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Call for Papers - Educational Big Data Analytics for Promoting Smart Learning

Guest Editors:

Dr. Bo-Chen Kuo: National Taichung University of Education, Taiwan
Dr. Cheng-Hsuan Li: National Taichung University of Education, Taiwan
Dr. Ting-Chia Hsu: National Taiwan Normal University, Taiwan
Dr. Daner Sun: The Education University of Hong Kong, Hong Kong

Submission Status: Open

Abstract or initial manuscript submission emailed to the guest editor(s): 1 November 2023
Final manuscript submission, submitted via our submission system: 29 February 2024


Smart Learning Environments is calling for submissions to our Collection on Educational Big Data Analytics for Promoting Smart Learning. This special issue aims to capture the current classroom practices of frontline teachers and the adaptive mechanisms to fulfil different students' learning paces in addition to emerging digital content, digital learning platforms, teaching tools, and pedagogical theories. Therefore, readers can understand the latest developments in digital learning, and the changes in classroom settings and the growth of children.

Meet the Guest Editors

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Bo-Chen KuoNational Taichung University of Education, Taiwan

Bo-Chen Kuo is currently the President in National Taichung University of Education and also a Distinguished Professor at the Graduate Institute of Educational Information and Measurement in the same university. His research interests include computerized adaptive learning and testing, cognitive diagnostic modeling, machine learning, and artificial intelligence in education. Email: kbc@mail.ntcu.edu.tw
 

Cheng-Hsuan Li: National Taichung University of Education, Taiwan

Cheng-Hsuan Li is a Professor at the Graduate Institute of Educational Information and Measurement in National Taichung University of Education, and is currently the director in the department of information and technology education in MOE of Taiwan. His research interests include pattern recognition; data mining; multivariate analysis; hyperspectral image analysis; collaborative problem solving; nonparametric cognitive diagnosis. Email: chenghsuanli@gmail.com
 

Ting-Chia HsuNational Taiwan Normal University, Taiwan

Ting-Chia Hsu is currently a Distinguished Professor in the Department of Technology Application and Human Resource Development in National Taiwan Normal University. Her research interests include computational thinking education and technology-enhanced learning. Email: ckhsu@ntnu.edu.tw; education.chingkun.hsu@gmail.com;
 

Daner SunThe Education University of Hong Kong, Hong Kong

Daner Sun is currently an assistant professor in the Department of Mathematics and Information Technology, The Education University of Hong Kong. Her research interests include ICT in education, mobile learning and STEM. Email: dsun@eduhk.hk
 


About the collection

Digital learning often keeps up with the latest technological trends. Recently, there have been more opportunities for cross-disciplinary cooperation and multi-directional thinking in digital learning, owing to the rapid acceleration of internet speed, the popularity of mobile devices, the application of AI models, and the development of the metaverse.

As COVID-19 has pushed the need for online learning, frontline teachers have faced numerous challenges, from being obligated to teach remotely to accepting distance learning, and experimenting with various e-learning materials and tools in the classroom. Thus, digital learning was not merely a hesitant experiment for many teachers, but has become a regular occurrence in the classroom setting.

In light of this, many governments have built on its experience of supporting mobile learning, technology-assisted self-directed learning and digital learning on a small-scale basis, and developing smart classrooms on a Forward-Looking construction basis.

Enhancing digital learning is done via the creation and funding of digital content, increasing the speed of the Internet in the classroom, acquiring mobile devices for teaching and learning, as well as teacher training and big data analysis programs in many regions.

Contrary to the past, schools have been able to implement digital learning classrooms on a large scale, in line with careful planning and implementation of hardware and software. Teachers have also been able to use technology for a long time to support their teaching and learning, and to track student growth and learning achievements.

The special issue will focus on the following topics. Scholars, experts and teachers are welcome to submit papers.

• Emerging learning analytics in digital learning and innovative teaching and learning applications for smart learning.
• Development and evaluation of diagnostic models for digital learning and classroom practice.
• Development and evaluation of digital learning software and innovative teaching and learning applications.
• Curriculum recommendation and teaching practice in digital learning classrooms based on the results of educational data mining.
• Administrative introduction to e-Learning and technology leadership practices according to the results of the educational big data analysis.
• Data-informed learning/teaching theories or revisions/reinterpretations of existing theories for smart learning.
• Review studies that provide a systematic and methodological synthesis of the existing evidence for smart learning.
• Technological infrastructures for storage, sharing, and preservation of trace data for smart learning.
• Ethics and privacy concerns related to storage, sharing, and preservation of trace data for smart learning.
• Equity and fairness of the use of emerging technologies and learning/teaching trace data for smart learning.

There are currently no articles in this collection.

Submission Guidelines

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This Collection welcomes submission of Research Articles. Before submitting your manuscript, please ensure you have read our submission guidelines. Articles for this Collection should be submitted via our submission system.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Guest Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Guest Editors have competing interests is handled by another Editorial Board Member who has no competing interests.