ou analyse project full details
Professor in Responsible Artificial Intelligence
Participant(s):Zdenek Zdrahal, Jakub Kuzilek, Martin Hlosta, Drahomira Herrmannova, Vaclav Bayer, Christothea Herodotou
Timeline:01 Aug 2013 - 31 Jul 2015
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.
17 Nov 2022
17 Sep 2021
28 Jul 2021
05 Jul 2021
29 Jun 2021
Bayer, V., Hlosta, M. and Fernandez, M. (2021) Learning Analytics and Fairness: Do Existing Algorithms Serve Everyone Equally?, AIED 2021; 22nd International Conference on Artificial Intelligence in Education, ONLINE from Utrecht
Hlosta, M., Herodotou, C., Fernandez, M. and Bayer, V. (2021) Impact of Predictive Learning Analytics on Course Awarding Gap of Disadvantaged students in STEM, Artificial Intelligence in Education, AIED 2021, Online / Utrecht, NL
Rets, I., Herodotou, C., Bayer, V., Hlosta, M. and Rienties, B. (2021) Exploring critical factors of the perceived usefulness of a learning analytics dashboard for distance university students, pp. (In Press)
Herodotou, C., Maguire, C., McDowell, N., Hlosta, M. and Boroowa, A. (2021) The engagement of university teachers with predictive learning analytics
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