Member
Martin Hlosta 
Research Fellow
+44 (0)1908 332292
In KMi, I work as a Research Fellow acting as a technical lead and a data scientist of OU Analyse Project (https://analyse.kmi.open.ac.uk). The project is focused on improving student retention at Open University using machine learning techniques. I am exploring how to best use OUAnalyse to teachers (Associate Lecturers) in order to better use data and analytics for improving student outcomes. Moreover, I am responsible fore design and the development of the Curriculum Analytics Tool (CAT) - the tool has been deployed in 2017 to help in the curriculum review. It is expected to facilitate data-informed decisions on the curriculum and finance planning in the future.
Keys: OU Analyse Project, Machine Learning, CAT (Curriculum Analytics Tool)
Team: Vaclav Bayer, Miriam Fernandez, Jakub Kocvara, Zdenek Zdrahal
News
14 Nov 2019
11 Sep 2019
04 Jul 2018
12 Mar 2018
29 Jun 2017
Publications
Celik, D., Mikroyannidis, A., Hlosta, M., Third, A. and Domingue, J. (2019) ADA: A System for Automating the Learning Data Analytics Processing Life Cycle, EC-TEL 2019 14th European Conference on Technology Enhanced Learning, Delft, Netherlands
Hlosta, M., Kocvara, J., Beran, D. and Zdrahal, Z. (2019) Visualisation of key splitting milestones to support interventions, VISLA 2019: Workshop on Visual Approaches to Learning Analytics, Tempe, AZ, USA
Herodotou, C., Rienties, B., Boroowa, A., Zdrahal, Z. and Hlosta, M. (2019) A large-scale implementation of Predictive Learning Analytics in Higher Education: the teachers' role and perspective A large-scale implementation of Predictive Learning Analytics in Higher Education: the teachers' role and perspective
Herodotou, C., Hlosta, M., Boroowa, A., Rienties, B., Zdrahal, Z. and Mangafa, C. (2019) Empowering online teachers through predictive learning analytics Empowering online teachers through predictive learning analytics, pp. (Early Access)
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
