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Computers
Special Issues
Present and Future of E-Learning Technologies (2nd Edition)
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Present and Future of E-Learning Technologies (2nd Edition)

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 30 June 2025 | Viewed by 2388

Special Issue Editor

Prof. Dr. Antonio Sarasa Cabezuelo

E-Mail Website
Guest Editor
Facultad de Informática, Universidad Complutense de Madrid, Madrid 28040, Spain
Interests: NonSQL databases; machine learning; artificial intelligence; e-learning; programming languages
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over recent decades, the landscape of education and training has been transformed by the proliferation of e-learning, which has evolved to become more accessible, user-friendly, and omnipresent. This evolution has necessitated the development of a plethora of innovative technologies, including learning management systems, standardization specifications, tools for generating educational content, and sophisticated digital repositories enhanced by recommendation systems. Now, a new wave of technologies, predominantly rooted in artificial intelligence, such as data analysis, big data, cloud computing, and the Internet of Things, is poised to further enrich and personalize the e-learning experience of tomorrow.

Amidst these advancements, novel avenues of inquiry have emerged within the field, encompassing learning analytics, gamification, virtual assistants, and the integration of sensor technology for assessing learning processes, among others. Consequently, there arises a pivotal question: how will e-learning evolve in response to the seamless integration of these new technologies with existing software architectures and pedagogical methodologies?

In this Special Issue, we invite contributions that showcase the convergence of learning design with the implementation of innovative technologies to cultivate optimal, inclusive, and personalized learning environments. Furthermore, we encourage exploration into the symbiotic relationship between generative intelligence and e-learning, envisioning the transformative potential this alliance holds for the future of education.

We welcome submissions on a variety of topics, including, but not limited to, the following:

  • The impact of artificial intelligence on personalized learning experiences;
  • Leveraging big data in e-learning for enhanced educational outcomes;
  • Exploring the role of learning analytics in shaping pedagogical strategies;
  • Gamification techniques for engaging and motivating e-learners;
  • Integrating virtual assistants into e-learning platforms for seamless user experience;
  • Sensor technology applications in assessing and enhancing learning processes;
  • The future of e-learning: a convergence of innovative technologies and pedagogical approaches.

Authors are encouraged to contribute their research, case studies, and insights on these topics to advance our understanding of how these technologies can shape the future of e-learning.

Prof. Dr. Antonio Sarasa Cabezuelo
Guest Editor

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. Computers 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 1800 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

  • e-learning standards and specifications
  • quality in e-learning
  • instructional design, educational resources repositories
  • artificial intelligence applied to e-learning
  • creation of digital educational content
  • data science for e-learning
  • experiences in e-learning

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Published Papers (2 papers)

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Research

24 pages, 893 KiB  
Article
Why Are Other Teachers More Inclusive in Online Learning Than Us? Exploring Challenges Faced by Teachers of Blind and Visually Impaired Students: A Literature Review
by Rana Ghoneim, Wajdi Aljedaani, Renee Bryce, Yasir Javed and Zafar Iqbal Khan
Computers 2024, 13(10), 247; https://doi.org/10.3390/computers13100247 - 27 Sep 2024
Cited by 1 | Viewed by 1242
Abstract
Distance learning has grown rapidly in recent years. E-learning can aid teachers of students with disabilities, particularly visually impaired students (VISs), by offering versatility, accessibility, enhanced communication, adaptability, and a wide range of multimedia and non-verbal teaching methods. However, the shift from traditional [...] Read more.
Distance learning has grown rapidly in recent years. E-learning can aid teachers of students with disabilities, particularly visually impaired students (VISs), by offering versatility, accessibility, enhanced communication, adaptability, and a wide range of multimedia and non-verbal teaching methods. However, the shift from traditional face-to-face instruction to online platforms, especially during the pandemic, introduced unique challenges for VISs, with respect to including instructional methodologies, accessibility, and the integration of suitable technology. Recent research has shown that the resources and facilities of educational institutions pose challenges for teachers of visually impaired students (TVISs). This study conducts a literature review of research studies from the years 2000 to 2024 to identify significant issues encountered by TVISs with online learning to show the effects of distance learning before, during, and after the pandemic. This systematic literature review examines 25 publications. The evaluation reveals technological problems affecting the educational experience of visually impaired educators through a methodical categorization and analysis of these papers. The results emphasize important problems and suggest solutions, providing valuable knowledge for experts in education and legislation. The study recommends technology solutions to support instructors in providing inclusive online learning environments for VISs. Full article
(This article belongs to the Special Issue Present and Future of E-Learning Technologies (2nd Edition))
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15 pages, 1681 KiB  
Article
Parallel Attention-Driven Model for Student Performance Evaluation
by Deborah Olaniyan, Julius Olaniyan, Ibidun Christiana Obagbuwa, Bukohwo Michael Esiefarienrhe and Olorunfemi Paul Bernard
Computers 2024, 13(9), 242; https://doi.org/10.3390/computers13090242 - 23 Sep 2024
Viewed by 801
Abstract
This study presents the development and evaluation of a Multi-Task Long Short-Term Memory (LSTM) model with an attention mechanism for predicting students’ academic performance. The research is motivated by the need for efficient tools to enhance student assessment and support tailored educational interventions. [...] Read more.
This study presents the development and evaluation of a Multi-Task Long Short-Term Memory (LSTM) model with an attention mechanism for predicting students’ academic performance. The research is motivated by the need for efficient tools to enhance student assessment and support tailored educational interventions. The model tackles two tasks: predicting overall performance (total score) as a regression task and classifying performance levels (remarks) as a classification task. By handling both tasks simultaneously, it improves computational efficiency and resource utilization. The dataset includes metrics such as Continuous Assessment, Practical Skills, Presentation Quality, Attendance, and Participation. The model achieved strong results, with a Mean Absolute Error (MAE) of 0.0249, Mean Squared Error (MSE) of 0.0012, and Root Mean Squared Error (RMSE) of 0.0346 for the regression task. For the classification task, it achieved perfect scores with an accuracy, precision, recall, and F1 score of 1.0. The attention mechanism enhanced performance by focusing on the most relevant features. This study demonstrates the effectiveness of the Multi-Task LSTM model with an attention mechanism in educational data analysis, offering a reliable and efficient tool for predicting student performance. Full article
(This article belongs to the Special Issue Present and Future of E-Learning Technologies (2nd Edition))
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