Technologically Enhanced Teaching Practices That Engage Student Learning
A special issue of Education Sciences (ISSN 2227-7102). This special issue belongs to the section "Technology Enhanced Education".
Deadline for manuscript submissions: 15 July 2024 | Viewed by 577
Special Issue Editors
Interests: educational technology; learning analytics; cybersecurity
Special Issues, Collections and Topics in MDPI journals
Interests: ICT curriculum design and development; ICT industry placements within curriculum; student engagement and retention
Interests: ICT in education; information and communication technology; e-learning; technology enhanced learning; computers in education; online learning; online education; e-learning in higher education; blended learning; teaching and learning
Interests: deep-learning-based emotion recognition; medical image analysis; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
1) The issue introduction includes the background and the importance of this research area.
Teaching and learning in tertiary education has become more online dependent. Although online learning and teaching has a long history, it has flourished recently due to technological advancements as well as environmental demands such as COVID-19. At the same time, technology can enhance learning in a variety of other ways. Technology can support student-centered learning by providing connections between peers, staff, and resources, and making learning more engaging and accessible. Technology can assist academics with assessment, detecting plagiarism, and automated feedback. Learning management systems provide large datasets that, with the aid of learning analytics, enable a deeper understanding of how students interact with the system as well as with their peers.
2) The aim and scope of the Special Issue shall be highlighted.
This issue focuses on how technology enhances teaching and learning in tertiary education.
3) Suggested themes shall be listed.
In particular, this Special issue encourages submissions on the following topics: (1) online learning design; (2) project-based learning; (3) inclusive learning engagement in online learning; (4) technology-enhanced learning; (5) AI or deep-learning techniques applied based on interactive data including emotion; (6) learning analytics.
Dr. Soonja Yeom
Dr. Nicole Herbert
Dr. Matthew Springer
Prof. Dr. Soo-Hyung Kim
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 1800 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
- educational technology
- learning analytics
- retention
- engagement
- technology-enhanced learning
- emotion and learning
- applied AI techniques in learning
- emotion detection from students’ comments (interaction with LMS)
