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Education Sciences
Special Issues
The Future of Educational Technology
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Special Issue "The Future of Educational Technology"

A special issue of Education Sciences (ISSN 2227-7102).

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 11777

Special Issue Editors

Dr. Mohammed Saqr

E-Mail Website
Guest Editor
School of Computing, University of Eastern Finland, fi-80100 Joensuu, Finland
Interests: big data; collaborative learning; data science; educational data mining; educational technology; learning analytics; network science; social network analysis
Dr. Sonsoles López-Pernas

E-Mail Website
Guest Editor
Departamento de Ingeniería de Sistemas Telemáticos, Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain
Interests: educational escape rooms; authoring tools; learning analytics; engineering education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of educational technology has undergone major changes since its early beginnings with video projectors and instructional films. One of the main milestones was the incorporation of computers into teaching and learning, which led to the current prevalence of online learning. The rapid advances in educational technology pose a great challenge for educators to stay abreast of the latest trends and for educational institutions who need to continuously update their digital infrastructure. The recent COVID-19 pandemic has caused the adoption rate of some of these technologies to be even faster than usual.

In this Special Issue, we aim to understand the newly emerging trends in the field of educational technology. We seek contributions examining new forms of educational technology, as well as new uses for existing technology and the challenges of using and repurposing these technologies. We are also interested in the ways educational theories have evolved with the incorporation of educational technology. Moreover, we also seek to understand new privacy and ethical challenges that have emerged due to use of new and repurposed educational technology.

Dr. Mohammed Saqr
Dr. Sonsoles López-Pernas
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 double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Education Sciences 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 1400 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

  • emerging educational technologies
  • online learning
  • game-based learning
  • learning analytics
  • artificial intelligence
  • mobile learning
  • voice user interface
  • blockchain
  • virtual and augmented reality
  • ethics and privacy
  • future of education
  • industry-academia collaboration

Published Papers (4 papers)

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Research

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Article
On the Use of eXplainable Artificial Intelligence to Evaluate School Dropout
by
Elvis Melo
,
Ivanovitch Silva
,
Daniel G. Costa
,
Carlos M. D. Viegas
and
Thiago M. Barros
Educ. Sci. 2022, 12(12), 845; https://doi.org/10.3390/educsci12120845 - 22 Nov 2022
Cited by 3 | Viewed by 1776
Abstract
The school dropout problem has been recurrent in different educational areas, which has reinforced important challenges when pursuing education objectives. In this scenario, technical schools have also suffered from considerable dropout levels, even when considering a still increasing need for professionals in areas [...] Read more.
The school dropout problem has been recurrent in different educational areas, which has reinforced important challenges when pursuing education objectives. In this scenario, technical schools have also suffered from considerable dropout levels, even when considering a still increasing need for professionals in areas associated to computing and engineering. Actually, the dropout phenomenon may be not uniform and thus it has become urgent the identification of the profile of those students, putting in evidence techniques such as eXplainable Artificial Intelligence (XAI) that can ensure more ethical, transparent, and auditable use of educational data. Therefore, this article applies and evaluates XAI methods to predict students in school dropout situation, considering a database of students from the Federal Institute of Rio Grande do Norte (IFRN), a Brazilian technical school. For that, a checklist was created comprising explanatory evaluation metrics according to a broad literature review, resulting in the proposal of a new explainability index to evaluate XAI frameworks. Doing so, we expect to support the adoption of XAI models to better understand school-related data, supporting important research efforts in this area. Full article
(This article belongs to the Special Issue The Future of Educational Technology)
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Review

