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Konzeption, Erstellung und Evaluation von VR-Räumen für die betriebliche Weiterbildung in KMU
(2023)
Der Beitrag adressiert die Erstellung von Virtual-Reality gestützten (Lehr- und Lern-) Räumen für die betriebliche Weiterbildung im Rahmen eines Forschungsprojektes. Der damit verbundene Konzeptions- und Umsetzungsprozess ist mit verschiedenen Herausforderungen verbunden: einerseits ist Virtual-Reality ein vergleichsweise neues Lehr- und Lernmedium, womit wenig praktische Handreichungen zur praktischen Umsetzung existieren. Andererseits existieren theoretisch-konzeptionelle Ansätze zur Gestaltung digitaler Lehr- und Lernarrangements, die jedoch 1) oft Gefahr laufen, an den realen Bedürfnissen der Praxis „vorbei“ zu gehen und 2) zumeist nicht konkret Virtual-Reality bzw. damit verbundene Lehr- und Lernumgebungen adressieren. In dieser Folge sind Best-Practice Beispiele basierend auf erfolgreichen Umsetzungsvorhaben, die nachfolgenden Projekten als „Wegweiser“ dienen könnten, äußerst rar. Der Beitrag setzt an dieser Stelle an: basierend auf zwei real existierenden betrieblichen Anwendungsfällen aus den Bereichen Natursteinbearbeitung sowie Einzel- und Sondermaschinenbau werden Herausforderungen und Lösungswege des Erstellungsprozesses von Virtual-Reality gestützten (Lehr- und Lern-)Räumen beschrieben. Ebenfalls werden basierend auf den gemachten Projekterfahrungen Handlungsempfehlungen für die gelingende Konzeption, Umsetzung und Evaluation dieser Räume formuliert. Betriebliche Beschäftigte aus den Bereichen Aus- und Weiterbildung, Management oder Human Ressources, die in eigenen Projekten im Bereich Virtual Reality aktiv werden wollen, profitieren von den herausgestellten praktischen Handreichungen. Forschende Personen sollen Anregungen für weiterführende Forschungsvorhaben erhalten.
Yes, we can (?)
(2021)
The COVID-19 crisis has caused an extreme situation for higher education institutions around the world, where exclusively virtual teaching and learning has become obligatory rather than an additional supporting feature. This has created opportunities to explore the potential and limitations of virtual learning formats. This paper presents four theses on virtual classroom teaching and learning that are discussed critically. We use existing theoretical insights extended by empirical evidence from a survey of more than 850 students on acceptance, expectations, and attitudes regarding the positive and negative aspects of virtual teaching. The survey responses were gathered from students at different universities during the first completely digital semester (Spring-Summer 2020) in Germany. We discuss similarities and differences between the subjects being studied and highlight the advantages and disadvantages of virtual teaching and learning. Against the background of existing theory and the gathered data, we emphasize the importance of social interaction, the combination of different learning formats, and thus context-sensitive hybrid learning as the learning form of the future.
Dieses Kapitel diskutiert die Notwendigkeit einer stärkeren Praxisorientierung für die Schaffung konkreter Lehr- und Lernräume in Unternehmen und zeigt die Vorteile einer Lernfabrik vor dem Hintergrund der stattfindenden Digitalisierung als Mittel zur Kompetenzentwicklung auf. Die technologiebedingt erweiterten Weiterbildungsziele erfordern die Nutzung geeigneter Konzepte und Lösungen. Dahingehend erfolgt die zielorientierte Konkretisierung der Kreation geeigneter Lehr- und Lernsituationen. Die Darstellung der Nutzbarmachung einer Modellfabrik als Lernfabrik der betrieblichen Weiterbildungspraxis zeigt nicht nur eine Lösung für die intendierte Bereitstellung flexibler Lehr- und Lernsituationen, sondern liefert ebenso Handlungsempfehlungen und Best-Practices für die erfolgreiche Kompetenzentwicklung. Insbesondere Praktiker profitieren von der Darstellung der Lernfabrik: aus dieser können sowohl betriebliche Weiterbildner als auch Geschäftsverantwortliche Implikationen für die didaktische Transformation betrieblicher Arbeitsorte in betriebliche Lern-Orte ableiten. Die detaillierte Darstellung einer Tagesschulung zum Thema Auswirkungen von Industrie 4.0 auf die Arbeit der Mitarbeiter sowie Illustration eines Lernszenarios geben reale Einblicke, wie betriebliche Weiterbildung abseits von Lehr-Lern-Kurzschluss-orientierter Didaktik gelingt.
