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ou analyse project full details

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Champion: Zdenek Zdrahal
Professor of Knowledge Engineering Email Icon Website Icon RDF Icon

Participant(s):Jakub Kuzilek, Martin Hlosta, Drahomira Herrmannova

Similar Projects:RETAIN

Timeline:01 Aug 2013 - 31 Jul 2015

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OU Analyse

The OU Analyse project is piloting new machine learning based methods for early identification of students who are at risk of failing.

A list of such students is communicated weekly to the module and Student Support teams to help them consider appropriate support. The overall objective is to significantly improve the retention of OU students. This is ‘research-led’ as the project builds on previous experience from the Jisc funded Retain in 2010/2011 and the joint OU-Microsoft Research Cambridge project in 2012/2013.

The work is innovative in that it is applying machine learning techniques to two types of data: student demographic data and dynamic data represented by their VLE activities. Records of previous presentations are used to build and validate predictive models, which are then applied to the data of the presentation currently running.

News

04 Jul 2018

Rachel Coignac-Smith


12 Mar 2018

Rachel Coignac-Smith


27 Mar 2017

Rachel Coignac-Smith


05 Nov 2016

John Domingue


29 Sep 2016

Rachel Coignac-Smith


View all 18 Articles

Publications

Publications | Visit External Site for Details Publications | doi 

Hlosta, M. and Zendulka, Z. (2018) Are we meeting a deadline? classification goal achievement in time in the presence of imbalanced data, Knowledge Based Systems, Elsevier

Publications | Visit External Site for Details Publications | doi 

Huptych, M., Hlosta, M., Zdrahal, Z. and Kocvara, J. (2018) Investigating Influence of Demographic Factors on Study Recommenders, Poster at Artificial Intelligence in Education, Springer, Cham

Publications | Visit External Site for Details Publications | Visit External Site for Details Publications | doi

Kuzilek, J., Hlosta, M. and Zdrahal, Z. (2017) Open University Learning Analytics dataset, Scientific Data, 4, Nature Publishing Group

Publications | Visit External Site for Details  

Bart, R., , C., Coughlan, D., Cross, T., Edwards, S., Gaved, C., Herodotou, M., Hlosta, C., Jones, M., Rogaten, J., Ullmann, J. and , T. (2017) Scholarly insight Autumn 2017:a Data wrangler perspective, Scholarly insight Autumn 2017, IET, The Open University

Publications | Visit External Site for Details Publications | Visit External Site for Details Publications | doi

Hlosta, M., Zdrahal, Z. and Zendulka, J. (2017) Ouroboros: Early identification of at-risk students without models based on legacy data, Learning Analytics & Knowledge (LAK 17), ACM

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Knowledge Media Institute
The Open University
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