The influence of political power and ideology on quality evaluation policies in higher education

Purpose: The diversity of political views provides great opportunities for sound evaluation methods in higher education, since these are defined, enacted upon and funded through governmental processes; their implementation is constantly subjected to political pressures –This paper explores how results are evaluated as intended to occur many years after implementation of education policy and what is the role of critical political institutions such as accountability and transparency. Methods: We present as a case study the evolution of evaluation concepts in Greek universities, through a 35-year period, characterized by the shifting of political power. These observations are generalized by the results of interviews conducted with an international group of academics. Results: There is a strong link between political power and ideology and the determination of quality evaluation, leading to distinct and different outcomes, as implemented in national strategies for higher education, strongly affecting HEI’s in all aspects. Implications: In this paper we show how the state political control shapes the context of QA in universities. Universities must have the courage to protect their core values, democracy, transparency, accountability and the creation of knowledge

Measuring Democratization and Detecting State Transitions

In the proposed research, an in-depth analysis of the features presented in six representative international organizations measuring democracy worldwide revealed a sizeable set of significant and complementary indicators that provided the basis for the construction of a common conceptual framework for democratization. The size and variety of the examined datasets overcomes any possible skepticism for data biasing. We also propose a method of combining such complementary or competing indicators using Multi-objective Optimization, in order to increase objectivity. The periodic monitoring of the proposed indicators allows for the detection of State Transitions, especially under alarming conditions. Our aim is to propose an objective tool for policy makers that would eliminate selective interpretation of democracy and its transitions, by allowing political change to be meaningfully understood in its proper perspective using facts and data

The influence of political power and ideology on quality evaluation policies in higher education

Purpose: The diversity of political views provides great opportunities for sound evaluation methods in higher education, since these are defined, enacted upon and funded through governmental processes; their implementation is constantly subjected to political pressures. This paper explores how results are evaluated as intended to occur many years after implementation of education policy and what is the role of critical political institutions such as accountability and transparency. Methods: We present as a case study the evolution of evaluation concepts in Greek universities, through a 35-year period, characterized by the shifting of political power. These observations are generalized by the results of interviews conducted with an international group of academics. Results: There is a strong link between political power and ideology and the determination of quality evaluation, leading to distinct and different outcomes, as implemented in national strategies for higher education, strongly affecting HEI’s in all aspects. Implications: In this paper we show how the state political control shapes the context of QA in universities. Universities must have the courage to protect their core values, democracy, transparency, accountability and the creation of knowledge. (DIPF/Orig.

Modelling the Collapsing University

Although university’s contribution to the democratic society has been studied adequately, the establishment of its internal democratic institutions has not. Issues of autonomy and accountability exist whereas, today’s Postmodernism introduces further uncertainty. After constructing a framework for measuring democracy within a university using democracy indicators selected from international organizations, we attempt to interrelate these indicators to its democratic characteristics, raising the question: “To what extent could these characteristics be eroded before the university collapses?” Interviews with European academics were conducted and the influence of forces external to the university were studied using the Central European University in Hungary as a case study. The findings show that increased state control undermines institutional autonomy and so does imposing unnecessary restrictions. Protecting democracy and academic freedom, civil rights, and supporting an open society are of paramount importance, otherwise the university collapses. A model that captures such catastrophic state changes is finally proposed

Machine learning and deep learning based methods toward Industry 4.0 predictive maintenance in induction motors: Α state of the art survey

Purpose: Developments in Industry 4.0 technologies and Artificial Intelligence (AI) have enabled data-driven manufacturing. Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction motors (IMs). The maintenance and early FD/D of IMs are critical processes, considering that they constitute the main power source in the industrial production environment. Machine learning (ML) methods have enhanced the performance and reliability of PdM. Various deep learning (DL) based FD/D methods have emerged in recent years, providing automatic feature engineering and learning and thereby alleviating drawbacks of traditional ML based methods. This paper presents a comprehensive survey of ML and DL based FD/D methods of IMs that have emerged since 2015. An overview of the main DL architectures used for this purpose is also presented. A discussion of the recent trends is given as well as future directions for research. Design/methodology/approach: A comprehensive survey has been carried out through all available publication databases using related keywords. Classification of the reviewed works has been done according to the main ML and DL techniques and algorithms Findings: DL based PdM methods have been mainly introduced and implemented for IM fault diagnosis in recent years. Novel DL FD/D methods are based on single DL techniques as well as hybrid techniques. DL methods have also been used for signal preprocessing and moreover, have been combined with traditional ML algorithms to enhance the FD/D performance in feature engineering. Publicly available datasets have been mostly used to test the performance of the developed methods, however industrial datasets should become available as well. Multi-agent system (MAS) based PdM employing ML classifiers has been explored. Several methods have investigated multiple IM faults, however, the presence of multiple faults occurring simultaneously has rarely been investigated. Originality/value: The paper presents a comprehensive review of the recent advances in PdM of IMs based on ML and DL methods that have emerged since 2015Peer Reviewe

Editorial

This is the sixth publication of JCETR (volume 4, issue 1), starting its fourth year of publication. In previous issues, this journal presented original refereed papers, both conceptual and research-based, focused both on educational management and on education practice & research. Volume 4, issue 1 focus on furthering the journal’s scope and consolidating its position in both conceptual developments and practical applications in contemporary education theory and practice through the publication of another six quality manuscripts

Manufacturing Scheduling Using Colored Petri Nets and Reinforcement Learning

Agent-based intelligent manufacturing control systems are capable to efficiently respond and adapt to environmental changes. Manufacturing system adaptation and evolution can be addressed with learning mechanisms that increase the intelligence of agents. In this paper a manufacturing scheduling method is presented based on Timed Colored Petri Nets (CTPNs) and reinforcement learning (RL). CTPNs model the manufacturing system and implement the scheduling. In the search for an optimal solution a scheduling agent uses RL and in particular the Q-learning algorithm. A warehouse order-picking scheduling is presented as a case study to illustrate the method. The proposed scheduling method is compared to existing methods. Simulation and state space results are used to evaluate performance and identify system properties