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User engagement has become a much-cited construct in human-computer interaction (HCI) design and evaluation research and practice. Constructed as a positive and desirable outcome of users' interactions, more frequent and longer interactions are considered evidence of engagement. Disengagement, when discussed, is considered a best avoided outcome of...
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... the game when they are Theorycrafting; the diversity of players' online and offline experiences contribute to their engagement with WOW. Focusing on disengagement as a stopping behaviour in this context misses critical elements of players' engagement. Instead, we might envision parallel engagement cycles taking place that feed into each other. In Fig. 2, engagement with a primary task (e.g., playing WoW, from the above example) is depicted in a linear way to represent ongoing (but not continuous) use. As users play over time, they engage with other online and offline resources (depicted as X, Y and Z) and complete related tasks that influence their engagement with the primary task ...
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... summary, there is a natural ebb and flow of digitally-mediated activities that is not always captured by looking at sustained use of a technology punctuated by stopping (Fig. 1). User experience with any task may be influenced by parallel engagement with various (non-) digital tools (Fig. 2) or may consist of smaller micro-engagements that are significant when pieced together over time (Fig. 3). These alternative conceptualizations are more complex than Fig. 1, and reinforce that disengagement is not "simply the absence of engagement behaviours" (Chipchase et al., 2017, p. ...
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... an activity or inactivity (Fig. 1). 2) In section 4, we modeled disengagement as part of the natural course of interacting with technology as users perform tasks and work toward their goals. Disengagement is part of a cyclical process that may involve taking a break from one technology to engage with another or other task-relevant activities (Fig. 2), or micro-interactions representing shifts in engagement over time (Fig. 3). These depictions show disengagement as moving the user forward and we can imagine the cycles being thick or thin, linear or branching, and rich with decision points. 3) Finally, through unpacking the fallacies (section 3.2), we have shown that disengagement is ...
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... cyclical view of engagement depicted earlier in Fig. 3 shows disengagement as part of the natural course of engagement, and of parallel engagements with different resources (Fig. 2), supporting the view that engagement need not be continuous (Section 3.2.3). This enables us to see ebbs in engagement as equally valid to overall experience as engagement and re-engagement, and to view what users do outside of a particular application as relevant to their long-term ...
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... to measurement is that we are looking for a single, simple measure to capture a complex construct, and this is impossible; more interactivity does not necessarily mean high agentic engagement (Section 3.2.2). If we expand our view of engagement as cyclical (Fig. 3) and operating in parallel with different sources of information and interaction (Fig. 2), then we must explore ways to incorporate measures of pause and reflection into our evaluation ...
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... positive, more interactive, continuous, or beneficial to end users. Second, we built upon the Process Model of User Engagement (O'Brien & Toms, 2008) to contest the simplified view that disengagement is the end of engagement (Fig. 1). Specifically, we explored parallel engagements of online and offline activities related to a core task (Fig. 2), and cyclical, micro-engagements unfolding over time (Fig. 3). Finally, we proposed a definition of disengagement as pausing or ceasing the use of a technology in the context of end-user goals and agency in order to evaluate its desirability. In framing both engagement and disengagement as something that can add to or diminish the user ...
Citations
... • Perceived value of patient-generated health data [32,33] for patients, which is defined as functional (to support health outcomes), emotional (understand and regulate emotions), social (share experience and support with peers), transactional (enrich consultations with health professionals), efficiency (eliminate unnecessary appointments), and self-determination value (empower patients). • User engagement, which is defined as the process of how people start and continue to use technology for a certain purpose [34], which can be cyclical and include phases of disengagement and re-engagement [35]. Engagement is different from adherence, in that it is more dynamic and shaped by subjective factors such as a person's goal, challenge, and experience with technology [36]. ...
... In examining the barriers to engagement, we found that participants took breaks from using MyFootCare when they experienced health disruptions, e.g., when they received care in hospitals. As predicted by technology engagement frameworks [34,35], we found that participants re-engaged when they were back at home. ...
People with diabetes-related foot ulcers (DFUs) need to perform self-care consistently over many months to promote healing and to mitigate risks of hospitalisation and amputation. However, during that time, improvement in their DFU can be hard to detect. Hence, there is a need for an accessible method to self-monitor DFUs at home. We developed a new mobile phone app, “MyFootCare”, to self-monitor DFU healing progression from photos of the foot. The aim of this study is to evaluate the engagement and perceived value of MyFootCare for people with a plantar DFU over 3 months’ duration. Data are collected through app log data and semi-structured interviews (weeks 0, 3, and 12) and analysed through descriptive statistics and thematic analysis. Ten out of 12 participants perceive MyFootCare as valuable to monitor progress and to reflect on events that affected self-care, and seven participants see it as potentially valuable to enhance consultations. Three app engagement patterns emerge: continuous, temporary, and failed engagement. These patterns highlight enablers for self-monitoring (such as having MyFootCare installed on the participant’s phone) and barriers (such as usability issues and lack of healing progress). We conclude that while many people with DFUs perceive app-based self-monitoring as valuable, actual engagement can be achieved for some but not for all people because of various facilitators and barriers. Further research should target improving usability, accuracy and sharing with healthcare professionals and test clinical outcomes when using the app.
