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.