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Visualizing and Assessing Reader Navigation in
Hypertext
John E. McEneaney, Ph.D.Oakland University
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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
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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
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Visualizing and Assessing Reader Navigation in Hypertext 4
Path Matrices & MetricsRepresenting Structure in Navigation
Path Distance Matrix
Path Diagram
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Empirical Validation: Study Materials
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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?
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Visualizing and Assessing Reader Navigation in Hypertext 7
Empirical Validation: Visual Analysis (Individual) High Scores Low Scores
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Visualizing and Assessing Reader Navigation in Hypertext 8
Empirical Validation: Visual Analysis (Groups)High Scores Low
Scores
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Empirical Validation: Path Metrics
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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
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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.
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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.
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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?