Applying social tagging to manage cognitive load in a Web 2.0 self-learning environment

Abstract

Web-based self-learning (WBSL) has received a lot of attention in recent years due to the vast amount of varied materials available in the Web 2.0 environment. However, this large amount of material also has resulted in a serious problem of cognitive overload that degrades the efficacy of learning. In this study, an information graphics method is proposed to resolve this problem. This method is based on social tagging, which is used to visualize the relationships among materials and can thus assist learners in facilitating learning. To examine the feasibility of the proposed method for managing cognitive load, an experimental model was designed in which cognitive load theory was adopted as the theoretical framework. A total of 60 university students participated in the experiment, and the partial least squares method was used to verify the experimental model. The results show that the information graphics method has a positive impact on three types of cognitive load, namely intrinsic, extraneous, and germane. Furthermore, intrinsic and germane cognitive load have a positive influence on perceived learning effectiveness, while extraneous cognitive load does not have a significant influence. One possible reason for this outcome is that the problem of visual load was not considered in the design of this study. The overall summary of the findings is that the use of social tagging can effectively manage cognitive load and positively links to perceived learning effectiveness.

Keywords:

• web-based self-learning
• information graphics method
• social tagging
• cognitive load theory
• partial least squares

Acknowledgements

The authors would like to thank the National Science Council of the Republic of China for financially supporting this research under Contract No. NSC 97-2511-S-006-001-MY3, NSC 99-2631-S-011-002, NSC 99-2631-S-006-001, and NSC 98-2631-S-006-001.