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

Yueh Min Huang, Yong Ming Huang, Chien Hung Liu, Chin Chung Tsai

Research output: Contribution to journalArticle

28 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)273-289
Number of pages17
JournalInteractive Learning Environments
Volume21
Issue number3
DOIs
Publication statusPublished - 2013 Jun 1

Keywords

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

ASJC Scopus subject areas

  • Education
  • Computer Science Applications

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