Research on the Evolution and Classification of Digital Learning Resources
- DOI
- 10.2991/assehr.k.200401.025How to use a DOI?
- Keywords
- digital learning resources, information technology, connotation, classification
- Abstract
With the maturity of new technologies such as big data, virtual reality and artificial intelligence, the types of digital learning resources are constantly enriched, The current hot topic of academic circles is how to systematically classify digital learning resources, and then provide guidance for exploring the construction, supply and application of different types of digital learning resources. This study cuts into the research of foreign AECT on digital learning resources and research results of domestic digital learning resources. At the same time, it combines the typical information technology in the education field, and summarizes the classification framework of digital learning resources, and uses the compass principle as a guide to summarize the digital learning resource classification framework and divide the digital learning resources. The framework divides digital learning resources into six categories: basic knowledge classes, auxiliary extension classes, tool software classes, network platform classes, virtual reality classes, and generative learning resources, and proposes a digital learning resource compass from three dimensions: discipline, acquisition mode of digital learning resources and types of digital learning resources.
- Copyright
- © 2020, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Jiayang Wang AU - Meng Han AU - Wanwan Wang PY - 2020 DA - 2020/04/06 TI - Research on the Evolution and Classification of Digital Learning Resources BT - Proceedings of the International Conference on Education, Economics and Information Management (ICEEIM 2019) PB - Atlantis Press SP - 92 EP - 100 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.200401.025 DO - 10.2991/assehr.k.200401.025 ID - Wang2020 ER -