ACWW: Adding Automation to the Cognitive Walkthrough for the Web

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ACWW Adding Automation to the Cognitive
Walkthrough for the Web

• by Richard Brown
• Thesis Director Dr. A. Sánchez
• University of North Florida
• CIS Department
• November, 2005

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What is CWW?

• CWW stands for Cognitive Walkthrough for the Web,
  an Usability Evaluation Method (UEM)
• CWW extends
• Comprehension-Based Linked model of Deliberate
  Search (CoLiDeS)
• A cognitive model that describes how users select
  objects on the screen.
• Uses Latent Semantic Analysis (LSA) to estimate
  similarity between goal statement and objects on
  the screen.
• Cognitive Walkthrough (CW)
• A UEM that predicts a how a user learns by
  exploration.

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Latent Semantic Analysis (LSA)

• Created to solve Platos Problem
• People have more knowledge than they have
  explicitly learned.
• LSA simulates knowledge gained by reading
• Uses semantic spaces from a corpus of various
  sources to represent the material read by a
  person at a given age-grade level.

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CWW Problems

• Current process is semi-manual, which makes it
  cumbersome due to the use of multiple tools
• Results of one tool must be transferred to the
  input of another by the analyst
• Analyst must format the final results
• Analyst must start from scratch when wanting to
  run the same data over multiple analysis options

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ACWW is an Automatic Alternative

• ACWW eliminates the preparation work (e.g. link
  elaboration, excel formatting)
• ACWW frees analysts from error-prone activities
  such as copy-and-paste.
• ACWW provides analysts with a richer set of
  results (e.g. Problem Identification, Analysis
  options, PMC)
• ACWW can perform multiple analyses on the same
  data, using different options

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ACWW Flow of Control
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Example of Semi-Manual Results
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Example of ACWW Results
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Results I
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Results II
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Results III
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Results IV
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ACWW Demo

• http//autocww.colorado.edu/brownr

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Conclusions and Recommendations

• ACWW is, on average, 5.6 times faster than the
  manual method.
• ACWW reduces human error by removing as many
  touch points as currently possible.
• User interface can be replaced with no changes to
  the DB or backend application.

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Acknowledgements

• Dr. Marylyn Blackmon, a Research Associate at the
  University of Colorado, who provided numerous
  hours to test and validate the system as well as
  provided guidance as ACWW was built.
• ACWW was mentioned in a paper by Dr. Blackmon at
  the Association for Computing Machinery
  Conference on Human Factors in Computing Systems
  (CHI) in April of 2005.
• ACWW is currently used by researchers and
  students at the Institute of Cognitive Science at
  the University of Colorado and by researchers at
  the National Institute of Advanced Industrial
  Science and Technology (AIST).

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Questions?