Latest updates from The Open University.
Herodotou, C; Carr, J; Shrestha, S; Comfort, C: Bayer, V; Maguire, C: Lee, J; Andrews, H; Mulholland, P; and Fernandez, M. (2025) Journal of Learning Analytics A participatory approach to designing a student-facing dashboard for online and distance education
Herodotou, C; Carr, J; Shrestha, S; Comfort, C: Bayer, V; Maguire, C: Lee, J; Mulholland, P; and Fernandez, M. (2025) Prescriptive analytics motivating distance learning student action: A case study of a student-facing dashboard. In: LAK25: The 15th International Learning Analytics and Knowledge Conference (LAK 2025), ACM, New York, NY, USA, (In press).
Bayer, V; Mulholland, P; Hlosta, M; Farrell, T; Herodotou, C; & Fernandez, M. (2024). Co-creating an equality diversity and inclusion learning analytics dashboard for addressing awarding gaps in higher education. in British Journal of Educational Technology.
Herodotou, Christothea; Maguire, Claire; Hlosta, Martin and Mulholland, Paul (2023). Predictive Learning Analytics and University Teachers: Usage and perceptions three years post implementation In: LAK2023: LAK23: 13th International Learning Analytics and Knowledge Conference, pp. 68-78.
Rets, Irina; Herodotou, Christothea and Gillespie, Anna (2023). Six Practical Recommendations Enabling Ethical Use of Predictive Learning Analytics in Distance Education Journal of Learning Analytics (Early Access)
Hlosta M., Mutwarasibo, F., Farrell T., Bayer V. and Fernandez M. (2022). Understanding the BAME awarding gap at The Open University by means of quantitative and qualitative data analytics. 2020-22 eSTEem Project Final Report.
Boroowa, A. and Herodotou, C. (2022). Learning Analytics in Open and Distance Higher Education: The Case of the Open University UK . In: Prinsloo, P.; Slade, S. and Khalil, M. eds. Learning Analytics in Open and Distributed Learning.SpringerBriefs in Education. Singapore: Springer, pp. 46-62.
Rienties, Bart and Herodotou, Christothea (2022). Making sense of learning data at scale. In: Sharpe, Rhona; Bennett, Sue and Varga-Atkins, Tünde eds. Handbook for Digital Higher Education . Cheltenham: Edward Elgar Publishing, pp. 260-270.
Hlosta, Martin; Herodotou, Christothea; Papathoma, Tina; Gillespie, Anna and Bergamin, Per (2022). Predictive learning analytics in online education: A deeper understanding through explaining algorithmic errors . Computers and Education: Artificial Intelligence, 3, article no. 100108.
Herodotou C., Maguire C., McDowell N., Hlosta M., Boroowa A. The engagement of university teachers with predictive learning analytics Computers & Education, Volume 173, November 2021, 104285
Rets I., Herodotou C., Bayer V., Hlosta M., and Rienties B. Exploring critical factors of the perceived usefulness of a learning analytics dashboard for distance university students International Journal of Educational Technology in Higher Education (In Press).
Bayer V., Hlosta M., Fernandez M. Learning Analytics and Fairness: Do Existing Algorithms Serve Everyone Equally? AIED 2021; 22nd International Conference on Artificial Intelligence in Education, 14-18 Jun 2021, ONLINE from Utrecht.
Hlosta M., Herodotou Ch., Fernandez M., Bayer V. Impact of Predictive Learning Analytics on Course Awarding Gap of Disadvantaged students in STEM AIED 2021; 22nd International Conference on Artificial Intelligence in Education, 14-18 Jun 2021, ONLINE from Utrecht.
Herodotou Ch. Predictive learning analytics: An empowering tool in the hands of online teachers – Society for Learning Analytics Research (SoLAR) (2021) SoLAR Nexus series, 29th Apr 2021
Kaliisa, Rogers; Gillespie, Anna; Herodotou, Christothea; Kluge, Anders and Rienties, Bart (2021). Teachers’ Perspectives on the Promises, Needs and Challenges of Learning Analytics Dashboards: Insights from Institutions Offering Blended and Distance Learning . In: Sahin, Muhittin and Dirk, Ifenthaler eds. Visualizations and Dashboards for Learning Analytics.Advances in Analytics for Learning and Teaching. Cham: Springer, pp. 351-370.
Hlosta M., Papathoma T., Herodotou C. Explaining errors in predictions of at-risk students in distance learning education International Conference on Artificial Intelligence in Education (AIED’20), 06-10 Jul 2020, Ifrane, Morocco
Hlosta M., Bayer V., Zdrahal Z. Mini Survival Kit: Prediction based recommender to help students escape their critical situation in online courses Edrecsys@LAK Worskhop at 10th International Conference on Learning Analytics and Knowledge (LAK’20)
Herodotou C., Boroowa A., Hlosta M., Rienties B. What do distance learning students seek from student analytics? International Conference on Learning Sciences (ICLS’20), 19-23 Jun 2020, Nashville, TN, USA
Hlosta M., Zdrahal Z., Bayer V., Herodotou C. Why Predictions of At-Risk Students Are Not 100% Accurate? Showing Patterns in False Positive and False Negative Predictions 10th International Conference on Learning Analytics and Knowledge (LAK20)
Herodotou C., Rienties B., Hlosta M., Boroowa A., Mangafa C., Zdrahal Z. The scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study The Internet and Higher Education, Volume 45, April 2020, 100725
Herodotou, Christothea; Rienties, Bart; Verdin, Barry and Boroowa, Avinash (2019). Predictive learning analytics ‘at scale’: Guidelines to successful implementation in Higher Education based on the case of the Open University UK . Journal of Learning Analytics, 6(1) pp. 85-95.
Hlosta M., Kocvara J., Beran D., Zdrahal Z. Visualisation of key splitting milestones to support interventions LAK ’19: Companion Proceedings 9th International Conference on Learning Analytics & Knowledge
Herodotou C., Rienties B., Boroowa A., Zdrahal Z., Hlosta M. A large-scale implementation of Predictive Learning Analytics in Higher Education: The teachers’ role and perspective Educational Technology Research and Development
Herodotou C., Hlosta M., Boroowa A., Rienties B., Zdrahal Z., Mangafa C. Empowering online teachers through predictive learning analytics British Journal of Educational Technology pp. 1-17.
Hlosta M., Zdrahal Z., Zendulka J. Are we meeting a deadline? classification goal achievement in time in the presence of imbalanced data Knowledge-Based Systems. ISSN 09507051
Huptych M., Hlosta M., Zdrahal Z., Kocvara J. Investigating Influence of Demographic Factors on Study Recommenders Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science, volume 10948. Springer, Cham
Nguyen, Q., Huptych, M. and Rienties, B. Linking students’ timing of engagement to learning design and academic performance LAK ’18: Proceedings of the Seventh International Learning Analytics & Knowledge Conference
Kuzilek J., Hlosta M., Zdrahal Z. Open University Learning Analytics dataset Nature Scientific 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., 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
