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This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.

In the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.

The authors got very satisfactory classification results.

DDPML system is specially designed to smoothly handle big data mining classification.

The purpose of this study was to investigate the mediating role of teachers' intrinsic and extrinsic motivation in the relationship between principals' perceived distributed leadership and organizational learning capability in schools.

The study employs a quantitative research design and a survey methodology. Data were collected from 400 teachers in Bahrain.

The results reveal that teachers' intrinsic and extrinsic motivation fully mediates the relationship between principals' perceived distributed leadership and organizational learning capability in schools.

The study contributes to the literature on distributed leadership, organizational learning and motivation by highlighting the important mediating role of teachers' intrinsic and extrinsic motivation in the relationship between principals' perceived distributed leadership and organizational learning capability. The study also has practical implications for school administrators by suggesting that distributed leadership practices can be an effective strategy for promoting organizational learning capability in schools.

The purpose of this paper is to propose distributed learning-based three different metaheuristic algorithms for the identification of nonlinear systems. The proposed algorithms are experimented in this study to address problems for which input data are available at different geographic locations. In addition, the models are tested for nonlinear systems with different noise conditions. In a nutshell, the suggested model aims to handle voluminous data with low communication overhead compared to traditional centralized processing methodologies.

Population-based evolutionary algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and cat swarm optimization (CSO) are implemented in a distributed form to address the system identification problem having distributed input data. Out of different distributed approaches mentioned in the literature, the study has considered incremental and diffusion strategies.

Performances of the proposed distributed learning-based algorithms are compared for different noise conditions. The experimental results indicate that CSO performs better compared to GA and PSO at all noise strengths with respect to accuracy and error convergence rate, but incremental CSO is slightly superior to diffusion CSO.

This paper employs evolutionary algorithms using distributed learning strategies and applies these algorithms for the identification of unknown systems. Very few existing studies have been reported in which these distributed learning strategies are experimented for the parameter estimation task.

This paper explores the role of distributed leadership for learning and innovation in organizations. Learning and innovation being collective interactive processes, individual leadership is not the most effective way to drive them. This paper discusses how developing a distributed approach to leadership can be useful in enhancing the effectiveness of these processes, particularly in the current context of dispersed and remote working spurred by the pandemic.

This paper draws on literature from the domains of leadership and learning to discuss how effectiveness of learning and innovation can be enhanced through the application of appropriate leadership models.

This paper brings out the importance of developing a distributed leadership approach to enhance learning and innovation in organizations. It provides actionable suggestions that can be used by organizations to develop shared leadership capabilities.

Moving away from traditional leadership models, this paper highlights the significant role that distributed leadership can play to enhance the effectiveness of collective processes such as learning and innovation. The approach is even more relevant in the current pandemic context where organizations are operating in a distributed setup with flexible work from home arrangements. Learning and innovation in such virtual, asynchronous work arrangements is a challenge. Development of distributed leadership mindset and approach can enable organizations to operate more effectively in the new normal.

This study aims to deepen understanding of the use of stereoscopic 3D technology (stereo3D) in facilitating organizational learning. The emergence of advanced virtual technologies, in particular to the stereo3D virtual reality, has fundamentally changed the ways in which organizations train their employees. However, in academic or professional studies, there is hitherto, very limited research has been found in understanding the framework of distributed organizational learning in stereo3D virtual reality.

The aim of this research is to investigate the employees’ learning achievement in both the conventional in-house training program and distributed organizational learning approach in a designed stereo3D virtual reality to deepen our understanding of this undiscovered framework. In all, 76 employees from local fashion and apparel organizations were recruited in this empirical study. The quasi-experimental method was adopted to compare the experimental and control groups. The criterion-referenced assessment scale was applied as a post-test to assess employees’ learning achievement in a customer service management training course. A tailor-made stereo3D virtual learning environment was established to conduct the virtual training program.

Surprisingly, the results of this research found no significant difference in both the groups, which indicates that, nowadays, the two completely different learning formats have become similar in organizational learning practices.

This research, therefore, suggests a new organizational learning framework with three components: a blended in-house training, a distributed enhancement program in stereo3D virtual reality and an organizational memory system.

The introduction of Web‐based course instruction into an existing degree programme offers the opportunity to re‐examine models supporting learning and the transfer of knowledge among students enrolled in such courses. By removing the barriers of time and place, instructors can create and sustain student learning communities supported by interactive communication tools grounded in asynchronous learning models. The instructor’s role moves to that of a facilitator who seeks to stimulate interactions between students and between students and the instructor, in the pursuit of improved learning and knowledge base construction.

The purpose of this paper is to propose a distributed deep learning architecture for smart cities in big data systems.

We have proposed an architectural multilayer to describe the distributed deep learning for smart cities in big data systems. The components of our system are Smart city layer, big data layer, and deep learning layer. The Smart city layer responsible for the question of Smart city components, its Internet of things, sensors and effectors, and its integration in the system, big data layer concerns data characteristics 10, and its distribution over the system. The deep learning layer is the model of our system. It is responsible for data analysis.

We apply our proposed architecture in a Smart environment and Smart energy. 10; In a Smart environment, we study the Toluene forecasting in Madrid Smart city. For Smart energy, we study wind energy foresting in Australia. Our proposed architecture can reduce the time of execution and improve the deep learning model, such as Long Term Short Memory10;.

