Definition, framework and research issues of smart learning environments - a context-aware ubiquitous learning perspective

By Gwo-Jen Hwang. Smart Learning Environments, 2014

493 citations recorded by the source corpus.

The rapid progress of mobile, wireless communication and sensing technologies has enabled the development of context-aware ubiquitous learning (u-learning) environments, which are able to detect the real-world learning status of students as well as the environmental contexts. Accordingly, appropriate information can be provided to individual students in the right place and at the right time. However, researchers have indicated that, to support students to learn in real-world contexts in smart ways, more factors need to be taken into account when designing and developing learning systems. In this paper, the definition and criteria of smart learning environments are presented from the perspective of context-aware ubiquitous learning. A framework is also presented to address the design and development considerations of smart learning environments to support both online and real-world learning activities. Moreover, some emerging technologies that might facilitate the development of smart learning environments as well as the features and criteria of smart learning are addressed. Finally, research issues related to smart learning are provided.

Subfields: Computer Science Applications, Education, Information Systems

View Paper: https://www.semanticscholar.org/paper/384db4688e4a4a94f1220d4a9a1604122e65e49d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

1. Haoran Xie
2. Dragan Gasevic
3. Gwo-Jen Hwang
4. J. Harris
5. Jinhee Kim
6. Pengcheng Jiao
7. Luyi Zheng
8. Ouyang Fan

In Education

1. Haoran Xie
2. Gwo-Jen Hwang
3. Dragan Gasevic

In Information Systems

1. Haoran Xie
2. Gwo-Jen Hwang
3. Dragan Gasevic
4. Jaclyn Broadbent

Citations in the corpus

• Vision, challenges, roles and research issues of Artificial Intelligence in Education (2020, background): “One of the crucial objectives of AI in education is the provision of personalized learning guidance or supports to individual students based on their learning status, preferences, or personal characteristics (Hwang, 2014).”