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Applied Sciences
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ICT in Education, 2nd Edition
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ICT in Education, 2nd Edition

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 February 2025 | Viewed by 1990

Special Issue Editors

Prof. Dr. Jenny Pange

E-Mail Website1 Website2
Guest Editor
Laboratory of NT and Distance Learning, School of Education, University of Ioannina, 451 10 Ioannina, Greece
Interests: ICT in education; learning theories in digital technologies; e-learning; digitalization in education
Special Issues, Collections and Topics in MDPI journals
Dr. Zoi Nikiforidou

E-Mail Website
Guest Editor
Early Years Learning and Care Department, University of Ioannina, University of Ioannina, 451 10 Ioannina, Greece
Interests: methodological and theoretical issues on teaching and learning; ICT in Education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The idea behind publishing this Special Issue is not to predict the future of teaching information and communication technology (ICT), but to foresee the great potential of using ICT in education.

The changes that entail the transition from teaching through ICT to sustainable educational applications using ICT are often followed by the strengths of adopting them. The unprecedented public health crisis with the COVID-19 pandemic has given us a small taste of such a point, with its impact on teaching, learning, and the transition to online education in general. Education will not be the same in the post-COVID-19 era. Learning will no longer be all about teaching but about developing a new learning environment and a reliable cooperative discovery-based understanding of new concepts in unpredictable situations.

Classroom studies, case studies, and teaching practices involving ICT in education are welcome. This planned Special Issue aims to explore this field.

Prof. Dr. Jenny Pange
Dr. Zoi Nikiforidou
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

  • ICT
  • digital technologies
  • e-learning
  • digitalization in education

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Published Papers (2 papers)

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Research

27 pages, 1914 KiB  
Article
Leveraging Gamification in ICT Education: Examining Gender Differences and Learning Outcomes in Programming Courses
by Rafael Mellado, Claudio Cubillos, Rosa Maria Vicari and Gloria Gasca-Hurtado
Appl. Sci. 2024, 14(17), 7933; https://doi.org/10.3390/app14177933 - 5 Sep 2024
Viewed by 839
Abstract
This study investigates the differential effects of gamification on learning outcomes, motivation, and usability perceptions in an introductory programming course, focusing on gender differences. While gamification has shown promise for increasing student engagement in educational settings, its impact may vary across genders. An [...] Read more.
This study investigates the differential effects of gamification on learning outcomes, motivation, and usability perceptions in an introductory programming course, focusing on gender differences. While gamification has shown promise for increasing student engagement in educational settings, its impact may vary across genders. An experimental study was conducted with 88 university students randomly assigned to gamified and non-gamified groups. Learning gains were assessed through pre- and post-tests, motivational factors were measured via questionnaires, and usability perceptions were evaluated using the Technology Acceptance Model (TAM) questionnaire. Results revealed that women learned significantly more than men in the non-gamified condition, while men outperformed women in the gamified condition. Furthermore, men reported higher enjoyment, usefulness, and comfort with the gamified tool than women. Interestingly, both genders indicated greater satisfaction with the non-gamified version. These findings contribute nuanced insights into how gamification impacts genders differently in programming education, suggesting that gamification may hinder women’s learning while modestly benefiting men. The study highlights the importance for practitioners to carefully consider gender dynamics when implementing gamified approaches, potentially offering customization options or blended techniques to optimize learning outcomes for all students in programming education. Full article
(This article belongs to the Special Issue ICT in Education, 2nd Edition)
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18 pages, 2634 KiB  
Article
Toward a Comprehensive Evaluation of Student Knowledge Assessment for Art Education: A Hybrid Approach by Data Mining and Machine Learning
by Shan Wang, Hongtao Wang, Yijun Lu and Jiandong Huang
Appl. Sci. 2024, 14(12), 5020; https://doi.org/10.3390/app14125020 - 8 Jun 2024
Viewed by 611
Abstract
By analyzing students’ understanding of a certain subject’s knowledge and learning process, and evaluating their learning level, we can formulate students’ learning plans and teachers’ curricula. However, the large amount of data processing consumes a lot of manpower and time resources, which increases [...] Read more.
By analyzing students’ understanding of a certain subject’s knowledge and learning process, and evaluating their learning level, we can formulate students’ learning plans and teachers’ curricula. However, the large amount of data processing consumes a lot of manpower and time resources, which increases the burden on educators. Therefore, this study aims to use a machine learning model to build a model to evaluate students’ learning levels for art education. To improve the prediction accuracy of the model, SVM was adopted as the basic model in this study, and was combined with SSA, ISSA, and KPCA-ISSA algorithms in turn to form a composite model. Through the experimental analysis of prediction accuracy, we found that the prediction accuracy of the KPCA-ISSA-SVMM model reached the highest, at 96.7213%, while that of the SVM model was only 91.8033%. Moreover, by putting the prediction results of the four models into the confusion matrix, it can be found that with an increase in the complexity of the composite model, the probability of classification errors in model prediction gradually decreases. It can be seen from the importance experiment that the students’ achievements in target subjects (PEG) have the greatest influence on the model prediction effect, and the importance score is 9.5958. Therefore, we should pay more attention to this characteristic value when evaluating students’ learning levels. Full article
(This article belongs to the Special Issue ICT in Education, 2nd Edition)
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