I am a Visiting Research Fellow. Currenlty at Swiss Distance University of Applied Sciences

(Fernfachhochschule Schweiz – FFHS) - The Institute for

Research in Open, Distance and eLearning (IFeL) I spent 7 years in KMi, most recently as 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...

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

Bayer, V., Mulholland, P., Hlosta, M., Farrell, T., Herodotou, C. and Fernandez, M. (2024) Co‐creating an equality diversity and inclusion learning analytics dashboard for addressing awarding gaps in higher education, British Journal of Educational Technology, pp. (Early access)

Albuquerque, J., Rienties, B., Holmes, W. and Hlosta, M. (2024) From Hype to Evidence: Exploring Large Language Models for Inter-Group Bias Classification in Higher Education, Interactive Learning Environments, pp. (In Press)

Bonnin, G., Bayer, V., Fernandez, M., Herodotou, C., Hlosta, M. and Mulholland, P. (2023) CERSEI: Cognitive Effort Based Recommender System for Enhancing Inclusiveness, 18th European Conference on Technology Enhanced Learning, EC-TEL 2023, Aveiro, Portugal

Herodotou, C., Maguire, C., Hlosta, M. and Mulholland, P. (2023) Predictive Learning Analytics and University Teachers: Usage and perceptions three years post implementation, 13th International Learning Analytics and Knowledge Conference (LAK 2023), Arlington, Texas

Bonnin, G., Dessì, D., Fenu, G., Hlosta, M., Marras, M. and Sack, H. (2022) Guest Editorial of the FGCS Special Issue on Advances in Intelligent Systems for Online Education, 127, pp. 331-333

Hlosta, M., Herodotou, C., Papathoma, T., Gillespie, A. and Bergamin, P. (2022) Predictive learning analytics in online education: A deeper understanding through explaining algorithmic errors, Computers and Education: Artificial Intelligence, 3

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

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)

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

Hlosta, M., Zdrahal, Z. and Zendulka, J. (2018) Are we meeting a deadline? classification goal achievement in time in the presence of imbalanced data, Knowledge-Based Systems, 160, pp. 278-295, Elsevier

Huptych, M., Hlosta, M., Zdrahal, Z. and Kocvara, J. (2018) Investigating Influence of Demographic Factors on Study Recommenders, Poster at Artificial Intelligence in Education, Springer, Cham

Kuzilek, J., Hlosta, M. and Zdrahal, Z. (2017) Open University Learning Analytics dataset, Scientific Data, 4, Nature Publishing Group

Herodotou, C., Gilmour, A., Boroowa, A., Rientes, B., Zdrahal, Z. and Hlosta, M. (2017) Predictive modelling for addressing students' attrition in Higher Education: The case of OU Analyse, CALRG Annual Conference 2017, The Open University

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

Herodotou, C., Rienties, B., Boroowa, A., Zdrahal, Z., Hlosta, M. and Naydenova, G. (2017) Implementing predictive learning analytics on a large scale: the teacher's perspective, Learning Analytics & Knowledge Conference (LAK'17), ACM

Huptych, M., Bohuslavek, M., Hlosta, M. and Zdrahal, Z. (2017) Measures for recommendations based on past students' activity, Learning Analytics & Knowledge (LAK 17), ACM

Kuzilek, J., Hlosta, M. and Zdrahal, Z. (2016) Open University Learning Analytics Dataset, Workshop: Data Literacy for Learning Analytics at Learning Analytics and Knowledge (LAK 16)

Zdrahal, Z., Hlosta, M. and Kuzilek, J. (2016) Analysing performance of first year engineering students, Workshop: Data Literacy for Learning Analytics at Learning Analytics and Knowledge (LAK 16)

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