Special Issue "Technology-Enhanced Learning and Teaching: Sustainable Education"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Education and Approaches".

Deadline for manuscript submissions: 15 October 2021.

Special Issue Editors

Prof. Dr. Danial Hooshyar
E-Mail Website
Guest Editor
1. Department of Computer Science and Engineering, Korea University, 145 Seoul, Korea
2. Center of Educational Technology, University of Tartu, 51005 Tartu, Estonia.
Interests: artificial intelligence in education; educational data mining; educational technology; game-based learning; adaptivity
Special Issues and Collections in MDPI journals
Prof. Dr. Michael D. Kickmeier-Rust
E-Mail Website
Guest Editor
Institute for Educational Assessment, St.Gallen University of Teacher Education, 27,9000 St.Gallen, Switzerland
Interests: game-based learning; digital learning; game-based competence measurement; learning and test systems; learning analytics
Special Issues and Collections in MDPI journals
Dr. Nour El Mawas
E-Mail Website
Guest Editor
Lille Interuniversity Research Center in Education, University of Lille, 59000 Lille, France
Interests: MOOC; technology enhanced learning, instructional design and learning scenarios, learning management system, serious games
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The surge in the proliferation of technology-enhanced learning and teaching has provided researchers, teachers, students, instructional designers, and policy makers with rich opportunities to improve teaching and learning. The global COVID‐19 pandemic has further accelerated the use of technology-enhanced learning in an unprecedented way. This Special Issue will focus on the impact of technology-enhanced learning and teaching on society.

We invite authors to submit high-quality papers containing original research results or survey articles in, but not limited to, the following fields:

  • The design of technology-enhanced learning systems and environments;
  • The integration of emerging technologies, such as social media, web-based tools, augmented and virtual reality as well as games in teaching and learning;
  • Teachers’ readiness to use emerging technologies in classrooms;
  • Theoretical frameworks and/or practical strategies on how technology can be used to enhance teaching and learning;
  • The best practices for online teaching and learning;
  • The assessment of educational technology;
  • Psychological, social, and cultural impacts of technology in education;
  • Digital citizenship: Concept, practices, and assessment;
  • Methods used in technology-enhanced learning systems (e.g., AI in education);
  • Learning analytics and educational data mining;
  • Adaptivity and personalization in education.

You may choose our Joint Special Issue in Education Sciences.

Prof. Dr. Danial Hooshyar
Prof. Dr. Michael D. Kickmeier-Rust
Dr. Nour El Mawas
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 papers will be 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. Sustainability 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 1900 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

  • technology-enhanced learning
  • classroom
  • systems
  • environments
  • emerging technologies
  • teaching and learning
  • impacts of technology in education

Published Papers (2 papers)

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Research

Article
Applying Interactive Teaching Experience and Technology Action Puzzles in Disaster Prevention Education
Sustainability 2021, 13(9), 4788; https://doi.org/10.3390/su13094788 - 24 Apr 2021
Viewed by 553
Abstract
This study incorporated technology action puzzle and obstacle challenge activities in the course design. Using the 921 Earthquake in Taiwan as the theme, this study integrated the content of various subjects and course modules and applied information technology to present the humanistic care [...] Read more.
This study incorporated technology action puzzle and obstacle challenge activities in the course design. Using the 921 Earthquake in Taiwan as the theme, this study integrated the content of various subjects and course modules and applied information technology to present the humanistic care elements. The subjects of this study were Grade 9 students of a public middle school in central Taiwan. After the interactive operation introduction and theme film viewing, the students were divided into groups to participate in the technology action puzzle and obstacle challenge activities. Students’ learning performance using smart technological tools and overall course feedback were evaluated from the aspects of building structure safety knowledge, disaster prevention and mitigation, integrated interdisciplinary thinking, and problem-solving abilities through the course planning and quasi-experimental design. The results show that (1) in terms of the learning achievement scale, the pre-test and post-test of paired samples reached statistical significance; (2) in terms of the learning response scale, the mean of the Likert five-point scale reached above 4.0; (3) the results of mediating regression analysis show that, compared with the traditional classroom teaching mode, the interactive teaching experience and technology action puzzle have a mediating effect on learning performance and overall course feedback. Full article
(This article belongs to the Special Issue Technology-Enhanced Learning and Teaching: Sustainable Education)
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Article
A Study in the Early Prediction of ICT Literacy Ratings Using Sustainability in Data Mining Techniques
Sustainability 2021, 13(4), 2141; https://doi.org/10.3390/su13042141 - 17 Feb 2021
Cited by 1 | Viewed by 653
Abstract
It would be very beneficial to determine in advance whether a student is likely to succeed or fail within a particular learning area, and it is hypothesized that this can be accomplished by examining student patterns based on the data generated before the [...] Read more.
It would be very beneficial to determine in advance whether a student is likely to succeed or fail within a particular learning area, and it is hypothesized that this can be accomplished by examining student patterns based on the data generated before the learning process begins. Therefore, this article examines the sustainability of data-mining techniques used to predict learning outcomes. Data regarding students’ educational backgrounds and learning processes are analyzed by examining their learning patterns. When such achievement-level patterns are identified, teachers can provide the students with proactive feedback and guidance to help prevent failure. As a practical application, this study investigates students’ perceptions of computer and internet use and predicts their levels of information and communication technology literacy in advance via sustainability-in-data-mining techniques. The technique employed herein applies OneR, J48, bagging, random forest, multilayer perceptron, and sequential minimal optimization (SMO) algorithms. The highest early prediction result of approximately 69% accuracy was yielded for the SMO algorithm when using 47 attributes. Overall, via data-mining techniques, these results will aid the identification of students facing risks early on during the learning process, as well as the creation of customized learning and educational strategies for each of these students. Full article
(This article belongs to the Special Issue Technology-Enhanced Learning and Teaching: Sustainable Education)
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