I am a Data Engineer at the OUAnalyse team and currently also a technical lead of the project. As a part-time PhD student, I am investigating how Learning Analytics can be used to target awarding gaps, which is a severe problem occurring in Higher Education institutions across the whole UK. My main focus is currently the development of an Equity, Diversity and Inclusion dashboard to spotlight awarding gaps across diverse groups of students within their modules and help understand the driving...

Keys: Learning Analytics; Predictive Learning Analytics; Recommender Systems; Equity, Diversity and Inclusion; Research software development

Bonnin, G., Bayer, V., Fernandez, M., Herodotou, C., Hlosta, M. and Mulholland, P. (2023) CERSEI: Cognitive Effort Based Recommender System for Enhancing Inclusiveness, 18th European Conference on Technology Enhanced Learning, EC-TEL 2023, Aveiro, Portugal

Reyero Lobo, P., Mensio, M., Pavon Perez, A., Bayer, V., , j., Fernandez, M., Daga, E. and Alani, H. (2022) Estimating Ground Truth in a Low-labelled Data Regime: A Study of Racism Detection in Spanish, Workshop on Novel Evaluation Approaches for Text Classification Systems on Social Media (NEATCLasS), Atlanta, Georgia

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)

Bayer, V. (2021) Targeting degree-awarding gap across ethnicities through means of OUAnalyse predictions

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