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Data
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Big Data and E-learning
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Big Data and E-learning

A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Information Systems and Data Management".

Deadline for manuscript submissions: closed (30 October 2021) | Viewed by 14382

Special Issue Editors

Dr. Antonio Sarasa-Cabezuelo

E-Mail Website
Guest Editor
Facultad de Informática, Universidad Complutense de Madrid, 28001 Madrid, Spain
Interests: machine learning; artificial intelligence; e-learning; programming languages
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Santi Caballé

E-Mail Website
Guest Editor
Universitat Oberta de Catalunya, 08035 Barcelona, Spain
Interests: learning engineering; e-learning; collaborative learning; artificial intelligence; distributed computing; software engineering
Dr. Ana Fernández-Pampillón Cesteros

E-Mail Website
Guest Editor
Facultad de Filología, Universidad Complutense de Madrid, Av. Séneca, 2, 28040 Madrid, Spain
Interests: e-learning; artificial intelligence; educational technology; computational linguistics
Special Issues, Collections and Topics in MDPI journals
Dr. Joaquín Gayoso Cabada

E-Mail Website
Guest Editor
Complutense University of Madrid, 28001 Madrid, Spain
Interests: attribute grammars; computer language implementation tools; elearning; digital humanities; artificial intelligence

Special Issue Information

Dear Colleagues,

In the last decade, the Big Data phenomenon has facilitated access to enormous amounts of data on various areas of knowledge. In particular, in the field of eLearning, it is possible to access a wide variety of data on the interactions that occur in the learning process, such as data about accesses to learning management systems (activities, courses, resources, etc.), contents most visited by students, most valued resources, paths of navigation carried out by a student, etc. All this information can be exploited using machine learning techniques or artificial intelligence to obtain valuable information such as behavior patterns, predictions about grades, adaptive learning paths to students, etc. In this sense, data analysis techniques applied to the field of eLearning constitute a critical area to improve the learning–teaching process and the interactions that take place in eLearning environments.

In this Special Issue, we are interested in receiving contributions on descriptions of data sets of the eLearning area that can be exploited, analysis of data on large datasets related to eLearning, applications of artificial intelligence or machine learning techniques that use data from the field of eLearning, and other experiences that are related to the field of Big Data applied to eLearning.

Dr. Antonio Sarasa-Cabezuelo
Dr. Santi Caballé Llobet
Dr. Ana Fernández-Pampillón Cesteros
Dr. Joaquín Gayoso Cabada
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • eLearning datasets
  • Machine learning for eLearning
  • Learning analytics and educational data mining
  • Personalized learning paths
  • Adaptive learning
  • Deep learning for eLearning
  • Artificial intelligence, knowledge management in eLearning and real-time streaming processes
  • Data acquisition, integration, cleaning, and best practices
  • Computational modeling, data integration, and cloud computing
  • Algorithms and systems for big data search
  • Visualization analytics for big data
  • Analysis of data from MOOCs

Published Papers (3 papers)

