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 factors of awarding gaps.

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

Team: Miriam Fernandez, Martin Hlosta, Paul Mulholland, Zdenek Zdrahal

KMi Researchers Take Part in DATATHON AGAINST RACISM

A spotlight on Vaclav: OUAnalyse

OUAnalyse among 4 Best Projects for using AI in Education by UNESCO

Investigating Learning Technology for Equality, Diversity and Inclusion

OU wins two categories in DataIQ 2020 Awards

Reyero Lobo, P., Mensio, M., Pavon Perez, A., Bayer, V., Kwarteng, 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