OU Analyse is a project piloting machine-learning based methods for early identification of students at risk of failing. All students with their risk of failure are available weekly to the course tutors and the Student Support Teams to consider appropriate support. The overall objective is to significantly improve the retention of OU students.

Analysis process

Demographic and VLE data

Machine learning techniques are applied to two types of data: student demographic data and dynamic data represented by their VLE activities. Records of previous presentations are used to build and validate predictive models, which are then applied to the data of the running presentation. VLE data is collected daily at a very fine grain level, representing individual actions and activity types according to the module study plan.

Predictive models

OU Analyse uses the module fingerprint, demographic data of current students and their VLE activities to build a number of predictive models that take into account different properties of input data. Their conclusions are combined to classify students and predict who is at risk. The list of students is supplemented with justifications to explain the prediction.

Delivering the output

In Spring 2014 the project was piloted and evaluated on two introductory university modules with about 1500 and 3000 students, respectively. Until Summer 2016, forty module presentations have been supported. These predictions are available in the web dashboard application.

Beyond predictions

In addition to the identification of at risk students the dashboard featured the prototype of a personalised Activity Recommender to advise students how to improve their performance in the course.

OU Analyse Dashboard


Professor of Knowledge Engineering
PhD Research Student
Research Assistant
Research Associate
Research Assistant

Alumni team members

  • Petr Knoth
  • Jakub Kuzilek
  • Anna Kuzilkova
  • Andriy Nikolov
  • Michal Pantucek
  • Jonas Vaclavek
  • Lucie Vachova
  • Annika Wolff
  • Michal Bohuslavek



New Kuzilek, J. et al. Open University Learning Analytics dataset Sci. Data 4:170171 doi: 10.1038/sdata.2017.171 (2017).
Rienties B., Clow D., Coughlan T., Cross S., Edwards C., Gaved M., Herodotou C., Hlosta M., Jones J., Rogaten J., Ullmann T. Scholarly insight Autumn 2017: a Data wrangler perspective The Open University
Herodotou C., Gilmour A., Boroowa A., Rienties B., Zdrahal Z., Hlosta M. Predictive modelling for addressing students’ attrition in Higher Education: The case of OU Analyse CALRG Annual Conference 2017
Herodotou C., Rienties B., Boroowa A., Zdrahal Z., Hlosta M., Naydenova G. Implementing predictive learning analytics on a large scale: the teacher's perspective LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge Conference
Huptych M., Bohuslavek M., Hlosta M., Zdrahal Z., Measures for recommendations based on past students' activity LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge Conference
Hlosta M., Zdrahal Z., Zendulka J. Ouroboros: early identification of at-risk students without models based on legacy data LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge Conference


Rienties B., Cross S., Zdrahal, Z. Implementing a Learning Analytics Intervention and Evaluation Framework: what works? Big Data and Learning Analytics in Higher Education: Current Theory and Practice
Kuzilek, J., Hlosta, M., Zdrahal, Z. Open University Learning Analytics Dataset. Data Literacy For Learning Analytics Workshop at Learning Analytics and Knowledge (LAK16).


Kuzilek, J., Hlosta, M., Herrmannova, D., Zdrahal, Z. and Wolff, A. OU Analyse: Analysing At-Risk Students at The Open University. Learning Analytics Review, no. LAK15-1, March 2015, ISSN: 2057-7494.
Herrmannova, D., Hlosta, M., Kuzilek, J., Zdrahal, Z. Evaluating weekly predictions of at-risk students at the Open University: results and issues. EDEN 2015 , Barcelona, Spain.


Wolff, A., Zdrahal, Z., Herrmannova, D., Kuzilek, J. and Hlosta, M. Developing predictive models for early detection of at-risk students on distance learning modules , Workshop: Machine Learning and Learning Analytics at LAK, Indianapolis
Hlosta, M., Herrmannova, D., Vachova, L., Kuzilek, J., Zdrahal, Z. and Wolff, A. Modelling student online behaviour in a virtual learning environment, Workshop: Machine Learning and Learning Analytics at LAK, Indianapolis


Wolff, A., Zdrahal, Z., Nikolov, A. and Pantucek, M., Improving retention: predicting at-risk students by analysing clicking behaviour in a virtual learning environment, Learning Analytics and Knowledge
Wolff, A., Zdrahal, Z., Herrmannova, D. and Knoth, P., Predicting Student Performance from Combined Data Sources, in eds. Alejandro Peña-Ayala, Educational Data Mining: Applications and Trends, 524, Springer


Wolff, A. and Zdrahal, Z., Improving retention by identifying and Supporting 'at-risk' students Case study for EDUCAUSE review online



From Bricks to Clicks - The Potential of Data and Analytics in HE

26 January 2016 - OU Analyse mentioned as the UK's only significant headway in Predictive Analytics


OU students' progress to be monitored by software

28 July 2015 - OU Analyse was featured on the BBC News website.


Students under surveillance

24 July 2015 - Predicting at-risk students at the Open University was part of the weekend article in The Financial Times.


The week in higher education – 30 July 2015

30 July 2015 - Times Higher Education mentioned OU Analyse in their weekly overview in academia.


Knowledge Media Institute,
The Open University,
Milton Keynes,
MK7 6AA,
United Kingdom.
Phone: +44 (0)1908 653800