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Applied Sciences
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Data-Driven Mobile Networks and Education
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Data-Driven Mobile Networks and Education

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 10106

Special Issue Editors

Prof. Dr. Agostino Marengo

E-Mail Website
Guest Editor
Department of Agricultural Sciences, Food, Natural Resources and Engineering, University of Foggia, 71122 Foggia, Italy
Interests: e-learning; computer science; EdTech
Prof. Dr. Panos Fitsilis

E-Mail Website
Guest Editor
1. Department of Business Administration, University of Thessaly, 382 21 Volos, Greece
2. School of Science & Technology, Hellenic Open University, 263 35 Patra, Greece
Interests: software engineering; agile project management; smart cities; Industry 4.0
Prof. Dr. Alessandro Pagano

E-Mail Website
Guest Editor
Department of Economics, University of Bari, 70121 Bari, Italy
Interests: e-learning technology and methodology; learning management systems; digital training; open source software development; end-user development; Internet of Things; smart environments

Special Issue Information

Dear Colleagues,

In the rapidly evolving landscape of education, the integration of data-driven mobile networks into educational technology is not just an option but a necessity. This Special Issue, titled "Data-Driven Mobile Networks and Education", aims to explore the dynamic interplay between mobile network technologies and educational frameworks. With it, we seek to understand how data analytics and mobile networks can revolutionize learning environments, pedagogical strategies, and educational outcomes.

We are particularly interested in research that examines the impact of mobile networks on e-learning platforms, the use of big data in personalized learning experiences, and the challenges of implementing these technologies in higher-education settings. Contributions may include, but are not limited to, studies on adaptive learning systems, mobile learning applications, data privacy and security in educational contexts, and the scalability of network infrastructures in education. This issue will provide a platform for discussing innovative approaches, practical challenges, and future directions in integrating data-driven mobile networks into the educational technology landscape.

Prof. Dr. Agostino Marengo
Prof. Dr. Panos Fitsilis
Prof. Dr. Alessandro Pagano
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • data-driven learning
  • mobile networks in education
  • e-learning technologies
  • higher-education innovation
  • big data in education
  • adaptive learning systems
  • mobile learning applications
  • educational data analytics
  • network infrastructure in education
  • privacy and security in e-learning

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Published Papers (2 papers)

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Research

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17 pages, 851 KiB  
Article
Grades and Learning Styles: New Software for Analyzing Correlations in a Longitudinal Study Across Different Branches of Knowledge and Years
by Miguel A. Molina-Cabello, José Serrano-Angulo, Jesús Benito-Picazo and Karl Thurnhofer-Hemsi
Appl. Sci. 2024, 14(24), 11941; https://doi.org/10.3390/app142411941 - 20 Dec 2024
Viewed by 743
Abstract
Knowing how students engage with their materials helps educators design methodologies to enhance the learning process. To help teachers in determining students’ average learning style profiles and analyzing potential connections between learning styles and academic performance, new freely accessible software, integrated with Moodle, [...] Read more.
Knowing how students engage with their materials helps educators design methodologies to enhance the learning process. To help teachers in determining students’ average learning style profiles and analyzing potential connections between learning styles and academic performance, new freely accessible software, integrated with Moodle, has been developed in this work. A comprehensive case study involving one thousand students across various subjects and degrees from several branches of knowledge in different years has been conducted using this tool. The analysis reveals low participation in the survey, with only 6 out of 19 courses having a participation rate that surpasses 50%), where respondents generally achieved higher grades compared to non-participants (on a grade scale from 0 to 10, participants scored on average one point higher than those who did not complete the questionnaire), suggesting that they were more motivated. Higher-level courses have higher participation rates, but overall participation has declined over the years. Despite slight differences in learning styles between courses, the general pattern remains similar. The correlation between learning styles and grades, analyzed through linear regression, shows that this model performs better when both learning styles and grade marks are distributed by score rather than by level. Full article
(This article belongs to the Special Issue Data-Driven Mobile Networks and Education)
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Other

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24 pages, 757 KiB  
Systematic Review
Mobile Learning and Its Effect on Learning Outcomes and Critical Thinking: A Systematic Review
by Liliana Pedraja-Rejas, Camila Muñoz-Fritis, Emilio Rodríguez-Ponce and David Laroze
Appl. Sci. 2024, 14(19), 9105; https://doi.org/10.3390/app14199105 - 9 Oct 2024
Viewed by 8687
Abstract
This paper explores the relationships between m-learning, learning outcomes, and developing critical thinking in university students. A systematic review of empirical articles in English published in journals indexed in the Web of Science from 2015–May 2024 was carried out. A sample of 50 [...] Read more.
This paper explores the relationships between m-learning, learning outcomes, and developing critical thinking in university students. A systematic review of empirical articles in English published in journals indexed in the Web of Science from 2015–May 2024 was carried out. A sample of 50 articles was obtained. The results show that, in most of the analyzed articles, integrating m-learning tools can potentially to improve students’ learning outcomes and critical thinking skills. Considering the above, we recommended that educators and university managers integrate and promote the adoption of new technologies in teaching methods. Several recommendations are provided for the effective integration of m-learning into learning activities, stressing the importance of teachers becoming familiar with technology-enhanced learning environments early in their training. Full article
(This article belongs to the Special Issue Data-Driven Mobile Networks and Education)
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