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20 pages, 609 KiB  
Article
Factors That Affect E-Learning Platforms after the Spread of COVID-19: Post Acceptance Study
by Rana Saeed Al-Maroof, Khadija Alhumaid, Iman Akour and Said Salloum
Data 2021, 6(5), 49; https://doi.org/10.3390/data6050049 - 12 May 2021
Cited by 23 | Viewed by 5545
Abstract
The fear of vaccines has led to population rejection due to various reasons. Students have had their own inquiries towards the effectiveness of the vaccination, which leads to vaccination hesitancy. Vaccination hesitancy can affect students’ perception, hence, acceptance of e-learning platforms. Therefore, this [...] Read more.
The fear of vaccines has led to population rejection due to various reasons. Students have had their own inquiries towards the effectiveness of the vaccination, which leads to vaccination hesitancy. Vaccination hesitancy can affect students’ perception, hence, acceptance of e-learning platforms. Therefore, this research attempts to explore the post-acceptance of e-learning platforms based on a conceptual model that has various variables. Each variable contributes differently to the post-acceptance of the e-learning platform. The research investigates the moderating role of vaccination fear on the post-acceptance of e-learning platforms among students. Thus, the study aims at exploring students’ perceptions about their post-acceptance of e-learning platforms where vaccination fear functions as a moderator. The current study depends on an online questionnaire that is composed of 29 items. The total number of respondents is 630. The collected data was implemented to test the study model and the proposed constructs and hypotheses depending on the Smart PLS Software. Fear of vaccination has a significant impact on the acceptance of e-learning platforms, and it is a strong mediator in the conceptual model. The findings indicate a positive effect of the fear of vaccination as a mediator in the variables: perceived ease of use and usefulness, perceived daily routine, perceived critical mass and perceived self-efficiency. The implication gives a deep insight to take effective steps in reducing the level of fear of vaccination, supporting the vaccination confidence among educators, teachers and students who will, in turn, affect the society as a whole. Full article
(This article belongs to the Special Issue Big Data and E-learning)
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11 pages, 1209 KiB  
Data Descriptor
Dataset of Search Results Organized as Learning Paths Recommended by Experts to Support Search as Learning
by Verónica Proaño-Ríos and Roberto González-Ibáñez
Data 2020, 5(4), 92; https://doi.org/10.3390/data5040092 - 27 Sep 2020
Cited by 2 | Viewed by 2924
Abstract
In this article, we introduce a dataset of curated learning paths (LPs) to support search as learning. LPs were obtained through an online survey delivered to experts in different domains. Data were then analyzed and described in terms of a set of variables. [...] Read more.
In this article, we introduce a dataset of curated learning paths (LPs) to support search as learning. LPs were obtained through an online survey delivered to experts in different domains. Data were then analyzed and described in terms of a set of variables. The resulting dataset comprised 83 LPs, each containing three web pages, for an overall collection consisting of 249 documents. The dataset is intended to provide information scientists, education researchers, and industry professionals, who provide information services in educational contexts, a valuable resource to (i) investigate patterns in the order of LPs, (ii) improve ranking models and/or re-ranking methods, (iii) explain the structure of the recommended LPs, and (iv) investigate alternative approaches to display search results based on the features of LPs. Full article
(This article belongs to the Special Issue Big Data and E-learning)
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6 pages, 439 KiB  
Data Descriptor
Data on Vietnamese Students’ Acceptance of Using VCTs for Distance Learning during the COVID-19 Pandemic
by Duc-Hoa Pho, Xuan-An Nguyen, Dinh-Hai Luong, Hoai-Thu Nguyen, Thi-Phuong-Thao Vu and Thi-Thuong-Thuong Nguyen
Data 2020, 5(3), 83; https://doi.org/10.3390/data5030083 - 11 Sep 2020
Cited by 7 | Viewed by 4810
Abstract
The outbreak of COVID-19 at the beginning of 2020 has heavily influenced education all around the world. In Vietnam, educational institutes were suspended, and distance learning was conducted to ensure students’ learning process, with distance learning occurring mainly via video conferencing tools (VTCs). [...] Read more.
The outbreak of COVID-19 at the beginning of 2020 has heavily influenced education all around the world. In Vietnam, educational institutes were suspended, and distance learning was conducted to ensure students’ learning process, with distance learning occurring mainly via video conferencing tools (VTCs). The purpose of this paper is to provide data on Vietnamese students’ acceptance of using VCTs in distance learning during the COVID-19 pandemic through an extended technology acceptance model (TAM) and structural equation modeling (SEM) method. This study used the TAM of Venkatesh and Davis. The questionnaire was designed based on Venkatesh and Davis and Salloum et al.’s scale. An online survey with snowball sampling was selected in April. The final dataset consisted of 277 valid records. This data descriptor presented descriptive statistics (mean, standard deviation), internal consistency (Cronbach’s alpha), reliability and validity measures (composite reliability, average value extracted test), and factor loading of items of eight factors: output quality, computer playfulness, subjective norm, perceived usefulness, perceived ease of use, attitude towards to use, behavioral intention to use, and actual system to use. Results indicated that external factors such as subjective norm and computer playfulness had a significant impact on most TAM constructs. Furthermore, output quality was found to have a positive influence on students’ perceived usefulness and acceptance of VCTs in distance learning. Full article
(This article belongs to the Special Issue Big Data and E-learning)
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