Understanding hypertext navigation through cluster analysis

Kimberly A. Lawless

doi:10.2190/DVAP-DE23-3XMV-9MXH

Return

Understanding hypertext navigation through cluster analysis

1996-06-01

115

PRE

Kimberly A. Lawless *

Jonna M. Kulikowich

DOI：10.2190/DVAP-DE23-3XMV-9MXH

Original

Original request for help

Save

Abstract

En 中文

Hypertext documents are unlike traditional text for not only do they represent a computer-based medium, but also readers can process the hypertext in a nonlinear, random access fashion. The ability to navigate through hypertext sometimes presents individuals with difficulty perhaps because these readers do not possess adequate domain knowledge or interest in the topics encountered. This study was designed to inspect the navigational profiles of participants as they process a hypertext document. Using cluster analysis, three performance profiles emerged: 1) knowledge seekers, 2) feature explorers, and, 3) apathetic hypertext users. Analyses demonstrated that domain knowledge seems to differentiate among the cluster groups. Results indicated that students who are interested in computers and hypertext but who do not possess relevant amounts of domain knowledge aligned with the text material experienced difficulty when trying to comprehend hypertext. Implications for learning, assessment, and teaching are discussed.

Keywords:

KNOWLEDGE

DESIGN

TEXTS

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.