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Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments.

, , , , and . EDM, International Educational Data Mining Society, (2022)

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Tackling the ordinal and imbalance nature of a melanoma image classification problem., , , , and . IJCNN, page 2156-2163. IEEE, (2016)A PAC-Bayesian Perspective on Structured Prediction with Implicit Loss Embeddings., , , and . CoRR, (2020)Ordinal Regression Methods: Survey and Experimental Study., , , , and . IEEE Trans. Knowl. Data Eng., 28 (1): 127-146 (2016)Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments., , , , and . EDM, International Educational Data Mining Society, (2022)X5Learn: A Personalised Learning Companion at the Intersection of AI and HCI., , , , , , , and . IUI Companion, page 70-74. ACM, (2021)TrueLearn: A Python Library for Personalised Informational Recommendations with (Implicit) Feedback., , , , , and . ORSUM@RecSys, volume 3549 of CEUR Workshop Proceedings, CEUR-WS.org, (2023)The Probabilities Also Matter: A More Faithful Metric for Faithfulness of Free-Text Explanations in Large Language Models., , , and . CoRR, (2024)Machine learning paradigms for weed mapping via unmanned aerial vehicles., , , , , and . SSCI, page 1-8. IEEE, (2016)Transfer and zero-shot learning for scalable weed detection and classification in UAV images., , , , and . Knowl. Based Syst., (2024)Ordinal Evolutionary Artificial Neural Networks for Solving an Imbalanced Liver Transplantation Problem., , , , and . HAIS, volume 9648 of Lecture Notes in Computer Science, page 451-462. Springer, (2016)