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Intelligent Techniques, Platforms and Applications of E-learning
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Intelligent Techniques, Platforms and Applications of E-learning

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

Deadline for manuscript submissions: 20 June 2025 | Viewed by 3409

Special Issue Editor

Prof. Dr. Boris Aberšek

E-Mail Website
Guest Editor
Department of Technical Education, Institute for Contemporary Technologies, Faculty for Natural Sciences and Mathematics, University of Maribor, 2000 Maribor, Slovenia
Interests: education; philosophy of mind; philosophy of AI; cognitive modelling; machine behavior; ethics in AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Technological development throughout human history has directly influenced social changes and, consequently, the well-being of society. The challenge of modern "digital" society seems even more significant, as the time dimension of technological development today is fundamentally different: exponential technological advancement, a global market, innovative entrepreneurial approaches, and various social changes are transforming the world as we know it. Adapting education to include innovative teaching methods and techniques supported by digital technology as well as such digital content and learning environments is the challenge we address in this Special Issue of Applied Science, titled “Intelligent Techniques, Platforms and Applications of E-learning”.

We expect contributions that support this field from both technical and didactic perspectives. The technical–technological aspect includes using smart and intelligent platforms as well as applications supported by artificial intelligence, virtual and augmented reality, generative artificial intelligence, learning analytics, AI-supported avatars, and more. These technologies enable new ways of interaction, individual adaptation of learning content, and the creation of more engaging and effective learning experiences. The didactic aspect focuses on learner-centered teaching, supported by innovative working methods enabled by these technologies, which include personalized learning paths, interactive learning content, collaborative learning platforms, and strategies that promote critical thinking as well as problem solving. Special attention is also given to ensuring equal access to these advanced technologies for all learners, regardless of socioeconomic circumstances.

In this Special Issue, we aim to explore and present examples of best practices, innovative research, and case studies highlighting how intelligent techniques, platforms, and applications can transform e-learning. The goal is to stimulate a broader discussion on the future of education in the digital age and to contribute to the development of effective and inclusive learning environments that will be ready for future challenges.

We invite researchers, educators, developers, and experts from various disciplines to contribute their findings, experiences, and visions for advancing e-learning. Your contributions will help shape the future of education, grounded in using intelligent techniques, platforms, and technologies for developing applications of e-learning and innovative teaching methods.

Prof. Dr. Boris Aberšek
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. 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

  • E-learning
  • innovative teaching methods
  • cognitive modeling
  • machine behavior

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Published Papers (3 papers)

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Research

22 pages, 7287 KiB  
Article
Classroom Expression Recognition Based on Deep Learning
by Yang Gao, Linyan Zhou and Jialiang He
Appl. Sci. 2025, 15(1), 166; https://doi.org/10.3390/app15010166 (registering DOI) - 28 Dec 2024
Viewed by 223
Abstract
In the field of education, classroom expression recognition technology has important application value. It can help teachers understand students’ emotions and learning attitudes and adjust teaching strategies in time to improve learning outcomes. However, in a classroom environment with a large number of [...] Read more.
In the field of education, classroom expression recognition technology has important application value. It can help teachers understand students’ emotions and learning attitudes and adjust teaching strategies in time to improve learning outcomes. However, in a classroom environment with a large number of students, it is difficult for teachers to monitor individual learning progress, resulting in low classroom management efficiency, insufficient teaching personalization, and imperfect student evaluation mechanisms. To address the above problems, this paper constructs a dataset covering five typical classroom expressions (resistance, confusion, understanding, fatigue, and neutrality). To address the problem of insufficient data volume, this paper introduces a data enhancement method based on generative adversarial networks to improve the diversity and quality of the dataset. At the same time, a multi-attention fusion network (MAF-ER) is proposed, which combines spatial attention mechanism, channel attention mechanism, and self-attention mechanism. Experimental results show that the expression recognition accuracy of the MAF-ER algorithm reaches 88.34%, which is better than the baseline method. The ablation experiment further verifies the effectiveness of the multi-level attention (MLA) mechanism. The results show that the proposed method has a high accuracy in expression recognition tasks and provides strong support for smart classrooms. Full article
(This article belongs to the Special Issue Intelligent Techniques, Platforms and Applications of E-learning)
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18 pages, 9624 KiB  
Article
A Diffusion Modeling-Based System for Teaching Dance to Digital Human
by Linyan Zhou, Jingyuan Zhao and Jialiang He
Appl. Sci. 2024, 14(19), 9084; https://doi.org/10.3390/app14199084 - 8 Oct 2024
Viewed by 939
Abstract
The introduction of artificial intelligence (AI) has triggered changes in modern dance education. This study investigates the application of diffusion-based modeling and virtual digital humans in dance instruction. Utilizing AI and digital technologies, the proposed system innovatively merges music-driven dance generation with virtual [...] Read more.
The introduction of artificial intelligence (AI) has triggered changes in modern dance education. This study investigates the application of diffusion-based modeling and virtual digital humans in dance instruction. Utilizing AI and digital technologies, the proposed system innovatively merges music-driven dance generation with virtual human-based teaching. It achieves this by extracting rhythmic and emotional information from music through audio analysis to generate corresponding dance sequences. The virtual human, functioning as a digital tutor, demonstrates dance movements in real time, enabling students to accurately learn and execute dance postures and rhythms. Analysis of the teaching outcomes, including effectiveness, naturalness, and fluidity, indicates that learning through the digital human results in enhanced user engagement and improved learning outcomes. Additionally, the diversity of dance movements is increased. This system enhances students’ motivation and learning efficacy, offering a novel approach to innovating dance education. Full article
(This article belongs to the Special Issue Intelligent Techniques, Platforms and Applications of E-learning)
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15 pages, 4574 KiB  
Article
Student Behavior Recognition in Classroom Based on Deep Learning
by Qingzheng Jia and Jialiang He
Appl. Sci. 2024, 14(17), 7981; https://doi.org/10.3390/app14177981 - 6 Sep 2024
Viewed by 1935
Abstract
With the widespread application of information technology in education, the real-time detection of student behavior in the classroom has become a key issue in improving teaching quality. This paper proposes a Student Behavior Detection (SBD) model that combines YOLOv5, the Contextual Attention (CA) [...] Read more.
With the widespread application of information technology in education, the real-time detection of student behavior in the classroom has become a key issue in improving teaching quality. This paper proposes a Student Behavior Detection (SBD) model that combines YOLOv5, the Contextual Attention (CA) mechanism and OpenPose, aiming to achieve efficient and accurate behavior recognition in complex classroom environments. By integrating YOLOv5 with the CA attention mechanism to enhance feature extraction capabilities, the model’s recognition performance in complex backgrounds, such as those with occlusion, is significantly improved. In addition, the feature map generated by the improved YOLOv5 is used to replace VGG-19 in OpenPose, which effectively improves the accuracy of student posture recognition. The experimental results demonstrate that the proposed model achieves a maximum mAP of 82.1% in complex classroom environments, surpassing Faster R-CNN by 5.2 percentage points and YOLOv5 by 4.6 percentage points. Additionally, the F1 score and R value of this model exhibit clear advantages over the other two traditional methods. This model offers an effective solution for intelligent classroom behavior analysis and the optimization of educational management. Full article
(This article belongs to the Special Issue Intelligent Techniques, Platforms and Applications of E-learning)
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