Martin Hlosta
Martin Hlosta
Research Fellow, Knowledge Media Institute, The Open University
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OU Analyse: analysing at-risk students at The Open University
J Kuzilek, M Hlosta, D Herrmannova, Z Zdrahal, A Wolff
Learning Analytics Review, 1-16, 2015
Open University Learning Analytics Dataset
J Kuzilek, M Hlosta, Z Zdrahal
Scientific data, 2017
Developing predictive models for early detection of at-risk students on distance learning modules
A Wolff, Z Zdrahal, D Herrmannova, J Kuzilek, M Hlosta
Ouroboros: early identification of at-risk students without models based on legacy data
M Hlosta, Z Zdrahal, J Zendulka
Proceedings of the seventh international learning analytics & knowledge …, 2017
VGEN: Fast Vertical Mining of Sequential Generator Patterns
P Fournier-Viger, A Gomariz, M Šebek, M Hlosta
Data Warehousing and Knowledge Discovery: 16th International Conference …, 2014
Implementing predictive learning analytics on a large scale: the teacher's perspective
C Herodotou, B Rienties, A Boroowa, Z Zdrahal, M Hlosta, G Naydenova
Proceedings of the seventh international learning analytics & knowledge …, 2017
A large-scale implementation of predictive learning analytics in higher education: the teachers’ role and perspective
C Herodotou, B Rienties, A Boroowa, Z Zdrahal, M Hlosta
Educational Technology Research and Development 67 (5), 1273-1306, 2019
Modelling student online behaviour in a virtual learning environment
M Hlosta, D Herrmannova, L Vachova, J Kuzilek, Z Zdrahal, A Wolff
arXiv preprint arXiv:1811.06369, 2018
Empowering online teachers through predictive learning analytics
C Herodotou, M Hlosta, A Boroowa, B Rienties, Z Zdrahal, C Mangafa
British Journal of Educational Technology 50 (6), 3064-3079, 2019
Data literacy for learning analytics
A Wolff, J Moore, Z Zdrahal, M Hlosta, J Kuzilek
Proceedings of the Sixth International Conference on Learning Analytics …, 2016
The scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study
C Herodotou, B Rienties, M Hlosta, A Boroowa, C Mangafa, Z Zdrahal
The Internet and Higher Education 45, 100725, 2020
Constrained classification of large imbalanced data by logistic regression and genetic algorithm
M Hlosta, R Stríz, J Kupcík, J Zendulka, T Hruska
International Journal of Machine Learning and Computing 3 (2), 214, 2013
Scholarly insight Autumn 2017: a Data wrangler perspective
B Rienties, D Clow, T Coughlan, S Cross, C Edwards, M Gaved, ...
Open University UK, 2017
Evaluating weekly predictions of at-risk students at the open university: results and issues
D Herrmannova, M Hlosta, J Kuzilek, Z Zdrahal
Are we meeting a deadline? classification goal achievement in time in the presence of imbalanced data
M Hlosta, Z Zdrahal, J Zendulka
Knowledge-Based Systems 160, 278-295, 2018
Measures for recommendations based on past students' activity
M Huptych, M Bohuslavek, M Hlosta, Z Zdrahal
Proceedings of the Seventh International Learning Analytics & Knowledge …, 2017
MLSP: mining hierarchically-closed multi-level sequential patterns
M Šebek, M Hlosta, J Zendulka, T Hruška
International Conference on Advanced Data Mining and Applications, 157-168, 2013
Multi-level sequence mining based on gsp
M Šebek, M Hlosta, J Kupčík, J Zendulka, T Hruška
Acta Electrotechnica et Informatica (2), 31-38, 2012
Analysing performace of first year engineering students
Z Zdrahal, M Keynes, UK CIIRC, CZ Prague, M Hlosta, J Kuzilek
Why Predictions of At-Risk Students Are Not 100% Accurate? Showing Patterns in False Positive and False Negative Predictions
M Hlosta, Z Zdrahal, V Bayer, C Herodotou
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