Audrey Ekuban

Research Asst. for Mainstreaming Learning Analytics

I am currently engaged as a full-time Research Assistant in KMi. Funded by the Institute of Coding, I have been exploring ways in which Learning Analytics tools could be adopted across various academic institutions. In addition to liaising with these universities, I have been involved in writing Data Specification documents, data collection, data cleansing and the training of machine learning models. Prior to this, I have had several years’ experience working in Information Management as a Senior Systems Architect (Data Integration), at Anglia Ruskin University. I hold an MSc in Data Science which was gained from Goldsmiths, University of London. My MSc research project was entitled "Image Captioning with Deep Learning".

In addition to my Research Assistant role at KMi, I am also undertaking a part-time PhD, which I started in February 2020. The focus of my PhD is privacy-by-design, privacy-enhancing and decentralised technologies, that would students to retain data privacy, whilst at the same time allow academic institutions to benefit from ethically principled Learning Analytics. In this regard, my sub-research interests include Differential Privacy, Federated Learning, Homomorphic Encryption, Secure Multi-Party Computation, Blockchain.


Data Science, Learning Analytics, Machine Learning, Data Mining, Natural Language Processing, Computer Vision, Deep Learning


Ekuban, Audrey and Domingue, John(2023). An Architecture for a Decentralised Learning Analytics Platform (Positioning Paper). In: CEUR Workshop Proceedings: SEMMES 2023: Semantic Methods for Events and Stories workshop ESWC 2023 (Alam, Mehwish; Trojahn, Cassia; Hertling, Sven; Pesquita, Catia; Aebeloe, Christian; Aras, Hidir; Azzam, Amr; Cano, Juan; Domingue, John; Gottschalk, Simon; Hartig, Olaf; Hose, Katja; Kirrane, Sabrina; Lisena, Pasquale; Osborne, Francesco; Rohde, Philipp; Steels, Luc; Taelman, Ruben; Third, Aisling; Tiddi, Ilaria and Türker, Rima eds.), CEUR Workshop Proceedings (, 3443.

Ekuban, Audrey and Domingue, John(2023). Towards Decentralised Learning Analytics (Positioning Paper). In: Companion Proceedings of the ACM Web Conference 2023 (WWW '23 Companion), April 30-May 4, 2023, Austin, TX, USA (Ding, Ying; Tang, Jie; Sequeda, Juan; Aroyo, Lora; Castillo, Carlos and Houben, Geert-Jan eds.), ACM, New York, pp. 1435–1438.

Haleem, Muhammad Salman,Ekuban, Audrey, Antonini, Alessio, Pagliara, Silvio, Pecchia, Leandro and Allocca, Carlo ,(2023). Deep-Learning-Driven Techniques for Real-Time Multimodal Health and Physical Data Synthesis. Electronics, 12(9) p. 1989.

Ekuban, Audrey, (2021). EMPRESS: Privacy-preserving decentralised educational analytics with student self-sovereignty. Postgraduate Research Poster Competition, The Open University.

Ekuban, Audrey, Mikroyannidis, Alexander, Third, Allan and Domingue, John(2021). Using GitLab Interactions To Predict Student Success When Working As Part Of A Team. In: Educating Engineers for Future Industrial Revolutions. ICL 2020. Advances in Intelligent Systems and Computing (Auer, M.E. and Rüütmann, T. eds.), 1328 pp. 127–138.