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dc.contributor.authorCobos, Ruth
dc.contributor.authorWilde, Adriana
dc.contributor.authorZaluska, Ed
dc.identifier.citationCobos , R , Wilde , A & Zaluska , E 2017 , ' Comparing attrition prediction in FutureLearn and edX MOOCs ' , Paper presented at FutureLearn Workshop in Learning Analytics and Knowledge 2017 (LAK17) , Vancouver , Canada , 13/03/17 - 17/03/17 .en
dc.identifier.otherPURE: 251541290
dc.identifier.otherPURE UUID: 63a78dc4-580c-4ef9-b6eb-749b5458a807
dc.identifier.otherRIS: urn:20A512BA867602577C97DFAA1E0D76EE
dc.descriptionThis work has been funded by the Web Science Institute Pump-priming 2015/16 Project “The MOOC Observatory Dashboard: Management, analysis and visualisation of MOOC data ”. Additionally, Ruth Cobos’ contribution has been partially funded by the Madrid Regional Government with grant No. S2013/ICE - 2715, the Spanish Ministry of Economy and Competitiveness project "Flexor" (TIN2014- 52129- R).en
dc.description.abstractThere are a number of similarities and differences between FutureLearn MOOCs and those offered by other platforms, such as edX. In this research we compare the results of applying machine learning algorithms to predict course attrition for two case studies using datasets from a selected FutureLearn MOOC and an edX MOOC of comparable structure and themes. For each we have computed a number of attributes in a pre-processing stage from the raw data available in each course. Following this, we applied several machine learning algorithms on the pre-processed data to predict attrition levels for each course. The analysis suggests that the attribute selection varies in each scenario, which also impacts on the behaviour of the predicting algorithms.
dc.rightsCopyright 2017 the Authors.en
dc.subjectPredictive modelen
dc.subjectLearning analyticsen
dc.subjectAttribute selectionen
dc.subjectLB Theory and practice of educationen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.titleComparing attrition prediction in FutureLearn and edX MOOCsen
dc.typeConference paperen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.description.statusPeer revieweden

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