... La participación del usuario se ha convertido en una construcción muy citada en la investigación y la práctica del diseño y la evaluación de la interacción humano-computadora (HCI). Construido como un resultado positivo y deseable de las interacciones de los usuarios, las interacciones más frecuentes y largas se consideran evidencia de compromiso (O'Brien et al., 2022). ...
Technological advances and their impact on the education sector have led us to hear a variety of terms that a few years ago seemed distant. Thus, we are currently talking about cognitive society, virtual reality, augmented reality, immersive reality, i-learning (immersive learning), gamification, tutored metaverses, among others. These terms are part of our day to day and whether we like it or not, they are giving way to new possibilities to access the content that is published on the Internet and that is applicable to the educational field. Therefore, the present work aims to analyze the human-computer interaction and the considerations that must be taken when designing interfaces for the metaverse applied to education, for which a bibliographic review of the publications made in the various databases is carried outon the subject to identify pending issues to address and progress in the metaverse. As a result, the impact and level of acceptance that the metaverse has in various areas and its application in the educational field are shown, because these new scenarios provide us with a range of training tools to promote more active learning in the student.
... Human-computer interaction is defined as a way a human interacts with a computer and is a crucial part of designing the GUI of applications. For human-computer interaction research, proper user engagement is a desirable effect and O'Brien et al. [15] suggest focusing on disengagement as a necessary human-computer interaction design. There are many challenges in designing graphical user interfaces due to the lack of availability of guidance and targeted experience [16] . ...
Facility management and maintenance of the Thermal-Energy-Storage AirConditioning (TES-AC) system is a tedious task at a large scale mainly due to the charging load that can increase energy consumption if needed to be charged at peak hours. Besides, maintenance of TES-AC at a large scale gets complex as it contains many sensor data. By utilizing deep learning techniques on the sensor data, charging load prediction can be made possible, so facility managers can prepare in advance. However, a deep learning-based application will be unusable if it is not deployed in a user-friendly manner where facility managers can benefit from this application. Hence, this research focuses on gathering design guidelines for a deep learning-based application and further validates the design considerations with a developed application for efficient human-computer interaction through qualitative analysis. The approach taken to gather design guidelines demonstrated a positive correlation between expert-suggested features and the user-friendly aspect of the application as 67.08% of participants found the features suggested by experts to be most satisfactory. Furthermore, it evaluates user satisfaction with the advanced developed application for TES-AC according to the gathered design guidelines.
... However, how intervention work and the impact of intervention mechanisms also depend on end-user engagement. The concept "end-user engagement" refers to the extent to which users make cognitive, emotional, and temporal investments to interact with mHealth content [21]. ...
... O'Brien and colleagues state that user engagement is a matter of quality rather than quantity with respect to cognitive, emotional, and behavioral investments regarding intervention interaction [21]. The typologies presented in this study can be seen as representations of the four types of engagement proposed by O'Brien. ...
... Furthermore, the Vague typology shares characteristics with the negative engagement and low agency quadrant. That is, low activity due to, e.g., lacking motivation, but nevertheless completing tasks, which can result in passive learning [21]. ...
The effectiveness of mHealth interventions rely on whether the content successfully activate mechanisms necessary for behavior change. These mechanisms may be affected by end-users’ experience of the intervention content. The aim of this study was to explore how the content of a novel mHealth intervention (LIFE4YOUth) was understood, interpreted, and applied by high school students, and the consequences of engaging with the content. Qualitative content analysis was used inductively and deductively to analyze interview data (n = 16) based on think-aloud techniques with Swedish high school students aged 16–19 years. Theoretical constructs from social cognitive theory framed the deductive analysis. The analysis resulted in four categories which describe central activities of intervention engagement among end-users: defining, considering, centralizing, and personalizing. End-users engaged in these activities to different degrees as illustrated by four typologies: Literal, Vague, Rigid, and Creative engagement. Most informants knew about the risks and benefits of health behaviors, but engagement with intervention content generally increased informants’ awareness. In conclusion, this study provides in-depth knowledge on the cognitive process when engaging with mHealth content and suggests that deliberate and flexible engagement most likely deepens end-users’ understanding of why and how health behavior change can be managed.
