Website: UX Feedback
Deadline for full submissions: 31 March 2020
This special issue aims to explore the opportunities and challenges of combining implicit and explicit feedback to understand and design user experience (UX) in Human-Computer Interaction (HCI).
Measuring UX is important to understand how successful applications and systems are in reaching their goals. In general, there are two main approaches to measure UX: 1) explicit feedback (i.e., using data measured through surveys, interviews and focus groups) and 2) implicit feedback (i.e., using data describing users' observable interaction behavior measured through, for example, telemetry). Measuring explicit feedback is costlier, requires user input, and thus relies on smaller scale studies. However, it allows to gain deeper information and understanding about the relationship between user characteristics, their needs and preferences, their behavior and their experience. Although, implicit feedback can be collected automatically, it allows for limited understanding of the relationship between user behavior, user traits and user experience.
Implicit and explicit feedback can be combined to effectively measure and understand UX factors; implicit feedback can facilitate the breadth (by quantitatively indicating how designs influence UX) while explicit feedback can facilitate the depth (by providing insight how user behavior, user characteristics and user experience are related). The combination of these two approaches result in an understanding with a high level of detail with the cost efficiency of quantitative research.
Specific areas in which the combination of implicit and explicit feedback is valuable is in personalized and adaptive systems: systems that adapt itself based on users' interaction behavior to match their preferences or needs. A prominent direction using this approach is the field of recommender systems in which historical behavioral data (implicit feedback) is used to alter the order of items in a catalog (from highest predicted relevance to lowest predicted relevance), with the goal of helping users to find relevant items more easily or making them consume more items. In this case, implicit feedback (behavior) is used to make inferences about concepts that normally can only be measured through explicit feedback (preferences).
We encourage authors to submit original research articles, case studies, reviews, theoretical and critical perspectives, and viewpoint articles within the domain of HCI on topics including but not limited to:
For more information about the Special Issue, please see the website.
For information on manuscript preparation and related matters, please see the instructions for authors.
Although the deadline for submission of manuscripts to the Special Issue is 31 March 2020, papers will be reviewed and published as they are received. The entire set of invited papers and any others in this domain will appear at the link indicated.
Deadline for full submissions: 31 March 2020
Measuring UX is important to understand how successful applications and systems are in reaching their goals. In general, there are two main approaches to measure UX: 1) explicit feedback (i.e., using data measured through surveys, interviews and focus groups) and 2) implicit feedback (i.e., using data describing users' observable interaction behavior measured through, for example, telemetry). Measuring explicit feedback is costlier, requires user input, and thus relies on smaller scale studies. However, it allows to gain deeper information and understanding about the relationship between user characteristics, their needs and preferences, their behavior and their experience. Although, implicit feedback can be collected automatically, it allows for limited understanding of the relationship between user behavior, user traits and user experience.
Implicit and explicit feedback can be combined to effectively measure and understand UX factors; implicit feedback can facilitate the breadth (by quantitatively indicating how designs influence UX) while explicit feedback can facilitate the depth (by providing insight how user behavior, user characteristics and user experience are related). The combination of these two approaches result in an understanding with a high level of detail with the cost efficiency of quantitative research.
Specific areas in which the combination of implicit and explicit feedback is valuable is in personalized and adaptive systems: systems that adapt itself based on users' interaction behavior to match their preferences or needs. A prominent direction using this approach is the field of recommender systems in which historical behavioral data (implicit feedback) is used to alter the order of items in a catalog (from highest predicted relevance to lowest predicted relevance), with the goal of helping users to find relevant items more easily or making them consume more items. In this case, implicit feedback (behavior) is used to make inferences about concepts that normally can only be measured through explicit feedback (preferences).
We encourage authors to submit original research articles, case studies, reviews, theoretical and critical perspectives, and viewpoint articles within the domain of HCI on topics including but not limited to:
- Deriving metrics for measuring UX from qualitative research
- The interplay between user characteristics/user behavior and UX
- Combining explicit and implicit feedback for UX Research
- Empirical studies incorporating UX factors, user behavior and/or user characteristics (e.g., A/B testing)
- Explicit and implicit feedback in personalized/adaptive systems
- Implicit feedback for UX design (e.g., data-driven design)
- Explicit feedback for UX design (e.g., theory-driven design)
For more information about the Special Issue, please see the website.
For information on manuscript preparation and related matters, please see the instructions for authors.
Although the deadline for submission of manuscripts to the Special Issue is 31 March 2020, papers will be reviewed and published as they are received. The entire set of invited papers and any others in this domain will appear at the link indicated.
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