Showing all 7 Publications linked to the ou analyse project
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:...
Kuzilek, J., Hlosta, M. and Zdrahal, Z. (2017) Open University Learning Analytics dataset, Scientific Data, 4, Nature Publishing Group
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
Herrmannova, D., Hlosta, M., Kuzilek, J. and Zdrahal, Z. (2015) Evaluating Weekly Predictions of At-Risk Students at The Open University: Results and Issues, EDEN 2015, Barcelona, Spain
Kuzilek, J., Hlosta, M., Herrmannova, D., Vaclavek, J., Zdrahal, Z. and Wolff, A. (2015) OU Analyse: Analysing At-Risk Students at The Open University, Learning Analytics and Knowledge (LAK15), Learning Analytics Review, LAK15-1, LACE project
Wolff, A., Zdrahal, Z., Herrmannova, D., Kuzilek, J. and Hlosta, M. (2014) Developing predictive models for early detection of at-risk students on distance learning modules, Workshop: Machine Learning and Learning Analytics at Learning Analytics and Knowledge (LAK), Indianapolis
Hlosta, M., Herrmannova, D., Vachova, L., Kuzilek, J., Zdrahal, Z. and Wolff, A. (2014) Modelling student online behaviour in a virtual learning environment, Workshop: Machine Learning and Learning Analytics at Learning Analytics and Knowledge (LAK), Indianapolis
Bart, R., , C., Coughlan, D., Cross, T., Edwards, S., Gaved, C., Herodotou, M., Hlosta, C., Jones, M., Rogaten, J., Ullmann, J. and , T.Scholarly insight Autumn 2017:a Data wrangler perspective, Scholarly insight Autumn 2017, IET, The Open University