000 Allgemeines, Informatik, Informationswissenschaft
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Beim Online-Lernen ist es wichtig, angemessenes Feedback zu geben, damit der Schüler aus seinen Fehlern lernen und sich weiterbilden kann. Oft besteht Feedback nur aus ungenügenden Informationen, wie etwa nur aus den Worten „Richtig“ oder „Falsch“, mit denen der Schüler nicht viel anfangen kann und somit nicht aus seinen Fehlern lernen kann. Ein gutes Feedback bei inkorrekten Antworten enthält wichtige Informationen, warum eine Antwort oder Aktion falsch ist und wie sie verbessert werden kann. Bei korrekten Antworten ist ein Lob oder eine Anerkennung der richtigen Antwort ebenfalls fördernd.
In dieser Arbeit wird das Feedback des Systems XData, welches für das Erlernen von SQL (Structured Query Language) genutzt wird, verbessert. Dazu wird das aktuelle System beschrieben, um das aktuelle Feedback bei SQL-Queries beurteilen zu können. Um das aktuelle Feedback angemessen verbessern zu können, wird ein Einblick in die Themen Lernen und Feedback gegeben. Die aus den beiden Themen gewonnen Eindrücke und Erkenntnisse werden bestmöglich für das zu verbessernde Feedback genutzt. Um das System und sein Feedback beurteilen zu können, sowie das verbesserte Feedback bewerten zu können, werden verschiedene SQL-Queries (Abfragen) verwendet. Es wird die Implementierung des Feedbacks durch ein Textbausteinsystem beschrieben und die verschiedenen Feedback-Fälle vorgestellt. Abschließend werden die Resultate beschrieben und beurteilt, sowie über die Ausblicke des Systems diskutiert.
With the digitalisation, and the increased connectivity between manufacturing systems emerging in this context, manufacturing is shifting towards decentralised, distributed concepts. Still, for manufacturing scenarios manual input or augmentation of data is required at system boundaries. Especially in distributed manufacturing environments, like Cloud Manufacturing (CMfg) systems, constant changes to the available manufacturing resources and products pose challenges for establishing connections between them. We propose a feature-oriented representation of concepts, especially from the manufacturing domain, which serves as the basis for (semi-) automatically linking, e.g., manufacturing resources and products. This linking methodologies, as well as knowledge inferred using it, is then used to support distributed manufacturing, especially in CMfg environments, and enhance product development. The concepts and methodologies are to be evaluated in a real world learning factory.
Debugging errors in software applications can be a major challenge. It is not enough to know that a specific error exists, but the cause of it must be found in order to be able to fix it. Finding the source of an error can be time and cost intensive. The general approach is to analyse and debug the presumably erroneous part of the software. The analysis can be accompanied by instrumentation to gather additional information during the program execution. The analysis is made more difficult by the existence of different errors categories. Each category may need to be handled individually. Especially in embedded software applications, which commonly lack features like process or memory isolation, error detection and prevention can be even more challenging. This is the kind of problem this thesis tackles. This thesis tries to support developers during debugging and troubleshooting. The main focus is on errors related to memory management and concurrency. Specific features and properties of Arm Cortex-M processors are used to try to detect errors as well as their causes. For example, the memory protection unit is used to isolate the stack memories of different tasks running in a RTOS. The thesis tries to provide as much information as possible to the developer when reporting errors of any kind. The solution developed in this thesis also contains a custom memory allocator, which can be used to track down errors related to dynamic memory management. Furthermore, a Eclipse plugin has been developed which provides assertions for array accesses to detect and prevent out-of-bound accesses. The resulting solution has been implemented in commercial embedded software applications. This ensures that the developed solution is not only suitable for newly developed applications, but also for the integration into already existing products.
One goal of the project described in this paper is to create learning algorithms for machines and robots that lack a precise virtual controller for correct simulations. Using a digital twin approach, the developed mixed reality application aims for an overlay of a virtual robot model with the real world counterpart using Microsoft HoloLens 2 smart glasses. The application should help users to have an inside look into the results of the learning algorithm and therefore supervise and improve those results. The main focus of this paper is the visual representation of the digital twin on the smart glasses. One of the challenges is the level of abstraction and specific use of shaders (program code defining material attributes) to help the user differentiating between virtual and real objects. Therefore different presentation methods are described and evaluated. Study results with 48 persons show that the most abstract representation (wireframe) scores lowest, whereas a half-transparent model works best.