Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.735966
Title: An analytics-based approach to the study of learning networks in digital education settings
Author: Joksimovic, Srecko
ISNI:       0000 0004 6500 8062
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2017
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Abstract:
Investigating howgroups communicate, build knowledge and expertise, reach consensus or collaboratively solve complex problems, became one of the main foci of contemporary research in learning and social sciences. Emerging models of communication and empowerment of networks as a form of social organization further reshaped practice and pedagogy of online education, bringing research on learning networks into the mainstream of educational and social science research. In such conditions, massive open online courses (MOOCs) emerged as one of the promising approaches to facilitating learning in networked settings and shifting education towards more open and lifelong learning. Nevertheless, this most recent educational turn highlights the importance of understanding social and technological (i.e., material) factors as mutually interdependent, challenging the existing forms of pedagogy and practice of assessment for learning in online environments. On the other hand, the main focus of the contemporary research on networked learning is primarily oriented towards retrospective analysis of learning networks and informing design of future tasks and recommendations for learning. Although providing invaluable insights for understanding learning in networked settings, the nature of commonly applied approaches does not necessarily allow for providing means for understanding learning as it unfolds. In that sense, learning analytics, as a multidisciplinary research field, presents a complementary research strand to the contemporary research on learning networks. Providing theory-driven and analytics-based methods that would allow for comprehensive assessment of complex learning skills, learning analytics positions itself either as the end point or a part of the pedagogy of learning in networked settings. The thesis contributes to the development of learning analytics-based research in studying learning networks that emerge fromthe context of learning with MOOCs. Being rooted in the well-established evidence-centered design assessment framework, the thesis develops a conceptual analytics-based model that provides means for understanding learning networks from both individual and network levels. The proposed model provides a theory-driven conceptualization of the main constructs, along with their mutual relationships, necessary for studying learning networks. Specifically, to provide comprehensive understanding of learning networks, it is necessary to account for structure of learner interactions, discourse generated in the learning process, and dynamics of structural and discourse properties. These three elements – structure, discourse, and dynamics – should be observed as mutually dependent, taking into account learners’ personal interests, motivation, behavior, and contextual factors that determine the environment in which a specific learning network develops. The thesis also offers an operationalization of the constructs identified in the model with the aim at providing learning analytics-methods for the implementation of assessment for learning. In so doing, I offered a redefinition of the existing educational framework that defines learner engagement in order to account for specific aspects of learning networks emerging from learning with MOOCs. Finally, throughout the empirical work presented in five peer-reviewed studies, the thesis provides an evaluation of the proposed model and introduces novel learning analytics methods that provide different perspectives for understanding learning networks. The empirical work also provides significant theoretical and methodological contributions for research and practice in the context of learning networks emerging from learning with MOOCs.
Supervisor: Gasevic, Dragan ; Bayne, Sian ; Hatala, Marek Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.735966  DOI: Not available
Keywords: learning analytics ; learning networks ; MOOCs ; social interactions ; discourse ; massive open online courses
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