... Although educational research emphasizes the importance of engagement in the learning process and has demonstrated a robust link between engagement and achievement, more research is needed to make this complex construct more visible and measurable (D'Mello, Dieterle, & Duckworth, 2017;Ladd & Dinella, 2009). O'Brien, Roll, Kampen, and Davoudi (2021) suggest that to piece together the bigger picture of students' engagement, more holistic measures are needed to account for engagement as an ongoing cycle of engagement, disengagement, and re-engagement. Indeed, recent advances in the development of new data-capturing devices propose a multimodal approach of psycho-physiological signals and self-report data streams as a means of providing a person-oriented and continuous perspective on engagement. ...
This study uses a multimodal data analysis approach to provide a more continuous and objective insight into how students' engagement unfolds and impacts learning achievements. In this study, 61 nursing students' learning processes with a virtual reality (VR)-based simulation were captured by psycho-physiological data streams of facial expression, eye-tracking, and electrodermal activity (EDA) sensors, as well as by subjective self-reports. Students’ learning achievements were evaluated by a pre- and post-test content knowledge test. Overall, while both facial expression and self-report modalities revealed that students experienced significantly higher levels of positive than negative emotions, only the facial expression data channel was able to detect fluctuations in engagement during the different learning session phases. Findings point towards the VR procedural learning phase as a reengaging learning activity, which induces more facial expressions of joy and triggers a higher mental effort as measured by eye tracking and EDA metrics. Most importantly, a regression analysis demonstrated that the combination of modalities explained 51% of post-test knowledge achievements. Specifically, higher levels of prior knowledge and self-reported enthusiasm, and lower levels of angry facial expressions, blink rate, and devotion of visual fixations to irrelevant information, were associated with higher achievements. This study demonstrates that the methodology of using multimodal data channels encompassing different types of objective and subjective measures, can provide insights into a more holistic understanding of engagement in learning and learning achievements.
Virtual reality, as an excellent supportive instructional technology, has gained increasing attention from educators and professionals, where desktop‐based virtual reality (DVR) is broadly adopted due to its affordability and accessibility. However, when evaluating students' learning experiences such as flow experiences in DVR environments, most studies adopt a single construct (the total score of flow experience) rather than multiple constructs (enjoyment, engagement, concentration, presence and time distortion). This study implemented desktop‐based virtual reality for a STEM bridge designing program with a total of 254 undergraduates to investigate the relationship between self‐regulation skills, five dimensions of flow experience, learning satisfaction and continuous intention when engaging in a DVR learning environment. The results revealed that self‐regulated learning exerted a dominant impact on students' learning attitudes in DVR learning, in which students' flow experience had a significant mediating effect. Notably, although DVR exhibited poor time distortion, higher satisfaction and continuous intention were still predicted by the mentality of flow experience (ie, enjoyment, engagement, concentration and presence). The findings of this study contribute to the consideration of learning experiences and attitudes, which has insights for the future design of desktop‐based virtual reality environments and related instructional activities. Practitioner notesWhat is already known about this topicStudents are different in self‐regulation skills, which influences their satisfaction and continuous intention in learning.Students' self‐regulation skills are one of the important variables in predicting their flow experience.A high level of flow experience contributes to a coherent and efficient learning experience within desktop‐based virtual reality (DVR) environments.What this paper addsStudents' self‐regulation skills positively predicted their flow experience and satisfaction in DVR environments.The components of flow experience (enjoyment, concentration and presence) partially mediated the relationship between self‐regulation skills and satisfaction.Students' self‐regulation skills indirectly affect continuous intention by the enjoyment and engagement of flow experiences.Implications for practice and/or policyWhen delivering DVR‐based learning activities educators should be supportive of students with low levels of self‐regulation skills.Emphasis on promoting flow experiences such as enjoyment, engagement, concentration and presence in designing a DVR‐based classroom could enhance student satisfaction and continuous intention.Embedding scaffolding or feedback in DVR settings would support self‐regulated learning and subsequently improve student satisfaction and persistence through enhanced flow experience. What is already known about this topicStudents are different in self‐regulation skills, which influences their satisfaction and continuous intention in learning.Students' self‐regulation skills are one of the important variables in predicting their flow experience.A high level of flow experience contributes to a coherent and efficient learning experience within desktop‐based virtual reality (DVR) environments. What this paper addsStudents' self‐regulation skills positively predicted their flow experience and satisfaction in DVR environments.The components of flow experience (enjoyment, concentration and presence) partially mediated the relationship between self‐regulation skills and satisfaction.Students' self‐regulation skills indirectly affect continuous intention by the enjoyment and engagement of flow experiences. Implications for practice and/or policyWhen delivering DVR‐based learning activities educators should be supportive of students with low levels of self‐regulation skills.Emphasis on promoting flow experiences such as enjoyment, engagement, concentration and presence in designing a DVR‐based classroom could enhance student satisfaction and continuous intention.Embedding scaffolding or feedback in DVR settings would support self‐regulated learning and subsequently improve student satisfaction and persistence through enhanced flow experience.
