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dc.contributor.advisorGasevic, Dragan
dc.contributor.advisorBayne, Sian
dc.contributor.advisorHatala, Marek
dc.contributor.authorJoksimovic, Srecko
dc.date.accessioned2017-12-21T14:44:55Z
dc.date.available2017-12-21T14:44:55Z
dc.date.issued2017-11-29
dc.identifier.urihttp://hdl.handle.net/1842/25819
dc.description.abstractInvestigating 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.en
dc.language.isoenen
dc.publisherThe University of Edinburghen
dc.relation.hasversionJoksimovic, S., Gaševic, D., Bayne, S., Hatala, M., and Dawson, S. (2017). Studying Learning in Nonformal Digital Educational Settings. In Measurement in Digital Environments White Paper Series, 118, SRI International, Menlo Park, CA. Retrieved from http: //a4li.sri.com/archive/papers/Joksimovic_2017_Nonformal_Learning.pdf.en
dc.relation.hasversionJoksimovic, S., Poquet, O., Kovanovic, V., Dowell, N., Caitlin, M., Gaševic, D., Dawson, S., Brooks, C., Graesser, A. C. 2017. How do we Model Learning at Scale? A Systematic Review of the Literature. Review of Educational Research.en
dc.relation.hasversionSkrypnyk, O., Joksimovic, S., Kovanovic, V., Gaševic, D., and Dawson, S. (2015). Roles of course facilitators, learners, and technology in the flow of information of a CMOOC. International Review of Research in Open and Distance Learning, 16(3) pp.188–217.en
dc.relation.hasversionJoksimovic, S., Dowell, N., Skrypnyk, O., Kovanovic, V., Gaševic, D., Dawson, S., and Graesser, A. C. 2016. Exploring Development of Social Capital in a cMOOC Through Language and Discourse. The Internet and Higher Education.en
dc.relation.hasversionJoksimovic, S., Manataki, A., Gaševic, D., Dawson, S., Kovanovic, V., and de Kereki, I. F. (2016). Translating Network Position into Performance: Importance of Centrality in Different Network Configurations. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (LAK’16), pp.314323.en
dc.relation.hasversionJoksimovic, S., Kovanovic, V., Jovanovic, J., Zouaq, A., Gaševic, D., Hatala, M. (2016). What Do cMOOC Participants Talk About in Social Media? A Topic Analysis of Discourse in a cMOOC. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (LAK’16), pp.156165.en
dc.relation.hasversionJoksimovic, S., Jovanovic, J., Kovanovic, V., Gaševic, D., Milikic, N., Zouaq, A., and van Saalduinen, J.-P. (2017).Comprehensive analysis of discussion forum participation: from speech acts to discussion dynamics and course outcomes. Computers in Human Behavior.en
dc.relation.hasversionDawson, S. , Gaševic, D. , Siemens, G. and Joksimovic, S. (2014). Current state and future trends: A citation network analysis of the learning analytics field. In Proceedings of the Fourth International Learning Analytics and Knowledge Conference, LAK ’14, 231–240, ACM, New York, NY, USA.en
dc.relation.hasversionDowell, N. , Skrypnyk, O. , Joksimovic, S. , Graesser, A.C. , Dawson, S. , Gaševic, D. , de Vries, P. , Hennis, T. and Kovanovic, V. (2015). Modeling Learners’ Social Centrality and Performance through Language and Discourse. 250–257, Madrid, Spain.en
dc.relation.hasversionGaševic, D. , Kovanovic, V. and Joksimovic, S. (2017). Piecing the learning analytics puzzle: a consolidated model of a field of research and practice. Learning: Research and Practice, 3, 63–78.en
dc.relation.hasversionJoksimovic, S. , Gaševic, D. , Kovanovic, V. , Adesope, O. and Hatala, M. (2014). Psychological characteristics in cognitive presence of communities of inquiry: A linguistic analysis of online discussions. The Internet and Higher Education, 22, 1 – 10.en
dc.relation.hasversionKovanovic, V. , Joksimovic, S. , Gaševic, D. , Siemens, G. and Hatala, M. (2015). What public media reveals about moocs: A systematic analysis of news reports. British Journal of Educational Technology, 46, 510–527.en
dc.subjectlearning analyticsen
dc.subjectlearning networksen
dc.subjectMOOCsen
dc.subjectsocial interactionsen
dc.subjectdiscourseen
dc.subjectmassive open online coursesen
dc.titleAnalytics-based approach to the study of learning networks in digital education settingsen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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