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Collaborative Heuristic Evaluation: improving the effectiveness of heuristic evaluation Helen Petrie, Lucy Buykx Human Computer Interaction Research Group Department of Computer Science University of York, UK
Standard heuristic evaluation (SHE) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Collaborative Heuristic Evaluation (CHE) ,[object Object],[object Object],[object Object],[object Object],[object Object]
CHE Problem rating sheet 1 2 3 4 5  
Experiment method ,[object Object],[object Object],[object Object],[object Object],[object Object]
Experiment design Group 1 Group 2 CHE National Rail Visit Britain British Towns Easy Jet SHE Easy Jet British Towns Visit Britain National Rail
Analysis of data ,[object Object],[object Object],[object Object],[object Object],[object Object]
Results ,[object Object],[object Object],[object Object],[object Object],[object Object]
Inter-evaluator agreement
National Rail website SHE CHE
Any-two agreement: measure of evaluator effect SHE CHE Agree it’s a problem 8.9% 91.6% Agree severity rating 36.0% n avg  = 19 37.5% n avg  = 38
Conclusion ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object]
Matching process ,[object Object],[object Object],[object Object],[object Object]

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Collaborative Heuristic Evaluation, UPA2010

Notas do Editor

  1. SHE: E1 – 19 problems unique to that evaluator; 2 agreed with E5, 1 agreed with both E2 and E5 …and so on … none where all 5 agree CHE: 59 all five agree, 5 E6, E7, E10 not E9 or E8; 1 E6, E8 only
  2. Hertzum and Jacobsen (2001) argue that this any-two agreement is the best measure of evaluator effect
  3. 25 mins strict matching : same design element, same problem, same level abstraction Often needed the video to place the element - remember i wrote words agreed by eval so without video would have been really difficult