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Computers
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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 September 2025 | Viewed by 8877

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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  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (7 papers)

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Research

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19 pages, 570 KiB  
Article
Computer-Driven Assessment of Weighted Attributes for E-Learning Optimization
by Olga Ovtšarenko and Elena Safiulina
Computers 2025, 14(4), 116; https://doi.org/10.3390/computers14040116 - 23 Mar 2025
Viewed by 284
Abstract
Computer-driven assessment has revolutionized the way educational and professional assessments are conducted. Using artificial intelligence for data analytics, computer-based assessment improves efficiency, accuracy, and optimization of learning across disciplines. Optimizing e-learning requires a structured approach to analyzing learners’ progress and adjusting instruction accordingly. [...] Read more.
Computer-driven assessment has revolutionized the way educational and professional assessments are conducted. Using artificial intelligence for data analytics, computer-based assessment improves efficiency, accuracy, and optimization of learning across disciplines. Optimizing e-learning requires a structured approach to analyzing learners’ progress and adjusting instruction accordingly. Although learning effectiveness is influenced by numerous parameters, competency-based assessment provides a structured and measurable way to evaluate learners’ achievements. This study explores the application of artificial intelligence algorithms to optimize e-learners’ studying within a generalized e-course framework. A competency-based assessment model was developed using weighted parameters derived from Bloom’s taxonomy. The key contribution of this work is an innovative method for calculating competency scores using weighted attributes and a dynamic assessment parameter, making the optimization process applicable to both learners and instructors. The results indicate that using the weighted attribute method with a dynamic assessment parameter can improve the structuring of e-courses, increase learner engagement, and provide instructors with a clearer understanding of learners’ progress. The proposed approach supports data-driven decision making in e-learning, ensuring a personalized learning experience, and improving overall learning outcomes. Full article
(This article belongs to the Special Issue Present and Future of E-Learning Technologies (2nd Edition))
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17 pages, 6295 KiB  
Article
A Chatbot Student Support System in Open and Distance Learning Institutions
by Juliana Ngozi Ndunagu, Christiana Uchenna Ezeanya, Benjamin Osondu Onuorah, Jude Chukwuma Onyeakazi and Elochukwu Ukwandu
Computers 2025, 14(3), 96; https://doi.org/10.3390/computers14030096 - 7 Mar 2025
Viewed by 892
Abstract
The disruptive innovation of artificial intelligence (AI) chatbots is affecting educational dominance, which must be considered by higher educational institutions. Open and Distance Learning (ODL) becomes imperative for the effective and interactive communication between the institutions and learners. Drawbacks of isolation, motivation, insufficient [...] Read more.
The disruptive innovation of artificial intelligence (AI) chatbots is affecting educational dominance, which must be considered by higher educational institutions. Open and Distance Learning (ODL) becomes imperative for the effective and interactive communication between the institutions and learners. Drawbacks of isolation, motivation, insufficient time to study, and delay feedback mechanisms are some of the challenges encountered by ODL learners. The consequences have led to an increase in students’ attrition rate, which is one of the key issues observed by many authors facing ODL institutions. The National Open University of Nigeria (NOUN), one of the ODL institutions in Nigeria, is limited to an existing e-ticketing support system which is manually operated. A study on 2000 students of the NOUN using an online survey method revealed that 579 students responded to the questionnaire, equalling 29%. Further findings revealed significant delay time responses and inadequate resolutions as major barriers affecting the e-ticketing system in the NOUN. However, despite the quantitative method employed in the study, an artificial intelligence chatbot for automatic responses was also developed using Python 3.8+, ChatterBot (Version 1.0.5) Chatbot Framework, SQLite (default ChatterBot Storage, NLTK, and Web Interface: Flask (for integration with a web application). In testing the system, out of the 579 respondents, 370, representing 64% of the respondents, claimed that the chatbot was extremely helpful in resolving their issues and complaints. The adaptation of an AI chatbot in an ODL institution as a support system reduces the attrition rate, thereby revolutionising support services’ potential in Open and Distance Learning systems. Full article
(This article belongs to the Special Issue Present and Future of E-Learning Technologies (2nd Edition))
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21 pages, 11251 KiB  
Article
Predicting Student Performance and Enhancing Learning Outcomes: A Data-Driven Approach Using Educational Data Mining Techniques
by Athanasios Angeioplastis, John Aliprantis, Markos Konstantakis and Alkiviadis Tsimpiris
Computers 2025, 14(3), 83; https://doi.org/10.3390/computers14030083 - 27 Feb 2025
Viewed by 896
Abstract
This study investigates the use of educational data mining (EDM) techniques to predict student performance and enhance learning outcomes in higher education. Leveraging data from Moodle, a widely used learning management system (LMS), we analyzed 450 students’ academic records spanning nine semesters. Five [...] Read more.
This study investigates the use of educational data mining (EDM) techniques to predict student performance and enhance learning outcomes in higher education. Leveraging data from Moodle, a widely used learning management system (LMS), we analyzed 450 students’ academic records spanning nine semesters. Five machine learning algorithms—k-nearest neighbors, random forest, logistic regression, decision trees, and neural networks—were applied to identify correlations between courses and predict grades. The results indicated that courses with strong correlations (+0.3 and above) significantly enhanced predictive accuracy, particularly in binary classification tasks. kNN and neural networks emerged as the most robust models, achieving F1 scores exceeding 0.8. These findings underscore the potential of EDM to optimize instructional strategies and support personalized learning pathways. This study offers insights into the effective application of data-driven approaches to improve educational outcomes and foster student success. Full article
(This article belongs to the Special Issue Present and Future of E-Learning Technologies (2nd Edition))
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14 pages, 1649 KiB  
Article
CONNECT: An AI-Powered Solution for Student Authentication and Engagement in Cross-Cultural Digital Learning Environments
by Bilal Hassan, Muhammad Omer Raza, Yusra Siddiqi, Muhammad Farooq Wasiq and Rabiya Ayesha Siddiqui
Computers 2025, 14(3), 77; https://doi.org/10.3390/computers14030077 - 20 Feb 2025
Viewed by 738
Abstract
The COVID-19 pandemic accelerated the shift to digital education as universities across the world rapidly adopted virtual classrooms for remote learning. Ensuring continuous student engagement in virtual environments remains one of the key challenges. This paper discusses how AI and data analytics are [...] Read more.
The COVID-19 pandemic accelerated the shift to digital education as universities across the world rapidly adopted virtual classrooms for remote learning. Ensuring continuous student engagement in virtual environments remains one of the key challenges. This paper discusses how AI and data analytics are being applied to education, particularly the ways in which technologies such as biometrics and facial recognition can be used to improve student engagement in online and hybrid learning environments. This paper tries to revisit the dynamics of engagement across virtual platforms by comparing traditional learning models and digital learning models and showing the gaps that exist. This study reviewed six widely used video conferencing tools and their effectiveness in fostering engagement in virtual classrooms. The research goes on to investigate cross-cultural tech adoption in education—how regions and educational systems respond to these emerging technologies. Against this background of the challenges identified, a new application, “CONNECT”, is proposed in this paper that can integrate AI-driven features on face recognition and speech-to-text and attendance monitoring to enable real-time authentication and tracking of engagement. This study also provides an overview of the theoretical models of digital, hybrid, and blended learning and provides actionable recommendations for future research and innovation in cross-cultural online education. Full article
(This article belongs to the Special Issue Present and Future of E-Learning Technologies (2nd Edition))
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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 3 | Viewed by 1904
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 1377
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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Review

