Bonnin, Geoffray , Bayer, Vaclav, Fernandez, Miriam, Herodotou, Christothea , Hlosta, Martin and Mulholland, Paul(2023). CERSEI: Cognitive Effort Based Recommender System for Enhancing Inclusiveness. In: Responsive and Sustainable Educational Futures. EC-TEL 2023. Lecture Notes in Computer Science, vol 14200, Lecture Notes in Computer Science, Springer, Cham, pp. 692–697.

Reyero Lobo, Paula, Mensio, Martino, Pavon Perez, Angel , Bayer, Vaclav, Kwarteng, Joseph, Fernandez, Miriam, Daga, Enrico and Alani, Harith(2022). Estimating Ground Truth in a Low-labelled Data Regime: A Study of Racism Detection in Spanish. In: Workshop Proceedings of the 16th International AAAI Conference on Web and Social Media.

Bayer, Vaclav, (2021). Targeting degree-awarding gap across ethnicities through means of OUAnalyse predictions. Postgraduate Research Poster Competition, The Open University.

Rets, Irina , Herodotou, Christothea , Bayer, Vaclav, Hlosta, Martin and Rienties, Bart (2021). Exploring critical factors of the perceived usefulness of a learning analytics dashboard for distance university students. International Journal of Educational Technology in Higher Education, 18(1), article no. 46.

Hlosta, Martin, Herodotou, Christothea , Fernandez, Miriam and Bayer, Vaclav(2021). Impact of Predictive Learning Analytics on Course Awarding Gap of Disadvantaged students in STEM. In: Artificial Intelligence in Education: 22nd International Conference, AIED 2021, Utrecht, the Netherlands, June 14-18, 2021, Proceedings, Part II (Roll, Ido; McNamara, Danielle; Sosnovsky, Sergey; Luckin, Rose and Dimitrova, Vania eds.), Lecture Notes in Artificial Intelligence, Springer, Cham, pp. 190–195.

Bayer, Vaclav, Hlosta, Martin and Fernandez, Miriam(2021). Learning Analytics and Fairness: Do Existing Algorithms Serve Everyone Equally? In: Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science, vol 12749 (Roll, I.; McNamara, D.; Sosnovsky, S.; Luckin, R. and Dimitrova, V. eds.), Springer.

Hlosta, Martin, Bayer, Vaclav and Zdrahal, Zdenek(2020). Mini Survival Kit: Prediction based recommender to help students escape their critical situation in online courses. In: Proceedings of the 10th International Conference on Learning Analytics and Knowledge (LAK20), 23-27 Mar 2020, Frankfurt am Main, Germany.

Hlosta, Martin, Zdrahal, Zdenek, Bayer, Vaclav and Herodotou, Christothea (2020). Why Predictions of At-Risk Students Are Not 100% Accurate? Showing Patterns in False Positive and False Negative Predictions. In: Proceedings of the 10th International Conference on Learning Analytics and Knowledge (LAK20), 23-27 Mar 2020, Frankfurt am Main, Germany.

Pontika, Nancy, Knoth, Petr, Anastasiou, Lucas, Charalampous, Aristotelis , Cancellieri, Matteo, Pearce, Samuel and Bayer, Vaclav(2017). The uptake of the CORE recommender in repositories. OpenRepositories2017.

Knoth, Petr, Anastasiou, Lucas, Charalampous, Aristotelis , Cancellieri, Matteo, Pearce, Samuel, Pontika, Nancy and Bayer, Vaclav(2017). Towards effective research recommender systems for repositories. In: Open Repositories 2017, 26-30 Jun 2017, Brisbane, Australia.