The Future of Eye Tracking and User Experience

Schall, Andrew; Bergstrom, Jennifer Romano

doi:10.1016/b978-0-12-408138-3.00014-5

Cited by 20 publications

(14 citation statements)

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“…Using multiple metrics to analyze the eye-tracking data can improve the result [64]. Fixation count (location), fixation length, and saccades (movement) are the essential pieces of eye-tracking data that form important parameters [65]. When the eye is reasonably still, it is called a fixation.…”

Section: A Studymentioning

confidence: 99%

Gamification on OTT Platforms: A Behavioural Study for User Engagement

Suryavanshi¹,

Gahlot²,

Thapa³

et al. 2022

IJACSA

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This study examines the consumer's visual attention toward gamification options while watching the OTT (Over-the-top) online content. Also, the impact of gamification on user engagement (UE) on the OTT platform was studied using data collected by conducting an eye-tracking experiment and subsequently using a user engagement scale (UES). The study was carried out at the marketing and behavioural lab of a management institute in India using the OTT platform website and Tobii eye-tracker. Empirical data was collected from 52 respondents within the age group between 23 to 35 years. The relation between Attention to Gamification (AG), Reward Satisfaction (RS), and User Engagement (UE) were studied by running a mediating linear regression analysis. From the results, it was found that respondents were equally interested in watching the online content as well as ready to explore the gamification options. The research findings demonstrate that Reward Satisfaction (RS) acted as a mediating factor in the relation between Attention to Gamification (AG) and User Engagement (UE). This study adds to the literature on consumer engagement towards gamification on the OTT platform, where the literature is still limited. Future research could consider mobile apps as a platform to undertake the study. This study aimed to empirically test the effect of AG on UE with the involvement of RS as a mediator. The study is the first of its type to use eye-tracking data to understand the impact of gamification on the OTT platform.

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Section: A Studymentioning

confidence: 99%

Gamification on OTT Platforms: A Behavioural Study for User Engagement

Suryavanshi¹,

Gahlot²,

Thapa³

et al. 2022

IJACSA

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“…Eye tracking hardware measures the eye movements of a person when visually processing information [41]. Most commercial eye tracking hardware exploit the corneal reflection method to discover and trace the motion of the eye when it is shifting from one position to another [48].…”

Section: Introductionmentioning

confidence: 99%

“…In this method, a light source is used to illuminate the eye and detect the cornea using a high resolution camera [48]. Recently, machine learning has been used in the analysis of eye tracking data to successfully solve problems such as event detection [59] and automatic clustering of eye gaze data [37].…”

Section: Introductionmentioning

confidence: 99%

Classification of Eye Tracking Data in Visual Information Processing Tasks Using Convolutional Neural Networks and Feature Engineering

et al. 2021

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Eye tracking technology has been adopted in numerous studies in the field of human-computer interaction (HCI) to understand visual and display-based information processing as well as the underlying cognitive processes employed by users when navigating a computer interface. Analyzing eye tracking data can also help identify interaction patterns with regard to salient regions of an information display. Deep learning technology is increasingly being used in the analysis of eye tracking data by allowing for the classification of large amounts of eye tracking results. In this paper, eye tracking data and convolutional neural networks (CNNs) were used to perform a classification task to predict three types of information presentation methods. As a first step, a number of data preprocessing and feature engineering approaches were applied to eye tracking data collected through a controlled visual information processing experiment. The resulting data were used as input for the comparison of four CNN models with different architectures. In this experiment, two CNN models were effective in classifying the information presentations with overall accuracy greater than 80%.

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“…Sabendo que as emoções podem influenciar nas escolhas de informação das pessoas e sabendo que nem sempre tais emoções podem ser percebidas sem a utilização de aparatos tecnológicos, algumas tecnologias estão sendo utilizadas em testes com usuários com a finalidade de melhorar a qualidade dos resultados de testes em User Experience (UX), inclusive em estudos de interfaces de ambientes informacionais digitais (BERGSTROM; SCHALL, 2014).…”

Section: Introductionunclassified

Eye tracking em interface do google: a influência do elemento "Rich Snippet"

Rodas

Vidotti

2017

Inf.Inf.

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RESUMO:Introdução: Na era de intensa produção de informações na qual se vive, as interfaces humano-computador podem ter um impacto considerável sobre o sucesso de um produto. É por meio das interfaces que as pessoas acessam as informações e nesse contexto o usuário sempre deve ser considerado. Tendo em vista que o acesso às Tecnologias de Informação e Comunicação tem sido feito cada vez mais por pessoas menos especialistas, como crianças e idosos, as interfaces precisam se tornar gradativamente mais intuitivas e serem capazes de atrair a atenção de seus usuários. Nesse contexto, estudos em User Experience mostram que certas tecnologias, como o Eye Tracking, são capazes de capturar camadas extras de dados e assim permitir uma melhor compreensão do comportamento das pessoas ao acessarem informações digitais. Objetivo: Nos testes buscou-se identificar se os usuários poderiam ser influenciados pelas duas diferentes tarefas dadas, uma com e outra sem apelo emocional. Metodologia: Neste trabalho foram realizados testes com usuários sobre a página de resultados do mecanismo de busca do Google, utilizando a tecnologia de Eye Tracking, procurando estimular o apelo emocional em um dos dois grupos participantes. Resultados: Os resultados surpreenderam ao mostrar que os links contidos nas páginas de resultados do Google, que apresentavam Rich Snippets, foram capazes de influenciar o comportamento dos participantes, independente da tarefa. Conclusão: Esses resultados revelaram o impacto do elemento Rich Snippet na experiência do usuário ao buscar informações na interface do Google.

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The Future of Eye Tracking and User Experience

Cited by 20 publications

References 1 publication

Gamification on OTT Platforms: A Behavioural Study for User Engagement

Gamification on OTT Platforms: A Behavioural Study for User Engagement

Classification of Eye Tracking Data in Visual Information Processing Tasks Using Convolutional Neural Networks and Feature Engineering

Eye tracking em interface do google: a influência do elemento "Rich Snippet"

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