The influence of leads on cognitive load and learning in a hypertext environment
Introduction
Hypertext systems, like the World Wide Web, provide a linked computer-based information storage and retrieval system, in which massive amounts of information are organized into a vast semantic network (Rada, 1989). Since its very inception, psychologists and educators have been enthusiastic about the potential of hypertext to serve as an intellectual partner for the reader (e.g., Jonassen, 2000), thereby enriching the learning experience. The unique characteristics of hypertext provide a mechanism for elaborating topics via hyperlinks, leading researchers to believe that this format of text presentation requires learners to take a more active approach to reading by interacting more directly with the content (Landow, 1992), enhances conceptual and structural knowledge acquisition (Jonassen & Wang, 1993), and results in more flexible knowledge representations that enhance transfer of learning (Spiro, Vispoel, Schmitz, Samarapungavan, & Boerger, 1987). However, despite initial hopes that using hypertext would enhance learning, little empirical evidence exists to supports these claims, and the cognitive consequences of hypertext-assisted learning continue to be debated (Calandra and Barron, 2005, DeStefano and LeFevre, 2007).
Section snippets
Theoretical framework
Reading hypertext is a task of exploration. Unlike print text, that is typically read in a sequence prescribed by the author, hypertext is presented in brief nodes that contain one or more concise expository paragraphs, for which the reader determines access sequence. Each node must be self-contained because hypertext authors cannot make assumptions about which nodes have already been read. The unique characteristics of hypertext allow hypertext authors to create connections to other related
Subjects
The initial subject pool included 22 teacher education students from a large Midwestern university. An invitation to participate was e-mailed to all undergraduate education majors (n = 687). Fifteen dollars and an opportunity to win an iPod Nano™ served as incentives. The first 22 respondents who met the study criteria were enrolled as subjects in the study; however, two subjects did not complete one or more of the experimental tasks and were dropped from the study, yielding a final subject pool
Results
We employed a repeated measures design, enabling each subject to serve as her own experimental control. This design, however, obviated the use of covariance to control for systematic between-subject differences. We used subject selection (as described in the Section 3) to help control some differences, and report here variance associated with prior domain knowledge (M = 0.91, SD = 0.08), reading ability (M = 0.40, SD = 0.07), and metacognitive awareness (M = 0.74, SD = 0.11)—three interpersonal factors
Discussion
The aim of this study was to determine the influence of leads on cognitive load and learning in hypertext. The study produced several important findings. First, EEG-based cognitive load measures showed that subjects’ brain wave activity was less intense when they were accessing hypertext nodes via leads. Conversely, the self-report of mental effort measure did not detect significant differences in cognitive load between the two experimental conditions, and subjects tended to spend more time
Conclusion
Results of this study have led us to argue that single-attribute cognitive load assessment, such as the widely used practice of measuring overall load with self-reports may not be adequate for systematically describing the causes and effects of cognitive load. Instead, cognitive load should be conceptualized as a dynamic process and assessed using a comprehensive analytical framework that integrates measures to target both the temporal dimensions of cognitive load like instantaneous load, peak
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