described, how feature information is related, and we discuss the possibility to discover such information semi-automatically. To mirror the diversity of software development contexts, we choose open source systems, which are quite different, e.g., in the rigor of issue tracker usage. The results differ accordingly. One main result is that the user documentation did not provide more accurate information than the issue tracker compared to a provided feature list. The results also give hints on how the management of feature relevant information can be supported.
When developing new ICT systems and applications for domestic environments, rich qualitative approaches improve the understanding of the user's integral usage of technology in their daily routines and thereby inform design. This knowledge will often be reached through in-home studies, strong relationships with the users and their involvement in the design and evaluation process. However, whilst this kind of research offers valuable context insights and brings out unexpected findings, it also presents methodological, technical and organizational challenges for the study design and its underlying cooperation processes. In particular, due to heterogeneous users in households in terms of technology affinity, individual needs, age distribution, gender, social constellations, personal role assignment, project expectations, etc. it produces particular demands to collaborate with users in the design process and thereby exposes a range of practical challenges. The full-day workshop wishes to identify these practical challenges, discuss best practice and develop a roadmap for sustainable relationships for design with users.
Vor dem Hintergrund knapper Ressourcen, dem zunehmendem Reha-Bedarf und der politischen Diskussion um eine demografische Anpassung der Reha-Budgets gewinnt der Nachweis der Ergebnisqualität medizinischer Reha-Leistungen weiter an zentraler Bedeutung (z. B. Haaf, 2005; Steiner et al., 2009). Die kontinuierliche und klinikvergleichende Überprüfung der Behandlungsergebnisse ist darüber hinaus ein wichtiger Baustein eines funktionierenden Qualitätsmanagements (Schmidt et al., in press). Sie ermöglicht ein "Lernen von den Besten" und führt zu organisatorischen Lernprozessen (Toepler et. al., 2010).
This article describes an approach to rapidly prototype the parameters of a Java application run on the IBM J9 Virtual Machine in order to improve its performance. It works by analyzing VM output and searching for behavioral patterns. These patterns are matched against a list of known patterns for which rules exist that specify how to adapt the VM to a given application. Adapting the application is done by adding parameters and changing existing ones. The process is fully automated and carried out by a toolkit. The toolkit iteratively cycles through multiple possible parameter sets, benchmarks them and proposes the best alternative to the user. The user can, without any prior knowledge about the Java application or the VM improve the performance of the deployed application and quickly cycle through a multitude of different settings to benchmark them. When tested with the representative benchmarks, improvements of up to 150% were achieved.
As soon as data is noisy, knowledge as it is represented in an information system becomes unreliable. Features in databases induce equivalence relations—but knowledge discovery takes the other way round: given a relation, what could be a suitable functional description? But the relations we work on are noisy again. If we expect to record data for learning a classification of objects then it can well be the real data does not create a reflexive, symmetric and transitive relation although we know it should be. The usual approach taken here is to build the closure in order to ensure desired properties. This, however, leads to overgeneralisation rather quickly.
Software repository data, for example in issue tracking systems, include natural language text and technical information, which includes anything from log files via code snippets to stack traces. However, data mining is often only interested in one of the two types e.g. in natural language text when looking at text mining. Regardless of which type is being investigated, any techniques used have to deal with noise caused by fragments of the other type i.e. methods interested in natural language have to deal with technical fragments and vice versa. This paper proposes an approach to classify unstructured data, e.g. development documents, into natural language text and technical information using a mixture of text heuristics and agglomerative hierarchical clustering. The approach was evaluated using 225 manually annotated text passages from developer emails and issue tracker data. Using white space tokenization as a basis, the overall precision of the approach is 0.84 and the recall is 0.85.
Unexpected Situations in Service Robot Environment: Classification and Reasoning Using Naive Physics
(2014)
Robots, which are able to carry out their tasks robustly in real world environments, are not only desirable but necessary if we want them to be more welcome for a wider audience. But very often they may fail to execute their actions successfully because of insufficient information about behaviour of objects used in the actions.
In the field of domestic service robots, recovery from faults is crucial to promote user acceptance. In this context we focus in particular on some specific faults, which arise from the interaction of a robot with its real world environment. Even a well-modelled robot may fail to perform its tasks successfully due to unexpected situations, which occur while interacting. These situations occur as deviations of properties of the objects (manipulated by the robot) from their expected values. Hence, they are experienced by the robot as external faults.