This research needs the application of other deep learning models, such as convolution neuronal network and autoencoder.

Findings of the research will be helpful in Smart city architecture. It can provide a clear view into a Smart city, data storage, and data analysis. The 10; Toluene forecasting in a Smart environment can help the decision-maker to ensure environmental safety. The Smart energy of our proposed model can give a clear prediction of power generation.

The findings of this study are expected to contribute valuable information to decision-makers for a better understanding of the key to Smart city architecture. Its relation with data storage, processing, and data analysis.

The study aims to identify and recommend to the Marist International University College (MIUC) technologies that enhance knowledge management, with a particular focus on collaborative and distributed learning.

Nine senior full-time academic staff members were purposively selected for the study. The study employed a qualitative research design that involved the use of Google forms to conduct an online survey to collect data from the target population, and it achieved a 100% response rate. Using content analysis, data were analysed, interpreted and presented in a mini-dissertation.

This research paper presents the findings relating to the innovative use of technologies to enhance collaborative and distributive learning. The findings confirm the existence of informal knowledge management practices at the MIUC and recommend technologies established through a review of the literature to expedite these practices.

The proposed technologies are thought to be useful in enhancing collaborative and distributed learning in academic institutions as technologies act as enablers in knowledge management within academia.

As technologies continue to emerge, there is a chance for universities to hit a stalemate in terms of identifying appropriate technologies to enable knowledge management. This paper contributes by identifying not only KM practices at the university under study but also specific multimedia, social media, media sharing and brainstorming technologies from the literature that would be ideal in enhancing collaborative and distributed learning.

A number of empirical studies and evaluations in Norway and Sweden shows variabilities in the degree to which the municipalities succeed in their endeavors to support school principals’ instructional leadership practices. In response to this situation, the Norwegian and Swedish directorates of education have developed a joint collaborative design for practice learning of instructional leadership. Based on findings from two separate studies, the purpose of this paper is to contribute to theory development and improved practice for school district administrators and their subordinated school leaders.

The study draws on the data from participants who completed the program in June 2015, June 2016 and June 2017, respectively. The data are based on individual reflection documents from students on their learning and new leadership practices 4 months, 16 months and 28 months after the end of the program.

The project subjected to this study, labeled “Benchlearning,” involved learning from experiences of others, observational learning, dialogic group learning and in the final round translating what is learnt into the social and cultural context in which the individual school principal’s school is situated. When participating school principals experience observation-based learning together with trusted colleagues, followed by vicarious learning from these experiences in their schools, the authors see some facilitating factors to be of particular importance: learning infrastructure, digital tools, compulsory tasks associated with preparation and subsequent experiments with their teachers. Emerging from the analysis was a systematic balancing act of autonomy and structure running through the various learning activities. Finally, a strong evidence was found that developing core competence in digital learning and formative assessment among teaching staff required enhanced distributed leadership across the whole school organization. By sharing leadership tasks on instructional issues with teachers and other non-leaders, principals succeeded in leveling up instructional leadership significantly.

The implications of the study can be summed up in the following four principles. First, policy makers should take into accounts the fact that principals’ motivation and willingness to initiate change processes can be created in a synergy between structured school visits and engagement in learning groups based on a sound theoretical foundation. Second, within a socially contracted practice in a well-designed learning group, it is possible to raise principals’ level of self-efficacy. Third, a systematic reflection process on authentic practice is an example of how principals can develop their metacognitive capacity and how knowledge can be transformed into new practice. Finally, educators should be trained to be process leaders in order to create a balance between demand and support in promoting principals’ learning of new instructional leadership practices.

School district administrators should take into accounts the fact that changing practices will be supported by sense-making processes involving discussions about how new instructional practices are justified. Specifically, shifts in talk and actions will also involve shifts in the ways people relate to each other and how they relate to their internal context. Further, leadership programs should include trying out new practices as the focal learning mode, accompanied by individual and collective reflective activities.

The findings of the study underscore the mutual interdependence of distributed leadership and student-centered focus accompanied with the school’s learning capacity as enabling conditions for principals’ practice learning in the field of instructional leadership.

This article aims to analyze emergent learning practices for globalizing work through two research questions: “What are the conceptualizations of work represented by the Virtual Factory and how do they mediate globalizing work?” and “What is the potential of expansive learning efforts to expand conceptualizations towards the emergent learning practices of globalizing work?”.

Cultural‐historical activity theory is applied, specifically the historical tool‐mediated activity, concept formation and the zone of proximal development. A dynamic hierarchy of conceptualizations forms the framework for expansive learning efforts. Data were gathered by ethnographic and development interventionist methods from a distributed engineering design project.

The paper finds that, historically, multi‐layered conceptualizations of work face developmental challenges in globalizing work. Expansive learning efforts enhance the emergent learning practices when orienting global participants to motivating “why” and “where‐to” conceptualizations. In order to turn emergent practices into sustainable learning practices, material representations need to be created to mediate the bottom‐up and top‐down conceptualizations at the interfaces of distributed work.

Emergent learning practices are studied longitudinally through concrete work in transformation. The learning approach emphasizes developmental interventions at global workplaces.

Expansive learning efforts at different levels of conceptualization, may be supported by tools that mediate and sustain emergent learning practices.

Global workplace learning should be a concern of those involved with corporate social responsibility.

Emergent learning practices offers a new approach for studies of globalizing work through its multi‐layered conceptualizations of work.