Martin Hlosta

Research Fellow

In KMi, I work as a Research Fellow, I am a project leader of OUAnalyse (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 for 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.

Keywords

OU Analyse Project, Machine Learning, CAT (Curriculum Analytics Tool)


Publications

Bonnin, Geoffray, Dessì, Danilo, Fenu, Gianni,Hlosta, Martin, Marras, Mirko and Sack, Harald ,(2022). Guest Editorial of the FGCS Special Issue on Advances in Intelligent Systems for Online Education. Future Generation Computer Systems, 127 pp. 331–333.

Herodotou, Christothea , Maguire, Claire , McDowell, Nicola D. , Hlosta, Martin and Boroowa, Avinash (2021). The engagement of university teachers with predictive learning analytics. Computers & Education, 173, article no. 104285.

Rets, Irina , Herodotou, Christothea , Bayer, Vaclav, Hlosta, Martin and Rienties, Bart (2021). Exploring critical factors of the perceived usefulness of a learning analytics dashboard for distance university students. International Journal of Educational Technology in Higher Education, 18(1), article no. 46.

Hlosta, Martin, Herodotou, Christothea , Fernandez, Miriam and Bayer, Vaclav(2021). Impact of Predictive Learning Analytics on Course Awarding Gap of Disadvantaged students in STEM. In: 22nd International Conference on Artificial Intelligence in Education, AIED 2021, Lecture Notes in Artificial Intelligence, Springer.

Bayer, Vaclav, Hlosta, Martin and Fernandez, Miriam(2021). Learning Analytics and Fairness: Do Existing Algorithms Serve Everyone Equally? In: Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science, vol 12749 (Roll, I.; McNamara, D.; Sosnovsky, S.; Luckin, R. and Dimitrova, V. eds.), Springer.

Hlosta, Martin, Papathoma, Tina and Herodotou, Christothea (2020). Explaining Errors in Predictions of At-Risk Students in Distance Learning Education. In: Artificial Intelligence in Education, Lecture Notes in Computer Science (LNCS), Springer, pp. 119–123.

Herodotou, Christothea , Boroowa, Avinash , Hlosta, Martin and Rienties, Bart (2020). What do distance learning students seek from student analytics? In: International Conference on Learning Sciences, 19-23 Jun 2020, Nashville, TN, USA.

Hlosta, Martin, Bayer, Vaclav and Zdrahal, Zdenek(2020). Mini Survival Kit: Prediction based recommender to help students escape their critical situation in online courses. In: Proceedings of the 10th International Conference on Learning Analytics and Knowledge (LAK20), 23-27 Mar 2020, Frankfurt am Main, Germany.

Hlosta, Martin, Zdrahal, Zdenek, Bayer, Vaclav and Herodotou, Christothea (2020). Why Predictions of At-Risk Students Are Not 100% Accurate? Showing Patterns in False Positive and False Negative Predictions. In: Proceedings of the 10th International Conference on Learning Analytics and Knowledge (LAK20), 23-27 Mar 2020, Frankfurt am Main, Germany.

Herodotou, Christothea , Rienties, Bart , Hlosta, Martin, Boroowa, Avinash , Mangafa, Chrysoula and Zdrahal, Zdenek(2020). The scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study. Internet and Higher Education, 45, article no. 100725.