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ou analyse project full details

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Champion: Zdenek Zdrahal
Emeritus Professor 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

30 Sep 2020

Jane Whild


01 May 2020

Kiran Parmar


30 Apr 2020

Martin Hlosta


04 Feb 2020

John Domingue


14 Nov 2019

Jane Whild


View all 24 Articles

Publications

Publications | Visit External Site for Details  

Hlosta, M., Papathoma, T. and Herodotou, C. (2020) Explaining Errors in Predictions of At-Risk Students in Distance Learning Education, International Conference on Artificial Intelligence in Education, online

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

Herodotou, C., Boroowa, A., Hlosta, M. and Rienties, B. (2020) What do distance learning students seek from student analytics?, International Conference on Learning Sciences, Nashville, TN, USA

Publications | Visit External Site for Details  

Hlosta, M., Bayer, V. and Zdrahal, Z. (2020) Mini Survival Kit: Prediction based recommender to help students escape their critical situation in online courses, Proceedings of the 10th International Conference on Learning Analytics and Knowledge (LAK20), Frankfurt am Main, Germany

Publications | Visit External Site for Details  

Hlosta, M., Zdrahal, Z., Bayer, V. and Herodotou, C. (2020) Why Predictions of At-Risk Students Are Not 100% Accurate? Showing Patterns in False Positive and False Negative Predictions, Proceedings of the 10th International Conference on Learning Analytics and Knowledge (LAK20), Frankfurt am Main, Germany

Publications | Visit External Site for Details  

Herodotou, C., Rienties, B., Hlosta, M., Boroowa, A., Mangafa, C. and Zdrahal, Z. (2020) The scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study The scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study, pp. (In Press)

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