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Wells, M.; Wollenschlaeger, Alex; Lefevre, D.; Magoulas, George D.; Poulovassilis, Alexandra (2016)
Publisher: Association for Computing Machinery
Languages: English
Types: Part of book or chapter of book
Subjects: csis

Classified by OpenAIRE into

ACM Ref: ComputingMilieux_COMPUTERSANDEDUCATION
We analyse engagement and performance data arising from participants’ interactions with an in-house LMS at Imperial College London while a cohort of students follow two courses on a new online postgraduate degree in Management. We identify and investigate two main questions relating to the relationships between engagement and performance, drawing recommendations for improved guidelines to inform the design of such courses.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] J. Arbaugh. System, scholar or students? Which most in uences online MBA course e ectiveness? Journal of Computer Assisted Learning, 30(4):349{362, 2014.
    • [2] C. Co rin, L. Corrin, P. de Barba, and G. Kennedy. Visualizing patterns of student engagement and performance in moocs. In Proc. LAK'14, pages 83{92, 2014.
    • [3] A. L. Dyckho , V. Lukarov, A. Muslim, M. A. Chatti, and U. Schroeder. Supporting action research with learning analytics. In Proc. LAK 2013, pages 220{229.
    • [4] R. Ferguson and D. Clow. Examining engagement: analysing learner subpopulations in massive open online courses (MOOCs). In Proc. LAK 2015, pages 51{58.
    • [5] G. Siemens. Learning analytics: envisioning a research discipline and a domain of practice. In Proc. LAK 2012, pages 4{8.
    • [6] M. Wells, D. Lefevre, and F. Begklis. Innovation via a Thin LMS: A middleware alternative to the traditional learning management system. In Proc. 30th Ascilite Conference. Macquarie University, Australia, 2013.
    • [7] A. Wol , Z. Zdrahal, A. Nikolov, and M. Pantucek. Improving retention: predicting at-risk students by analysing clicking behaviour in a virtual learning environment. In Proc. LAK 2013, pages 145{149.
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