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Application of Smart Learning in Education
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Application of Smart Learning in 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: 20 June 2025 | Viewed by 3803

Special Issue Editor

Prof. Dr. Huseyin Uzunboylu

E-Mail Website
Guest Editor
Department of Special Education, University of Kyrenia, Kyrenia, Cyprus
Interests: educational technology; media education; learning theories; curriculum; special education; didactics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart learning is an approach that uses technology and data analytics to optimize the learning processes of learners and groups. This method adapts to the needs of learners by providing personalized learning experiences. The following methods and technologies are implemented across diverse interactive environments to enhance engagement and learning outcomes:

  • Personalized learning;
  • Use of analytical tools;
  • Blended learning;
  • Artificial Intelligence (AI)-supported learning;
  • Virtual reality (VR);
  • Augmented reality (AR);
  • Blended learning;
  • Online learning;
  • Mobile Learning.

This Special Issue includes smart learning applications in all application areas of education and educational sciences, as well as all smart and interactive research topics.

Prof. Dr. Huseyin Uzunboylu
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

  • smart learning
  • personalized learning
  • virtual reality (VR)
  • augmented reality (AR)
  • online learning
  • mobile Learning

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

17 pages, 264 KiB  
Article
The Effect of a Mathematics Learning Disability Program Offered Face to Face with Interactive Online Learning from Smart Learning Environments on Teachers’ Knowledge and Self-Efficacy Levels
by Necmi Sağıroğlu, Hüseyin Uzunboylu, Gönül Akçamete and Mukaddes Sakallı Demirok
Appl. Sci. 2025, 15(10), 5326; https://doi.org/10.3390/app15105326 - 10 May 2025
Viewed by 256
Abstract
This study examines the effectiveness of in-service training programs aimed at enhancing teachers’ knowledge and self-efficacy in the context of learning disabilities (LD) in mathematics. Despite the increasing use of both interactive online learning and face-to-face training methods in professional development, limited research [...] Read more.
This study examines the effectiveness of in-service training programs aimed at enhancing teachers’ knowledge and self-efficacy in the context of learning disabilities (LD) in mathematics. Despite the increasing use of both interactive online learning and face-to-face training methods in professional development, limited research has compared their relative effectiveness in this specific field. Furthermore, existing studies have not adequately addressed whether improvements in teachers’ knowledge and self-efficacy are sustained over time. To address this gap, the present study employed a quasi-experimental design with two experimental groups. The sample consists of 80 classroom teachers, with 40 participants in the interactive online learning education group and 40 in the face-to-face education group. The training program consists of 16 h of instruction over four weeks. Data were collected using a demographic questionnaire and the Mathematics Learning Difficulty Area Teacher Self-Efficacy Scale, and statistical analyses were conducted. The findings indicate that, prior to the intervention, teachers in the interactive online learning education group exhibited significantly higher levels of knowledge and self-efficacy. However, the post-intervention results revealed no statistically significant differences between the two groups. Cohen’s d analysis indicated a moderate effect size for interactive online learning education before the intervention, which diminished to a small effect size afterward. This study validates the efficiency of interactive online learning from smart learning environments for in-service training programs aimed at enhancing teachers’ knowledge and self-efficacy about learning disabilities in mathematics. These results suggest that both training modalities effectively improve teachers’ knowledge and self-efficacy, but neither demonstrate a clear long-term advantage. This study underscores the need for further research to determine optimal strategies for sustaining professional development in this domain. Full article
(This article belongs to the Special Issue Application of Smart Learning in Education)
29 pages, 7061 KiB  
Article
Mitigating Conceptual Learning Gaps in Mixed-Ability Classrooms: A Learning Analytics-Based Evaluation of AI-Driven Adaptive Feedback for Struggling Learners
by Fawad Naseer and Sarwar Khawaja
Appl. Sci. 2025, 15(8), 4473; https://doi.org/10.3390/app15084473 - 18 Apr 2025
Viewed by 494
Abstract
Adaptation through Artificial Intelligence (AI) creates individual-centered feedback strategies to reduce academic achievement disparities among students. The study evaluates the effectiveness of AI-driven adaptive feedback in mitigating these gaps by providing personalized learning support to struggling learners. A learning analytics-based evaluation was conducted [...] Read more.
Adaptation through Artificial Intelligence (AI) creates individual-centered feedback strategies to reduce academic achievement disparities among students. The study evaluates the effectiveness of AI-driven adaptive feedback in mitigating these gaps by providing personalized learning support to struggling learners. A learning analytics-based evaluation was conducted on 700 undergraduate students enrolled in STEM-related courses across three different departments at Beaconhouse International College (BIC). The study employed a quasi-experimental design, where 350 students received AI-driven adaptive feedback while the control group followed traditional instructor-led feedback methods. Data were collected over 20 weeks, utilizing pre- and post-assessments, real-time engagement tracking, and survey responses. Results indicate that students receiving AI-driven adaptive feedback demonstrated a 28% improvement in conceptual mastery, compared to 14% in the control group. Additionally, student engagement increased by 35%, with a 22% reduction in cognitive overload. Analysis of interaction logs revealed that frequent engagement with AI-generated feedback led to a 40% increase in retention rates. Despite these benefits, variations in impact were observed based on prior knowledge levels and interaction consistency. The findings highlight the potential of AI-driven smart learning environments to enhance educational equity. Future research should explore long-term effects, scalability, and ethical considerations in adaptive AI-based learning systems. Full article
(This article belongs to the Special Issue Application of Smart Learning in Education)
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22 pages, 4593 KiB  
Article
Quality Management System in Shaping Students’ Pro-Quality Attitude in the Era of Industry 4.0
by Bartosz Spychalski
Appl. Sci. 2025, 15(8), 4227; https://doi.org/10.3390/app15084227 - 11 Apr 2025
Viewed by 354
Abstract
The significance of the quality issue in Industry 4.0 increases due to the dynamically changing economy. Not only selected workers who form the quality department must be aware of this fact, but each member of the staff must be as well. A considerable [...] Read more.
The significance of the quality issue in Industry 4.0 increases due to the dynamically changing economy. Not only selected workers who form the quality department must be aware of this fact, but each member of the staff must be as well. A considerable portion of responsibility concerning the proper preparation of workers in the field of quality relies on the education system that should help graduates develop a pro-quality attitude. In order to fulfill this aim, schools must use a number of tools, including, among others, the ISO 9001 quality management system—one of the elements introduced by the author’s model of factors influencing the development of students’ pro-quality attitude. The subject of this article is to determine the impact of the quality management system implemented in schools on the development of students’ pro-quality attitude—an issue that allows us, on the one hand, to ensure a higher level of education and, on the other hand, to use it as an element of smart learning. For the needs of the performed research, the author has collected secondary data from the literature analysis, as well as primary data from surveys performed on 1294 people. The research results deserve the attention of people who manage schools to improve the use of the quality management system implemented in schools in order to develop students’ attitudes towards quality and to improve the system itself more effectively. They are also important for enterprises, which can positively influence young people’s (future participants of the labor market based on Industry 4.0) education through cooperation with schools. The research performed by the author proved the hypothesis according to which the implementation of the quality management system is not sufficient to ensure effective development of students’ pro-quality attitudes. Full article
(This article belongs to the Special Issue Application of Smart Learning in Education)
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