Special Issue on Technology-Enhanced Learning and Learning Analytics
Department of Computer Science and Artificial Intelligence, National Pingtung University, Pingtung City 900391, Taiwan
Appl. Sci. 2023, 13(19), 10914; https://doi.org/10.3390/app131910914
Submission received: 27 September 2023
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Accepted: 29 September 2023
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Published: 2 October 2023
(This article belongs to the Special Issue Technology-Enhanced Learning and Learning Analytics)
Technology-enhanced learning and learning analytics have always been important topics in the field of education. In recent years, driven by the data-driven concept, various technology-enhanced learning approaches have been proposed to effectively collect students’ and teachers’ digital footprints, and then analyze their teaching and learning outcomes and performances [1,2,3,4,5].
This Special Issue aims to collect and present the potential challenges and problems identified in the use of technologies and learning analytics for education. This Special Issue includes five papers, covering the topics of health science education, mathematics education, English education, science education, and automated learning analytics methods. Cabero-Almenara et al. [6] provided reliable findings concerning the training of professionals in the field of Health Sciences, specifically focusing on the use of immersive reality (IR) and 360° video in the initial training of doctors at the University of Seville. Lin and Cheng [7] proposed a technology-enhanced board game to support elementary school teachers and students in conducting prime factorization education in traditional mathematics classrooms. The research results revealed that the proposed approach not only promoted the students’ learning achievements in prime factorization education, but also improved their learning motivation and attitude. Criollo-C et al. [8] explained how to use a hybrid methodology for the improvement of communicative skills in the English language. This work can help to motivate students in their learning and in improving their communicative skills in the English language. High school teachers can use this methodology as an innovation in their educational programs. Yang et al. [9] developed a visual teaching platform that can calculate the magnetic field of magnetic core inductance in real-time. This research reported that the proposed platform can play an important role in improving teaching quality, and improving students’ interest in learning and their practical ability, and could have a positive impact on cultivating comprehensive and innovative talents. Aljohani [10] explored the difference between manually assigned research labels and automatically extracted keywords for the identification of specialist Learning Analytics (LA) researchers. This research contributed to bibliometrics and sustainable research and education.
Although submissions to this Special Issue re closed, in-depth research on “Technology-Enhanced Learning and Learning Analytics” is still ongoing to address the diverse issues we face today, such as Generative AI, self-directed/regulated learning, and lifelong learning.
Acknowledgments
Thanks to all the authors and peer reviewers for their valuable contributions to this Special Issue, ‘Technology-Enhanced Learning and Learning Analytics’. I would also like to express my gratitude to all the staff and people involved in this Special Issue.
Conflicts of Interest
The author declares no conflict of interest.
References
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- Aljohani, N.R. Comparing Manually Added Research Labels and Automatically Extracted Research Keywords to Identify Specialist Researchers in Learning Analytics: A Case Study Using Google Scholar Researcher Profiles. Appl. Sci. 2023, 13, 7172. [Google Scholar] [CrossRef]
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Lin, Y.-T. Special Issue on Technology-Enhanced Learning and Learning Analytics. Appl. Sci. 2023, 13, 10914. https://doi.org/10.3390/app131910914
AMA Style
Lin Y-T. Special Issue on Technology-Enhanced Learning and Learning Analytics. Applied Sciences. 2023; 13(19):10914. https://doi.org/10.3390/app131910914
Chicago/Turabian StyleLin, Yen-Ting. 2023. "Special Issue on Technology-Enhanced Learning and Learning Analytics" Applied Sciences 13, no. 19: 10914. https://doi.org/10.3390/app131910914
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