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, Stephane Carpenter, Miriam Fernandez, Zdenek Zdrahal
News
30 Sep 2020
01 May 2020
30 Apr 2020
04 Feb 2020
14 Nov 2019
Publications
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
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
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
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
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)
