Towards Big Data Analytics in the e-Learning Space

Попчев, Иван; Orozova, Daniela

doi:10.2478/cait-2019-0023

Cited by 24 publications

(8 citation statements)

References 4 publications

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“…We agree with the opinion of Foreman & Korallo (2014) that the prospects for their use in education are quite significant. Furthermore, the effectiveness of the use of virtual and augmented reality technologies in e-learning has been proven by a number of studies, such as Popchev & Orozova (2019), Petrov & Atanasova (2020).…”

Section: Innovative E-learning Technologies In Higher School Practicementioning

confidence: 99%

E-Learning Technologies for Future Teachers: Introduction of Educational Innovations in Higher School Practice

Lytvynov

Topolnyk

Chumak³

et al. 2022

BRAIN

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Mass introduction of e-learning in the higher education system due to spread of the pandemic has led to a search for effective information technologies and their adaptation, taking into account peculiarities of professional training of specialists in a particular field of knowledge. Based on the theoretical analysis, the conditions for ensuring professional growth of higher education teachers in accordance with the requirements of innovative development of society (improvement of professional education, continuing professional development, introduction of innovative pedagogical technologies, etc.) are established. Peculiarities of e-learning of specialists of pedagogical specialties and its functions in the process of their professional training are determined. The content of the concept of “e-learning” and ways to ensure its effectiveness (scientifically sound selection of the content of training courses, creation of discussion groups, feedback and online counselling).Innovative technologies of e-learning in the practice of higher education (gamification of the training process, use of cloud-based systems, information systems of intellectual teaching, mobile learning technologies, as well as virtual and augmented reality) are characterized. Their advantages, disadvantages and possibilities of introduction into the system of professional training of future teachers are determined. Creation of a model of continuous distance learning for high school teachers and experimental verification of its effectiveness seem promising.

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Section: Innovative E-learning Technologies In Higher School Practicementioning

confidence: 99%

E-Learning Technologies for Future Teachers: Introduction of Educational Innovations in Higher School Practice

Lytvynov

Topolnyk

Chumak³

et al. 2022

BRAIN

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“…It allows for the combination of various approaches including techniques connected to data analysis in the field of statistics, machine learning, artificial intelligence, etc. [6]. E-commerce, e-education, and e-services have been identified as important domains for analytics techniques [7].…”

Section: Introductionmentioning

confidence: 99%

“…E-commerce, e-education, and e-services have been identified as important domains for analytics techniques [7]. The science of data includes the processes of data clearing and integration, selection and transformation of data, knowledge extraction, and data analysis, evaluation and representation [6]. Machine learning and data analysis are proposed as possible solutions to explore the increased amounts of collected data generated in e-learning field with the goal of extracting knowledge and useful information, making the learning experience more efficient [8,9].…”

Section: Introductionmentioning

confidence: 99%

Learning Analytics Metrics into Online Course’s Critical Success Factors

Mourali¹,

Agrebi²,

Farhat³

et al. 2021

Advances in Intelligent Systems and Computing

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E-learning has gained tremendous popularity. In recent decades, major universities all over the world offer online courses aiming to support student learning performance, yet often exhibit low completion rates. Faced with the challenge of decreasing learners' dropout rates, e-learning communities are increasingly in need to improve trainings. Machine learning and data analysis have emerged as powerful methods to analyze educational data. They can therefore empower the Technology Enhanced Learning Environments (TELEs). Many research projects have interest in understanding dropout factors related to learners, but few works are committed to refine pedagogical content quality. To address this problem, this paper proposes descriptive statistics analysis to evaluate e-course factors contributing to its success. We take up, at first, the task of analyzing a sample of successful online courses. Then, we manage to identify features that are able to attract a large number of learners, meet their needs and improve their satisfaction. We report findings of an exploratory study that investigates the relationship between course success and the strategy of pedagogical content creation.

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“…Dynamic changes in virtualized resources, high availability, and automated fault tolerance are some of the many benefits of cloud technology over traditional IT environments. This is the reason more and more organizations are streamlining their operations and facilitating consumer access to cloud architecture [1], [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Cloud Service Selection as a Fuzzy Multi-criteria Problem

Ilieva¹,

Yankova²,

Hadjieva³

et al. 2020

TEM Journal

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Cloud adoption is an attractive technological innovation due to the capital cost reduction and fast quality improvements it provides. In this paper, we present a new fuzzy methodology for cloud service selection. Product features and functionalities, customer support, customer rating, and security options are just a few of the factors influencing cloud platforms evaluation. A practical example for ordering cloud storage systems is calculated by using fuzzy measurement of alternatives and ranking according to the compromise solution (MARCOS) method. After establishing the relevant indicators for cloud technologies’ assessment and their relative weights, crisp values and linguistic terms are transformed into triangular fuzzy numbers and then multi-criteria analysis is employed. The obtained ranking helps managers make an informed and wellgrounded decision for cloud platform selection.

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Towards Big Data Analytics in the e-Learning Space

Cited by 24 publications

References 4 publications

E-Learning Technologies for Future Teachers: Introduction of Educational Innovations in Higher School Practice

E-Learning Technologies for Future Teachers: Introduction of Educational Innovations in Higher School Practice

Learning Analytics Metrics into Online Course’s Critical Success Factors

Cloud Service Selection as a Fuzzy Multi-criteria Problem

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