IoT and Big Data Analytics in E-Learning

Author(s):  
Ivan Popchev ◽  
Daniela Orozova ◽  
Stanimir Stoyanov
2018 ◽  
Vol 7 (1.8) ◽  
pp. 164 ◽  
Author(s):  
S Kusuma ◽  
D Kasi Viswanath

The internet of things & Big data analytics in eLearning brings tremendous challenges & opportunities to educational institutions & students. In recent trends, the growth of Pervasive computing, Social media, evolving IoT capabilities, technologies such as cloud computing, and big data and analytics are improving the core values of teaching and conducting research but also instilling a new digital culture and developing an IoT-centric society. The primary purpose of this paper is to provide an impact of IoT & Big data analytics in the area of E-learning and study on different E-learning approaches. 


Author(s):  
Naziha Laaz ◽  
Karzan Wakil ◽  
Sara Gotti ◽  
Zineb Gotti ◽  
Samir Mbarki

This chapter proposes a new methodology for the automatic generation of domain ontologies to support big data analytics. This method ensures the recommendations of the MDA approach by transforming UML class diagrams to domain ontologies in PSM level through ODM, which is an OMG standard for ontology modeling. In this work, the authors have focused on the model-driven architecture approach as the best solution for representing and generating ontology artifacts in an intuitive way using the UML graphical syntax. The creation of domain ontologies will form the basis for application developers to target business professional context; however, the future of big data will depend on the use of technologies to model ontologies. With that said, this work supports the combination of ontologies and big data approaches as the most efficient way to store, extract, and analyze data. It is shown using the theoretical approach and concrete results obtained after applying the proposed process to an e-learning domain ontology.


2019 ◽  
Vol 19 (3) ◽  
pp. 16-24 ◽  
Author(s):  
Ivan P. Popchev ◽  
Daniela A. Orozova

Abstract The issues related to the analysis and management of Big Data, aspects of the security, stability and quality of the data, represent a new research, and engineering challenge. In the present paper, techniques for Big Data storage, search, analysis and management in the area of the virtual e-Learning space and the problems in front of them are considered. A numerical example for explorative analysis of data about the students from Burgas Free University is applied, using instrument for Data Mining of Orange. The analysis is a base for a system for localization of students at risk.


2019 ◽  
Vol 47 (1) ◽  
pp. 2-16 ◽  
Author(s):  
Soraya Sedkaoui ◽  
Mounia Khelfaoui

Purpose With the advent of the internet and communication technology, the penetration of e-learning has increased. The digital data being created by the educational and research institutions is also on the ascent. The growing interest in recent years toward big data, educational data mining and learning analytics has motivated the development of new analytical ways and approaches and advancements in learning settings. The need for using big data to handle, analyze this large amount of data is prime. This trend has started attracting the interest of educational institutions which have an important role in the development skills process and the preparation of a new generation of learners. “A real revolution for education,” it is based on this kind of terms that many articles have paid attention to big data for learning. How can analytics techniques and tools be so efficient and become a great prospect for the learning process? Big data analytics, when applied into teaching and learning processes, might help to improvise as well as to develop new paradigms. In this perspective, this paper aims to investigate the most promising applications and issues of big data for the design of the next-generation of massive e-learning. Specifically, it addresses the analytical tools and approaches for enhancing the future of e-learning, pitfalls arising from the usage of large data sets. Globally, this paper focuses on the possible application of big data techniques on learning developments, to show the power of analytics and why integrating big data is so important for the learning context. Design/methodology/approach Big data has in the recent years been an area of interest among innovative sectors and has become a major priority for many industries, and learning sector cannot escape to this deluge. This paper focuses on the different methods of big data able to be used in learning context to understand the benefits it can bring both to teaching and learning process, and identify its possible impact on the future of this sector in general. This paper investigates the connection between big data and the learning context. This connection can be illustrated by identifying the several main analytics approaches, methods and tools for improving the learning process. This can be clearer by the examination of the different ways and solutions that contribute to making a learning process more agile and dynamic. The methods that were used in this research are mainly of a descriptive and analytical nature, to establish how big data and analytics methods develop the learning process, and understand their contributions and impacts in addressing learning issues. To this end, authors have collected and reviewed existing literature related to big data in education and the technology application in the learning context. Authors then have done the same process with dynamic and operational examples of big data for learning. In this context, the authors noticed that there are jigsaw bits that contained important knowledge on the different parts of the research area. The process concludes by outlining the role and benefit of the related actors and highlighting the several directions relating to the development and implementation of an efficient learning process based on big data analytics. Findings Big data analytics, its techniques, tools and algorithms are important to improve the learning context. The findings in this paper suggest that the incorporation of an approach based on big data is of crucial importance. This approach can improve the learning process, for this, its implementation must be correctly aligned with educational strategies and learning needs. Research limitations/implications This research represents a reference to better understanding the influence and the role of big data in educational dynamic. In addition, it leads to improve existing literature about big data for learning. The limitations of the paper are given by its nature derived from a theoretical perspective, and the discussed ideas can be empirically validated by identifying how big data helps in addressing learning issues. Originality/value Over the time, the process that leads to the acquisition of the knowledge uses and receives more technological tools and components; this approach has contributed to the development of information communication and the interactive learning context. Technology applications continue to expand the boundaries of education into an “anytime/anywhere” experience. This technology and its wide use in the learning system produce a vast amount of different kinds of data. These data are still rarely exploited by educational practitioners. Its successful exploitation conducts educational actors to achieve their full potential in a complex and uncertain environment. The general motivation for this research is assisting higher educational institutions to better understand the impact of the big data as a success factor to develop their learning process and achieve their educational strategy and goals. This study contributes to better understand how big data analytics solutions are turned into operational actions and will be particularly valuable to improve learning in educational institutions.


2019 ◽  
Vol 54 (5) ◽  
pp. 20
Author(s):  
Dheeraj Kumar Pradhan

2020 ◽  
Vol 49 (5) ◽  
pp. 11-17
Author(s):  
Thomas Wrona ◽  
Pauline Reinecke

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.


2019 ◽  
Vol 7 (2) ◽  
pp. 273-277
Author(s):  
Ajay Kumar Bharti ◽  
Neha Verma ◽  
Deepak Kumar Verma

2017 ◽  
Vol 49 (004) ◽  
pp. 825--830
Author(s):  
A. AHMED ◽  
R.U. AMIN ◽  
M. R. ANJUM ◽  
I. ULLAH ◽  
I. S. BAJWA

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