1. Visualizing and Assessing Reader Navigation in Hypertext John E. McEneaney, Ph.D. Oakland University

2. Visualizing and Assessing Reader Navigation in Hypertext2 Background 1.The “lost in hyperspace” problem 2.Site maps and other design solutions 3.There is a need for empirical grounding: How do readers navigate hypertext? 4.Reader paths (trails, routes, etc.) 5.Structure in hypertext (nodes and links) 6.Structural metrics in hypertext Compactness: complexity Stratum: linearity

3. Visualizing and Assessing Reader Navigation in Hypertext3 Conceptual Foundations Representing Structure in Hypertext The Distance Matrix and Network Digraph

4. Visualizing and Assessing Reader Navigation in Hypertext4 Path Matrices & Metrics Representing Structure in Navigation Path Distance Matrix Path Diagram

5. Visualizing and Assessing Reader Navigation in Hypertext5 Empirical Validation: Study Materials

6. Visualizing and Assessing Reader Navigation in Hypertext6 Empirical Validation: Design Visual Analysis (n=29) Grouping of Ss (high & low scoring) Generate path diagrams Compare high and low scoring individuals Generate group diagrams Compare high and low scoring groups Path Metrics Analyses (n=89) Do measures correlate with performance?

7. Visualizing and Assessing Reader Navigation in Hypertext7 Empirical Validation: Visual Analysis (Individual) High ScoresLow Scores

8. Visualizing and Assessing Reader Navigation in Hypertext8 Empirical Validation: Visual Analysis (Groups) High ScoresLow Scores

9. Visualizing and Assessing Reader Navigation in Hypertext9 Empirical Validation: Path Metrics

10. Visualizing and Assessing Reader Navigation in Hypertext10 Interpretation Cognitive flexibility theory: Text as terrain Meta-text (TOC, glossary, etc.) as a reading tool Navigation as meta-cognition Inducing passivity in design Negative transfer of print reading skills

11. Visualizing and Assessing Reader Navigation in Hypertext11 Limitations 1. Weak association between metrics and performance C p =.239S p = -.205 2. Normalization of path matrices Is path length the most appropriate basis? 3. Based on one hierarchically organized hypertext.

12. Visualizing and Assessing Reader Navigation in Hypertext12 Conclusions Path visualization provides a new view on performance. Path metrics correlate significantly with performance. Metrics may prove useful as real-time measures. Reading hypertext involves new kinds of literacy skills.

13. Visualizing and Assessing Reader Navigation in Hypertext13 Speculation & Future Work Negative transfer from print reading skills? Comprehension as “mapping” (CFT). Metrics as a basis for user models. Metrics as a basis for adaptive hypertext. The order effect: What do readers learn?