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.

Kiran Parmar

Martin Hlosta

John Domingue

Jane Whild

Miriam Fernandez

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

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

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

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

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