Human computation is a computing approach that draws upon human cognitive abilities to solve computational tasks for which there are so far no satisfactory fully automated solutions even when using the most advanced computing technologies available. Human computation for citizen science projects consists in designing systems that allow large crowds of volunteers to contribute to scientific research by executing human computation tasks. Examples of successful projects are Galaxy Zoo and FoldIt. A key feature of this kind of project is its capacity to engage volunteers. An important requirement for the proposal and evaluation of new engagement strategies is having a clear understanding of the typical engagement of the volunteers; however, even though several projects of this kind have already been completed, little is known about this issue. In this paper, we investigate the engagement pattern of the volunteers in their interactions in human computation for citizen science projects, how they differ among themselves in terms of engagement, and how those volunteer engagement features should be taken into account for establishing the engagement encouragement strategies that should be brought into play in a given project. To this end, we define four quantitative engagement metrics to measure different aspects of volunteer engagement, and use data mining algorithms to identify the different volunteer profiles in terms of the engagement metrics. Our study is based on data collected from two projects: Galaxy Zoo and The Milky Way Project. The results show that the volunteers in such projects can be grouped into five distinct engagement profiles that we label as follows: hardworking, spasmodic, persistent, lasting, and moderate. The analysis of these profiles provides a deeper understanding of the nature of volunteers' engagement in human computation for citizen science projects.
The type of engagement is defined by the kind of personal resources and skills that humans invest in performing an activity. Examples of types of engagement are social engagement @cite_23 and cognitive engagement @cite_25 . Social engagement refers to actions that require humans to interact with others. It is widely studied in areas such as online social networks and communities @cite_52 @cite_42 . Cognitive engagement refers to actions that require mainly human cognitive effort. It has been widely addressed in educational psychology and work engagement @cite_8 @cite_6 .
@cite_8 @cite_42 @cite_52 @cite_6 @cite_23 @cite_25
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The purpose of this article is to critically deconstruct the term engagement as it applies to peoples' experiences with technology. Through an extensive, critical multidisciplinary literature review and exploratory study of users of Web searching, online shopping, Webcasting, and gaming applications, we conceptually and operationally defined engagement. Building on past research, we conducted semistructured interviews with the users of four applications to explore their perception of being engaged with the technology. Results indicate that engagement is a process comprised of four distinct stages: point of engagement, period of sustained engagement, disengagement, and reengagement. Furthermore, the process is characterized by attributes of engagement that pertain to the user, the system, and user-system interaction. We also found evidence of the factors that contribute to nonengagement. Emerging from this research is a definition of engagement—a term not defined consistently in past work—as a quality of user experience characterized by attributes of challenge, positive affect, endurability, aesthetic and sensory appeal, attention, feedback, variety-novelty, interactivity, and perceived user control. This exploratory work provides the foundation for future work to test the conceptual model in various application areas, and to develop methods to measure engaging user experiences. © 2008 Wiley Periodicals, Inc. Purpose - This paper aims to provide an overview of the recently introduced concept of work engagement. Design methodology approach - Qualitative and quantitative studies on work engagement are reviewed to uncover the manifestation of engagement, and reveal its antecedents and consequences. Findings - Work engagement can be defined as a state including vigor, dedication, and absorption. Job and personal resources are the main predictors of engagement; these resources gain their salience in the context of high job demands. Engaged workers are more creative, more productive, and more willing to go the extra mile. Originality value - The findings of previous studies are integrated in an overall model that can be used to develop work engagement and advance career development in today's workplace. Citizen participation in online communities of scientific investigations has recently become more popular. Enhancing the engagement of citizens within these communities is a focus of attention for researchers and practitioners who want to amplify the impact on learning, science and society. This study investigates the relationship between engagement factors and behaviour patterns in an online community that requires high levels of citizen participation. While other studies explore engagement in communities where citizens contribute data, the current research investigates a community to support citizens in facilitating their own scientific investigations. Data were collected from log files and questionnaires, and multiple measures of engagement were examined: engagement metrics, roles, motivation, attitude, satisfaction and belonging to the community. The results allowed comparison of the engagement levels among different types of citizen participation communities and categorised members in engagement profiles, according to their behaviour patterns. Findings indicate a need for differing design approaches based on the type of citizen participation community and individual engagement profiles. Human computation is a computing approach that draws upon human cognitive abilities to solve computational tasks for which there are so far no satisfactory fully automated solutions even when using the most advanced computing technologies available. Human computation for citizen science projects consists in designing systems that allow large crowds of volunteers to contribute to scientific research by executing human computation tasks. Examples of successful projects are Galaxy Zoo and FoldIt. A key feature of this kind of project is its capacity to engage volunteers. An important requirement for the proposal and evaluation of new engagement strategies is having a clear understanding of the typical engagement of the volunteers; however, even though several projects of this kind have already been completed, little is known about this issue. In this paper, we investigate the engagement pattern of the volunteers in their interactions in human computation for citizen science projects, how they differ among themselves in terms of engagement, and how those volunteer engagement features should be taken into account for establishing the engagement encouragement strategies that should be brought into play in a given project. To this end, we define four quantitative engagement metrics to measure different aspects of volunteer engagement, and use data mining algorithms to identify the different volunteer profiles in terms of the engagement metrics. Our study is based on data collected from two projects: Galaxy Zoo and The Milky Way Project. The results show that the volunteers in such projects can be grouped into five distinct engagement profiles that we label as follows: hardworking, spasmodic, persistent, lasting, and moderate. The analysis of these profiles provides a deeper understanding of the nature of volunteers' engagement in human computation for citizen science projects.