Integrating Eye Tracking, Feature Use, and Emotional Valence: A Multimodal Approach to Evaluating Search Interfaces

Interactive Information Retrieval (IIR) interfaces are typically evaluated using questionnaires that gather post-task subjective measures such as ease of use, usefulness, satisfaction, and user engagement, along with in-task objective measures derived from log analysis.However, a comprehensive evaluation requires a deeper understanding of user behaviour beyond such traditional measures.Integrating eye tracking data with logged feature use and emotional valence provides a multimodal approach to evaluating a search interface at the feature level.To validate this approach, we examined three search interfaces in a controlled laboratory study focused on exploratory search within the context of digital humanities archives.A key benefit of this multimodal approach is that it allows us to evaluate both traditional interaction with the search interface (looking at a feature, using it, and experiencing an emotional response) as well as passive interaction with the search interface (looking at a feature, choosing not to use it but possibly getting information from it, and experiencing an emotional response).Using this approach, we were able to identify specific features of the interfaces that generated positive and negative emotional valence responses when used, as well as features that generated such emotional valence responses when viewed but not used.Such feature-level assessments would be difficult to capture using other means, providing insight into the nature of the searchers' experiences using the search interfaces.

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Publication Details

Published
2025-03-24
DOI
https://doi.org/10.1145/3698204.3716444
Citations
1
Primary Topic
Advanced Text Analysis Techniques
Type
article
Field-Weighted Citation Impact
2.48

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article

Integrating Eye Tracking, Feature Use, and Emotional Valence: A Multimodal Approach to Evaluating Search Interfaces

David T. Gleeson, Orland Hoeber, Morgan Harvey, Abbas Pirmoradi Bezanjani et al.
1 citations
Advanced Text Analysis Techniques
2.48
article

Integrating Eye Tracking, Feature Use, and Emotional Valence: A Multimodal Approach to Evaluating Search Interfaces

David T. Gleeson, Orland Hoeber, Morgan Harvey, Abbas Pirmoradi Bezanjani, Milad Momeni
article en
1 citations

Abstract

Interactive Information Retrieval (IIR) interfaces are typically evaluated using questionnaires that gather post-task subjective measures such as ease of use, usefulness, satisfaction, and user engagement, along with in-task objective measures derived from log analysis.However, a comprehensive evaluation requires a deeper understanding of user behaviour beyond such traditional measures.Integrating eye tracking data with logged feature use and emotional valence provides a multimodal approach to evaluating a search interface at the feature level.To validate this approach, we examined three search interfaces in a controlled laboratory study focused on exploratory search within the context of digital humanities archives.A key benefit of this multimodal approach is that it allows us to evaluate both traditional interaction with the search interface (looking at a feature, using it, and experiencing an emotional response) as well as passive interaction with the search interface (looking at a feature, choosing not to use it but possibly getting information from it, and experiencing an emotional response).Using this approach, we were able to identify specific features of the interfaces that generated positive and negative emotional valence responses when used, as well as features that generated such emotional valence responses when viewed but not used.Such feature-level assessments would be difficult to capture using other means, providing insight into the nature of the searchers' experiences using the search interfaces.

University of Regina (CA), Northumbria University (GB), University of Sheffield (GB)
University of Regina, Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 11%
Advanced Text Analysis Techniques
2.48
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