Markel Sanz Ausin

Ausin, M. S., Maniktala, M., Barnes, T., & Chi, M. (2022, November 28). The Impact of Batch Deep Reinforcement Learning on Student Performance: A Simple Act of Explanation Can Go A Long Way. INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION. https://doi.org/10.1007/s40593-022-00312-3

Zhou, G., Azizsoltani, H., Ausin, M. S., Barnes, T., & Chi, M. (2021, August 16). Leveraging Granularity: Hierarchical Reinforcement Learning for Pedagogical Policy Induction. INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION, Vol. 8. https://doi.org/10.1007/s40593-021-00269-9

Ausin, M. S., Maniktala, M., Barnes, T., & Chi, M. (2021). Tackling the Credit Assignment Problem in Reinforcement Learning-Induced Pedagogical Policies with Neural Networks. ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT I, Vol. 12748, pp. 356–368. https://doi.org/10.1007/978-3-030-78292-4_29

Ausin, M. S., Maniktala, M., Barnes, T., & Chi, M. (2020). Exploring the Impact of Simple Explanations and Agency on Batch Deep Reinforcement Learning Induced Pedagogical Policies. In Lecture Notes in Computer Science (pp. 472–485). https://doi.org/10.1007/978-3-030-52237-7_38

Zhou, G., Azizsoltani, H., Ausin, M. S., Barnes, T., & Chi, M. (2019). Hierarchical Reinforcement Learning for Pedagogical Policy Induction. ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2019), PT I, Vol. 11625, pp. 544–556. https://doi.org/10.1007/978-3-030-23204-7_45