Citizen Science: The Ring to Rule Them All? Ronell van der Merwe and Judy van Biljon University of South Africa, Pretoria, South Africa vdmerwer@unisa.ac.za vbiljja@unisa.ac.za Abstract: There are many uncertainties about the future of e-Learning, but one thing is certain: e-Learning will be more data-driven in the future. The automation of data capturing, analysis and presentation, together with economic constraints that require evidence-based proof of impact, compels this data focus. On the other hand, the importance of community involvement in learning analytics and educational data mining is an accepted fact. Citizen science, at the nexus of community engagement, and data science can bridge the divide between data-driven and community-driven approaches to policy and content development. The rationale for this paper is the investigation of citizen science as an approach to collecting data for learning analytics in the field of e-Learning. Capturing data for policy and content development for learning analytics through citizen science projects is novel in the e-Learning field. Like any other new area, citizen science needs to be mapped in terms of the existing parent fields of data science and education so that differences and potential overlaps can be made explicit. This is important when considering conceptual or functional definitions, research tools and methodologies. A preliminary review of the literature has not provided any conceptual positioning of citizen science in relation to the research topics of learning analytics, data science, big data and visualisation in the e-Learning environment. The intent of this paper is firstly to present an overview of citizen science and the related research topics in the academic and practitioner literature based on a systematic literature review. Secondly, we propose a model that represents the relationship between citizen science and other salient concepts and shows how citizen science projects can be positioned in the e-Learning environment. Finally, we suggest research opportunities involving citizen science projects in the field of e- Learning. Keywords: E-learning, learning analytics, data science, citizen science 1. Introduction The data revolution is under way and it is reshaping knowledge production through new ways of data capturing, analysis and reporting in all spheres of life (Kitchin, 2014). Data science and learning analytics provide the educator with methods to obtain evidence-based information towards improving the experience of the learner in higher education (Baker et al., 2006; Clarke and Nelson, 2013; Scheffel et al., 2014; Prinsloo et al., 2015; Willis, Slade and Prinsloo, 2016; De Freitas and Bernard, 2017), both in an out of the classroom. A later and dynamically growing approach to capturing data is the citizen science approach. However, big data and, more specifically, citizen science as platforms both to capture data and to report data for learning analytics are not widely used or reported in literature (Chen et al., 2016; Chaurasia and Frieda Rosin, 2017). Given the various, sometimes contradicting definitions of the terms related to the data revolution, we present our interpretation of the terms data science, learning analytics and citizen science, and some contextualisation in the field of higher education, as follows: Data science has been defined as the study of the generalisable extraction of knowledge from data (Dhar, 2013). Modern society produces vast amounts of data, but most data is unstructured and requires processes and methods to extract useful information and present it in an understandable and useful format (Provost and Fawcett, 2013). Citizen science can be described as the engagement of ordinary citizens in gathering large quantities of data through various projects over an extended period of time that have a direct impact on either the society or the environment (Ali et al., 2013; Allan and Redden, 2017). Traditionally, citizen science, is used in the fields of natural sciences and environmental sciences to gather data across a wide spectrum of research fields. Often, projects engaging citizens in gathering data, aim at obtaining vast quantities of data across a spectrum of participants that is unattainable by the individual researchers. In recent years this field has grown, and professional citizen science societies have been established throughout the world. Within e-Learning, learning analytics can be described as the measurement, collection, analysis and reporting of electronic data gathered in a virtual learning environment about learners in their contexts, with the purpose of understanding and optimising the learning environment in which it occurs (Agudo-Peregrina et al., 2014; Baer and Norris, 2016; Muslim et al., 2016). Earlier definitions of learning analytics focused on the use of data in predicting and advising future learning (Hiller, Kyrillidou and Self, 2008; Palmer, Holt and Bray, 2008; Zhang et al., 2016). For the purpose of this paper, we use the definitions of analytics and learning analytics proposed by Barneveld, 471