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Review
Training and Preparing Tomorrow’s Workforce for the Fourth Industrial Revolution
by
Michael Max Bühler
,
Thorsten Jelinek
and
Konrad Nübel
Educ. Sci. 2022, 12(11), 782; https://doi.org/10.3390/educsci12110782 - 03 Nov 2022
Cited by 5 | Viewed by 4414
Abstract
We call for a paradigm shift in engineering education. We are entering the era of the Fourth Industrial Revolution (“4IR”), accelerated by Artificial Intelligence (“AI”). Disruptive changes affect all industrial sectors and society, leading to increased uncertainty that makes it impossible to predict [...] Read more.
We call for a paradigm shift in engineering education. We are entering the era of the Fourth Industrial Revolution (“4IR”), accelerated by Artificial Intelligence (“AI”). Disruptive changes affect all industrial sectors and society, leading to increased uncertainty that makes it impossible to predict what lies ahead. Therefore, gradual cultural change in education is no longer an option to ease social pain. The vast majority of engineering education and training systems, which have remained largely static and underinvested for decades, are inadequate for the emerging 4IR and AI labour markets. Nevertheless, some positive developments can be observed in the reorientation of the engineering education sector. Novel approaches to engineering education are already providing distinctive, technology-enhanced, personalised, student-centred curriculum experiences within an integrated and unified education system. We need to educate engineering students for a future whose key characteristics are volatility, uncertainty, complexity and ambiguity (“VUCA”). Talent and skills gaps are expected to increase in all industries in the coming years. The authors argue for an engineering curriculum that combines timeless didactic traditions such as Socratic inquiry, mastery-based and project-based learning and first-principles thinking with novel elements, e.g., student-centred active and e-learning with a focus on case studies, as well as visualization/metaverse and gamification elements discussed in this paper, and a refocusing of engineering skills and knowledge enhanced by AI on human qualities such as creativity, empathy and dexterity. These skills strengthen engineering students’ perceptions of the world and the decisions they make as a result. This 4IR engineering curriculum will prepare engineering students to become curious engineers and excellent collaborators who navigate increasingly complex multistakeholder ecosystems. Full article
(This article belongs to the Special Issue The Future of Educational Technology)
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Review
Empirical Findings on Learning Success and Competence Development at Learning Factories: A Scoping Review
by
Nine Reining
and
Simone Kauffeld
Educ. Sci. 2022, 12(11), 769; https://doi.org/10.3390/educsci12110769 - 29 Oct 2022
Cited by 1 | Viewed by 1150
Abstract
To meet the changing competence requirements for employees in engineering professions, education and training need to adapt accordingly. Learning factories offer various possibilities to design or integrate practice-oriented learning into training measures. Whether this approach in fact facilitates learning and competence development is [...] Read more.
To meet the changing competence requirements for employees in engineering professions, education and training need to adapt accordingly. Learning factories offer various possibilities to design or integrate practice-oriented learning into training measures. Whether this approach in fact facilitates learning and competence development is rarely investigated. For this reason, the objective of this scoping review is to analyze and summarize the existing empirical findings on learning success and competence development in learning factories regarding their evaluation methods and results. Following standardized guidelines (PRISMA, JBI) for scoping reviews, 12 databases were researched. The literature screening led to the identification of 24 publications included in the final analysis. The results indicate that a variety of evaluation methods are used to assess learning and competences at learning factories and that criteria of all four competence facets (professional, methodological, social, and self-competence) can be enhanced at learning factories in general. As many of the identified studies show potential for improvement regarding the quality of the used methods and analysis of results, further studies on these topics are needed. Evaluations should be integrated into all training measures at learning factories to ensure learning success and competence development and to be able to readjust design, structure, and didactics where necessary. Full article
(This article belongs to the Special Issue The Future of Educational Technology)
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Review
Current Interventions for the Digital Onboarding of First-Year Students in Higher Education Institutions: A Scoping Review
by
Hannes Schilling
,
Britta Wittner
and
Simone Kauffeld
Educ. Sci. 2022, 12(8), 551; https://doi.org/10.3390/educsci12080551 - 15 Aug 2022
Cited by 2 | Viewed by 2609
Abstract
Every year, students around the globe embark upon their higher education journey, making the onboarding of these students a critical task for colleges and universities. Combined with the growth in distance learning and the rapid development in technologies, the onboarding process occurs increasingly [...] Read more.
Every year, students around the globe embark upon their higher education journey, making the onboarding of these students a critical task for colleges and universities. Combined with the growth in distance learning and the rapid development in technologies, the onboarding process occurs increasingly in the digital setting. For this reason, the objective of this scoping review was to report and map interventions, which are used in digital onboarding of first-year students in higher education institutions and explore the digital settings that characterized these interventions. The PRISMA-ScR Guidelines and the JBI Manual for Evidence Synthesis guided this investigation, which included researching four databases and screening the resulting titles and abstracts to identify the 17 sources of evidence included in the final analysis. According to our results, digital and virtual onboarding interventions were categorized into four onboarding dimensions: information interventions, socialization interventions, counseling interventions, and self-study interventions. Examples of the purposes and outcomes of these onboarding interventions included the transfer of information and the socialization of incoming students. Of the five onboarding settings that were also identified in the categorization, telecommunication software and virtual environments predominated. An independently developed onboarding tool could combine the identified onboarding settings and dimensions in the future. Full article
(This article belongs to the Special Issue The Future of Educational Technology)
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