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#Gesellschaftslehre 7/8
(2022)
#Gesellschaftslehre 9/10
(2023)
'Tools' in public management
(2022)
Tools are methods or procedures, and thus operational patterns of action, applied in public administrations to solve standard problems. It is also possible to consider them as structured communication according to professional standards aiming at complexity reduction. Regularly, tools in management stem on a deductive-synoptic rationale offering a seemingly ‘objective’ decision basis. They have a strong formative influence on the organization, regularly also beyond the intended effects. The prominence of tools is sometimes confused with management as such, e.g. introducing tools is mistaken as equivalent to managing for a particular purpose. However, tools have to be closely and carefully managed regarding the objectives and purposes they should serve.
As AI technology is increasingly used in production systems, different approaches have emerged from highly decentralized small-scale AI at the edge level to centralized, cloud-based services used for higher-order optimizations. Each direction has disadvantages ranging from the lack of computational power at the edge level to the reliance on stable network connections with the centralized approach. Thus, a hybrid approach with centralized and decentralized components that possess specific abilities and interact is preferred. However, the distribution of AI capabilities leads to problems in self-adapting learning systems, as knowledgebases can diverge when no central coordination is present. Edge components will specialize in distinctive patterns (overlearn), which hampers their adaptability for different cases. Therefore, this paper aims to present a concept for a distributed interchangeable knowledge base in CPPS. The approach is based on various AI components and concepts for each participating node. A service-oriented infrastructure allows a decentralized, loosely coupled architecture of the CPPS. By exchanging knowledge bases between nodes, the overall system should become more adaptive, as each node can “forget” their present specialization.
In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.
In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.
A multidimensional and analytical perspective on Open Educational Practices in the 21st century
(2022)
Participatory approaches to teaching and learning are experiencing a new lease on life in the 21st century as a result of the rapid technology development. Knowledge, practices, and tools can be shared across spatial and temporal boundaries in higher education by means of Open Educational Resources, Massive Open Online Courses, and open-source technologies. In this context, the Open Education Movement calls for new didactic approaches that encourage greater learner participation in formal higher education. Based on a representative literature review and focus group research, in this study an analytical framework was developed that enables researchers and practitioners to assess the form of participation in formal, collaborative teaching and learning practices. The analytical framework is focused on the micro-level of higher education, in particular on the interaction between students and lecturers when organizing the curriculum. For this purpose, the research reflects anew on the concept of participation, taking into account existing stage models for participation in the educational context. These are then brought together with the dimensions of teaching and learning processes, such as methods, objectives and content, etc. This paper aims to make a valuable contribution to the opening up of learning and teaching, and expands the discourse around possibilities for interpreting Open Educational Practices.
A multidimensional and analytical perspective on Open Educational Practices in the 21st century
(2022)
Participatory approaches to teaching and learning are experiencing a new lease on life in the 21st century as a result of the rapid technology development. Knowledge, practices, and tools can be shared across spatial and temporal boundaries in higher education by means of Open Educational Resources, Massive Open Online Courses, and open-source technologies. In this context, the Open Education Movement calls for new didactic approaches that encourage greater learner participation in formal higher education. Based on a representative literature review and focus group research, in this study an analytical framework was developed that enables researchers and practitioners to assess the form of participation in formal, collaborative teaching and learning practices. The analytical framework is focused on the micro-level of higher education, in particular on the interaction between students and lecturers when organizing the curriculum. For this purpose, the research reflects anew on the concept of participation, taking into account existing stage models for participation in the educational context. These are then brought together with the dimensions of teaching and learning processes, such as methods, objectives and content, etc. This paper aims to make a valuable contribution to the opening up of learning and teaching, and expands the discourse around possibilities for interpreting Open Educational Practices.