OU Analyse

The OU Analyse project is piloting new machine learning based methods for early identification of students who are at risk of failing.

A list of such students is communicated weekly to the module and Student Support teams to help them consider appropriate support. The overall objective is to significantly improve the retention of OU students. This is 'research-led' as the project builds on previous experience from the Jisc funded Retain in 2010/2011 and the joint OU-Microsoft Research Cambridge project in 2012/2013.

The work is innovative in that it is applying machine learning techniques to two types of data: student...

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)

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

Bart, R., , C., Coughlan, D., Cross, T., Edwards, S., Gaved, C., Herodotou, M., Hlosta, C., Jones, M., Rogaten, J., Ullmann, J. and , T. (2017) Scholarly insight Autumn 2017:a Data wrangler perspective, Scholarly insight Autumn 2017, IET, The Open University

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

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