Cargando…
How Quickly Can We Predict Users’ Ratings on Aesthetic Evaluations of Websites? Employing Machine Learning on Eye-Tracking Data
This study examines how quickly we can predict users’ ratings on visual aesthetics in terms of simplicity, diversity, colorfulness, craftsmanship. To predict users’ ratings, first we capture gaze behavior while looking at high, neutral, and low visually appealing websites, followed by a survey regar...
Autores principales: | Pappas, Ilias O., Sharma, Kshitij, Mikalef, Patrick, Giannakos, Michail N. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7134250/ http://dx.doi.org/10.1007/978-3-030-45002-1_37 |
Ejemplares similares
-
Systematic Literature Review of E-Learning Capabilities to Enhance Organizational Learning
por: Giannakos, Michail N., et al.
Publicado: (2021) -
Experimental investigation on the effects of website aesthetics on user performance in different virtual tasks
por: Thielsch, Meinald T., et al.
Publicado: (2019) -
Utilizing Interactive Surfaces to Enhance Learning, Collaboration and Engagement: Insights from Learners’ Gaze and Speech
por: Sharma, Kshitij, et al.
Publicado: (2020) -
Keep Calm and Do Not Carry-Forward: Toward Sensor-Data Driven AI Agent to Enhance Human Learning
por: Sharma, Kshitij, et al.
Publicado: (2022) -
Evaluation of plastic surgery resident aesthetic clinic websites
por: Sayegh, Farah, et al.
Publicado: (2020)