visualizing and assessing reader navigation in hypertext john e. mceneaney, ph.d. oakland university
Post on 20-Dec-2015
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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?