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31 pages, 1517 KiB  
Review
ChatGPT Integration in Higher Education for Personalized Learning, Academic Writing, and Coding Tasks: A Systematic Review
by Kaberi Naznin, Abdullah Al Mahmud, Minh Thu Nguyen and Caslon Chua
Computers 2025, 14(2), 53; https://doi.org/10.3390/computers14020053 - 7 Feb 2025
Viewed by 2935
Abstract
The emergence of ChatGPT in higher education has raised immense discussion due to its versatility in performing tasks, including coding, personalized learning, human-like conversations, and information retrieval. Despite the rapidly growing use of ChatGPT, a dire need still exists for an overarching view [...] Read more.
The emergence of ChatGPT in higher education has raised immense discussion due to its versatility in performing tasks, including coding, personalized learning, human-like conversations, and information retrieval. Despite the rapidly growing use of ChatGPT, a dire need still exists for an overarching view regarding its role and implications in educational settings. Following the PRISMA guidelines, this study represents a systematic review of 26 articles exploring the use of ChatGPT in academic writing, personalized learning, and code generation. The relevant literature was identified through electronic databases, including Scopus, ACM Digital Library, Education Research Complete, Computers & Applied Sciences, Web of Science, and IEEE Xplore. Key details from each article were extracted and synthesized narratively to provide insights into ChatGPT’s efficacy in academic writing, personalized learning, and coding. The findings indicate that ChatGPT enhances tailored learning by adapting delivery methods to individual needs, supports academic writing through error detection and content refinement, and assists in coding by offering clarifications and reusable code snippets. However, there are concerns over its ethical implications, including the impact on academic integrity, overreliance by students on AI, and privacy concerns about data use. Based on these insights, this study proposes recommendations for the ethical and responsible integration of ChatGPT into higher education, ensuring its utility while maintaining academic integrity. In addition, the results are discussed based on the relevant learning theories to understand how students engage with, learn through, and adapt to AI technologies such as ChatGPT in educational contexts. Full article
(This article belongs to the Special Issue Present and Future of E-Learning Technologies (2nd Edition))
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