Currently browsing: Items authored or edited by Vaclav Bayer https://orcid.org/0000-0001-8953-6335

Bayer, Vaclav; Mulholland, Paul; Hlosta, Martin; Farrell, Tracie; Herodotou, Christothea and Fernandez, Miriam (2024). Co‐creating an equality diversity and inclusion learning analytics dashboard for addressing awarding gaps in higher education. British Journal of Educational Technology, 55(5) pp. 2058–2074.

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Herodotou, Christothea; Shrestha, Sagun; Comfort, Catherine; Fernandez, Miriam; Andrews, Heshan; Mulholland, Paul; Bayer, Vaclav; Maguire, Claire and Lee, John (2023). PVC-Challenges Response: A student facing dashboard for online undergraduate students.

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.

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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.

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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.

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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.

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

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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.

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.

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

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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.

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