A comprehensive and systematic model of user evaluation of web search engines: II. an evaluation by undergraduates

  • Authors:

  • Louise T. Su

  • Affiliations:

  • University of Pittsburgh, Pittsburgh, PA 15260/ 593 Wenhwa Road, Rende, Shiang, Tainan, Taiwan 717, ROC

  • Venue:
  • Journal of the American Society for Information Science and Technology
  • Year:
  • 2003

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Abstract

This paper presents an application of the model described in Part I to the evaluation of Web search engines by undergraduates. The study observed how 36 undergraduate used four major search engines to find information for their own individual problems and how they evaluated these engines based on actual interaction with the search engines. User evaluation was based on 16 performance measures representing five evaluation criteria: relevance, efficiency, utility, user satisfaction, and connectivity. Non-performance (user-related) measures were also applied. Each participant searched his/ her own topic on all four engines and provided satisfaction ratings for system features and interaction and reasons for satisfaction. Each also made relevance judgements of retrieved items in relation to his/her own information need and participated in post-search interviews to provide reactions to the search results and overall performance. The study found significant differences in precision PR1, relative recall, user satisfaction with output display, time saving, value of search results, and overall performance among the four engines and also significant engine by discipline interactions on all these measures. In addition, the study found significant differences in user satisfaction with response time among four engines, and significant engine by discipline interaction in user satisfaction with search interface. None of the four search engines dominated in every aspect of the multidimensional evaluation. Content analysis of verbal data identified a number of user criteria and users evaluative comments based on these criteria. Results from both quantitative analysis and content analysis provide insight for system design and development, and useful feedback on strengths and weaknesses of search engines for system improvement.