Process mining (PM) has established itself in recent years as a main method for visualizing and analyzing processes. However, the identification of knowledge has not been addressed adequately because PM aims solely at data-driven discovering, monitoring, and improving real-world processes from event logs available in various information systems. The following paper, therefore, outlines a novel systematic analysis view on tools for data-driven and machine learning (ML)-based identification of knowledge-intensive target processes. To support the effectiveness of the identification process, the main contributions of this study are (1) to design a procedure for a systematic review and analysis for the selection of relevant dimensions, (2) to identify different categories of dimensions as evaluation metrics to select source systems, algorithms, and tools for PM and ML as well as include them in a multi-dimensional grid box model, (3) to select and assess the most relevant dimensions of the model, (4) to identify and assess source systems, algorithms, and tools in order to find evidence for the selected dimensions, and (5) to assess the relevance and applicability of the conceptualization and design procedure for tool selection in data-driven and ML-based process mining research.
A growing number of business processes can be characterized as knowledge-intensive. The ability to speed up the transfer of knowledge between any kind of knowledge carriers in business processes with AR techniques can lead to a huge competitive advantage, for instance in manufacturing. This includes the transfer of person-bound knowledge as well as externalized knowledge of physical and virtual objects. The contribution builds on a time-dependent knowledge transfer model and conceptualizes an adaptable, AR-based application. Having the intention to accelerate the speed of knowledge transfers between a manufacturer and an information system, empirical results of an experimentation show the validity of this approach. For the first time, it will be possible to discover how to improve the transfer among knowledge carriers of an organization with knowledge-driven information systems (KDIS). Within an experiment setting, the paper shows how to improve the quantitative effects regarding the quality and amount of time needed for an example manufacturing process realization by an adaptable KDIS.
Since more and more business tasks are enabled by Artificial Intelligence (AI)-based techniques, the number of knowledge-intensive tasks increase as trivial tasks can be automated and non-trivial tasks demand human-machine interactions. With this, challenges regarding the management of knowledge workers and machines rise [9]. Furthermore, knowledge workers experience time pressure, which can lead to a decrease in output quality. Artificial Intelligence-based systems (AIS) have the potential to assist human workers in knowledge-intensive work. By providing a domain-specific language, contextual and situational awareness as well as their process embedding can be specified, which enables the management of human and AIS to ease knowledge transfer in a way that process time, cost and quality are improved significantly. This contribution outlines a framework to designing these systems and accounts for their implementation.
Process models are the basic ingredient for many attempts to improve business processes. The graphical depiction of otherwise not observable behavior in an enterprise is one of the most important techniques in the digital society. They help to enable decision making in the design of processes and workflows. Nevertheless it is not easy to correctly model business processes. Some approaches try to detect errors by an automated analysis of the process model. This contribution focuses on the creation of the first model from scratch. Which errors occur most frequently and how can these be avoided?
Faced with the triad of time-cost-quality, the realization of knowledge-intensive tasks at economic conditions is not trivial. Since the number of knowledge-intensive processes is increasing more and more nowadays, the efficient design of knowledge transfers at business processes as well as the target-oriented improvement of them is essential, so that process outcomes satisfy high quality criteria and economic requirements. This particularly challenges knowledge management, aiming for the assignment of ideal manifestations of influence factors on knowledge transfers to a certain task. Faced with first attempts of knowledge transfer-based process improvements [1], this paper continues research about the quantitative examination of knowledge transfers and presents a ready-to-go experiment design that is able to examine quality of knowledge transfers empirically and is suitable to examine knowledge transfers on a quantitative level. Its use is proven by the example of four influence factors, which namely are stickiness, complexity, competence and time pressure.
Industry 4.0, i.e. the connection of cyber-physical systems via the Internet in production and logistics, leads to considerable changes in the socio-technical system of the factory. The effects range from a considerable need for further training, which is exacerbated by the current shortage of skilled workers, to an opening of the previously inaccessible boundaries of the factory to third-party access, an increasing merging of office IT and manufacturing IT, and a new understanding of what machines can do with their data. This results in new requirements for the modeling, analysis and design of information processing and performance mapping business processes.
In the past, procedures were developed under the name of “process-oriented knowledge management” with which the exchange and use of knowledge in business processes could be represented, analyzed and improved. However, these approaches were limited to the office environment. A method that makes it possible to document, analyze and jointly optimize the new possibilities of knowledge processing by using artificial intelligence and machine learning in production and logistics in the same way and in a manner compatible with the approach in the office environment does not exist so far. The extension of the modeling language KMDL, which is described in this paper, will contribute to close this research gap.
This paper describes first approaches for an analysis and design method for a knowledge management integrating man and machine in the age of Industry 4.0.