visualizing and assessing reader navigation in hypertext john e. mceneaney, ph.d. oakland university

Visualizing and Assessing Reader Navigation in

Hypertext

John E. McEneaney, Ph.D.Oakland University

Visualizing and Assessing Reader Navigation in Hypertext 2

Background1. The “lost in hyperspace” problem2. Site maps and other design solutions3. 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: complexityStratum: linearity

Visualizing and Assessing Reader Navigation in Hypertext 3

Conceptual FoundationsRepresenting Structure in Hypertext

The Distance Matrix and Network Digraph

ToFrom

0 1 2 3 4 5 6 7

0 0 0 0 1 0 1 0 0

1 0 0 0 0 0 0 1 0

2 0 0 0 0 1 0 0 0

3 1 0 0 0 1 0 0 0

4 0 0 0 0 0 0 0 1

5 0 0 1 0 0 0 1 0

6 0 0 0 0 0 0 0 1

7 0 1 1 1 0 0 0 0

ToFrom

0 1 2 3 4 5 6 7

0 0 0 0 1 0 1 0 0

1 0 0 0 0 0 0 1 0

2 0 0 0 0 0 0 0

3 1 0 0 0 1 0 0 0

4 0 0 0 0 0 0 0 1

5 0 0 1 0 0 0 1 0

6 0 0 0 0 0 0 0 1

7 0 0 0 0 0

Visualizing and Assessing Reader Navigation in Hypertext 4

Path Matrices & MetricsRepresenting Structure in Navigation

Path Distance Matrix

Path Diagram

Visualizing and Assessing Reader Navigation in Hypertext 5

Empirical Validation: Study Materials

Visualizing and Assessing Reader Navigation in Hypertext 6

Empirical Validation: Design

Visual Analysis (n=29)Grouping of Ss (high & low scoring)Generate path diagramsCompare high and low scoring individualsGenerate group diagramsCompare high and low scoring groups

Path Metrics Analyses (n=89)Do measures correlate with performance?

Visualizing and Assessing Reader Navigation in Hypertext 7

Empirical Validation: Visual Analysis (Individual) High Scores Low Scores

Visualizing and Assessing Reader Navigation in Hypertext 8

Empirical Validation: Visual Analysis (Groups)High Scores Low

Scores

Visualizing and Assessing Reader Navigation in Hypertext 9

Empirical Validation: Path Metrics

Visualizing and Assessing Reader Navigation in Hypertext 10

Interpretation

Cognitive flexibility theory: Text as terrainMeta-text (TOC, glossary, etc.) as a reading toolNavigation as meta-cognitionInducing passivity in designNegative transfer of print reading skills

Visualizing and Assessing Reader Navigation in Hypertext 11

Limitations1. Weak association between metrics and

performance

Cp = .239 Sp= -.205

2. Normalization of path matricesIs path length the most appropriate basis?

3. Based on one hierarchically organized hypertext.

Visualizing and Assessing Reader Navigation in Hypertext 12

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.

Visualizing and Assessing Reader Navigation in Hypertext 13

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?