A practical approach to measuring user engagement with the refined user engagement scale (UES) and new UES short form

O'Brien, Heather L.; Cairns, Paul; Hall, Mark

doi:10.1016/j.ijhcs.2018.01.004

Cited by 566 publications

(423 citation statements)

References 23 publications

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“…The UES (O'Brien et al, 2018) is a 31-item experiential questionnaire that evaluates engagement in several settings, such as education, consumer applications, haptic technologies, and video games. The 31 items of the UES are organized around six dimen sions: focused attention, perceived usability, aesthetic appeal, endurability, novelty, and felt environment.…”

Section: Feedback / Reciprocity / Interactivitymentioning

confidence: 99%

ENGAGE-DEM: A Model of Engagement of People With Dementia

Perugia

Boladeras

Catala-Mallofre

et al. 2022

IEEE Trans. Affective Comput.

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ADVERTIMENT La consulta d'aquesta tesi queda condicionada a l'acceptació de les següents condicions d'ús: La difusió d'aquesta tesi per mitjà del r e p o s i t o r i i n s t i t u c i o n a l UPCommons (http://upcommons.upc.edu/tesis) i el repositori cooperatiu TDX ( h t t p : / / w w w . t d x . c a t / ) ha estat autoritzada pels titulars dels drets de propietat intel·lectual únicament per a usos privats emmarcats en activitats d'investigació i docència. No s'autoritza la seva reproducció amb finalitats de lucre ni la seva difusió i posada a disposició des d'un lloc aliè al servei UPCommons o TDX. No s'autoritza la presentació del seu contingut en una finestra o marc aliè a UPCommons (framing). Aquesta reserva de drets afecta tant al resum de presentació de la tesi com als seus continguts. En la utilització o cita de parts de la tesi és obligat indicar el nom de la persona autora.ADVERTENCIA La consulta de esta tesis queda condicionada a la aceptación de las siguientes condiciones de uso: La difusión de esta tesis por medio del repositorio institucional UPCommons (http://upcommons.upc.edu/tesis) y el repositorio cooperativo TDR (http://www.tdx.cat/?locale-attribute=es) ha sido autorizada por los titulares de los derechos de propiedad intelectual únicamente para usos privados enmarcados en actividades de investigación y docencia. No se autoriza su reproducción con finalidades de lucro ni su difusión y puesta a disposición desde un sitio ajeno al servicio UPCommons No se autoriza la presentación de su contenido en una ventana o marco ajeno a UPCommons (framing). Esta reserva de derechos afecta tanto al resumen de presentación de la tesis como a sus contenidos. En la utilización o cita de partes de la tesis es obligado indicar el nombre de la persona autora.WARNING On having consulted this thesis you're accepting the following use conditions: Spreading this thesis by the i n s t i t u t i o n a l r e p o s i t o r y UPCommons

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Section: Feedback / Reciprocity / Interactivitymentioning

confidence: 99%

ENGAGE-DEM: A Model of Engagement of People With Dementia

Perugia

Boladeras

Catala-Mallofre

et al. 2022

IEEE Trans. Affective Comput.

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“…The interaction was assessed through questions relating to the different dimensions of information behavior. Specifically (a) Motivation to consume the content: Participants were asked whether they would like to read the title (b) Evaluation of the content: Participants were asked to rate the title for interest, value, readability and other factors adopted from the User Engagement Scale (UES) (O'Brien, Cairns, & Hall, 2018) (c) knowledge retention: Participants answered post-task questions to test whether they recall the information.…”

Section: Methodsmentioning

confidence: 99%

Sticky words: Evaluation and optimization of information interactions based on linguistic analysis

Dvir

2018

Proc. Assoc. Info. Sci. Tech.

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This paper describes a novel approach to systematically improve interactions with digital content based solely on its wording. Following an interdisciplinary literature review, we recognized three key attributes of words that drive successful interactions: (1) Novelty (2) Familiarity (3) Sentimentality. Based on these attributes, we developed a model to systematically improve a given content using word frequency, sentiment analysis and semantic substitution, and by employing computational linguistics and natural language processing (NLP) tech‐niques. We conducted a pilot study (n=416) in which the model was used to formalize evaluation and optimization of academic titles. A between‐group design was used to compare responses to the original (control) and modified (treatment) titles. Results show that the modified titles had significantly higher scores for selection, user engage‐ment and memorability. Our findings suggest that users' successful interactions with digital content is fostered by, and perhaps dependent upon, the wording being used. They also provide empirical support that engaging content can be systematically evaluated and produced. Implications and future research directions are discussed.

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“…The User Engagement Scale (UES) [35,36] is a questionnaire developed to measure the quality of the user experience. The original UES consists of 31 items, but, in this work, a reduced version of 29 closed-ended questions on a scale of 1 to 7 is used, structured as summarized in Table 1.…”

Section: User Engagement Scale (Ues)mentioning

confidence: 99%

Questionnaires or Inner Feelings: Who Measures the Engagement Better?

et al. 2020

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This work proposes an innovative method for evaluating users’ engagement, combining the User Engagement Scale (UES) questionnaire and a facial expression recognition (FER) system, active research topics of increasing interest in the human–computer interaction domain (HCI). The subject of the study is a 3D simulator that reproduces a virtual FabLab in which users can approach and learn 3D modeling software and 3D printing. During the interaction with the virtual environment, a structured-light camera acquires the face of the participant in real-time, to catch its spontaneous reactions and compare them with the answers to the UES closed-ended questions. FER methods allow overcoming some intrinsic limits in the adoption of questioning methods, such as the non-sincerity of the interviewees and the lack of correspondence with facial expressions and body language. A convolutional neural network (CNN) has been trained on the Bosphorus database (DB) to perform expression recognition and the classification of the video frames in three classes of engagement (deactivation, average activation, and activation) according to the model of emotion developed by Russell. The results show that the two methodologies can be integrated to evaluate user engagement, to combine weighted answers and spontaneous reactions and to increase knowledge for the design of the new product or service.

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A practical approach to measuring user engagement with the refined user engagement scale (UES) and new UES short form

Cited by 566 publications

References 23 publications

ENGAGE-DEM: A Model of Engagement of People With Dementia

ENGAGE-DEM: A Model of Engagement of People With Dementia

Sticky words: Evaluation and optimization of information interactions based on linguistic analysis

Questionnaires or Inner Feelings: Who Measures the Engagement Better?

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