Investigating the mechanism for automatic generation of online learning resources

ABSTRACT

Learning resources are quite important for online learning while resource provision based on algorithms could not address learners’ ubiquitous needs well. Moreover, the structure and content of resources are pre-defined which makes the “Structure” and “Content” coupled closely and could not easily adjust when learners’ needs changed. To solve this problem, an automatic resource generation mechanism is needed. In this study, we summarize the main components of resource design and proposed a “Structure-Content Loosely Coupled” resource model (Learning Cell Model). The model separates the structure and content into independent yet connected parts by defining “Dynamic Structure” and “Container”. Then, the automatic resource generation mechanism and its supporting system were designed based on the model and used in two 5th Grade classes. Results showed the mechanism and system could generate resources according to learners’ needs accurately and improve learners’ learning outcomes without increasing their cognitive load. Further, the learners had good attitude, technique acceptance, and satisfaction. Overall, the “Structure-Content Loosely Coupled” model and the proposed mechanism could be used creatively for more flexible and adaptive resource provision. They made the resource generation timely and automatic which helped teachers’ resource design. The results are enlightening and foster further research in this field.

KEYWORDS:

• Learning resource
• automatic generation
• mechanism
• online learning
• Structure-Content Loosely Coupled Model

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This research was funded by the “Research on the Cultivation and Improvement of Higher-Order Capacity of Primary and Middle School Students in Beijing” (Grant Number: CEEA21011), supported by “Project for the 14th 5 Year Plan of Beijing Education Sciences 2021(Priority Topics)”.

Notes on contributors

Qi Wang

Qi Wang is a lecturer from Beijing Foreign Studies University. His research interests include mobile learning, context-aware adaptive learning, and design of computer assisted learning environments.

Shengquan Yu

Shengquan Yu is a Professor from Beijing Normal University, he is also the Executive Director of Advanced Innovation Center for Future Education and Director of the Joint Laboratory for Mobile Learning, Ministry of Education-China Mobile Communications Corporation. He was selected in the New Century Excellent Talents Supporting Plan, Ministry of Education, China, in 2008. His research fields include mobile learning, ubiquitous learning, information technology and curriculum integration, key technologies for network learning platform and regional education informationization. He has published more than 180 academic papers, 4 books and 3 academic monographs. He participated in a number of international and national conferences as an invited speaker, including the International Conference of Educational Technology in Korea in 2013 and the computer-supported collaborative learning in 2011. He was in charge of many research projects of great significance, such as Research on Mobile Learning Development in Colleges and Universities (supported by the Science and Technology Development Center, Ministry of Education, China) in 2014, Research on Tablet-Based Innovative Teaching (supported by Intel Semiconductor (USA) Co., Ltd.) in 2014 and An Exploration into Technologically Reformed Teaching in the Future (supported by the Department of Basic Education II, Ministry of Education, China) in 2012.