Guojing Zhou

Works (4)

Updated: July 5th, 2023 15:05

2021 chapter book

Evaluating Critical Reinforcement Learning Framework in the Field

By: S. Ju n, G. Zhou n, M. Abdelshiheed n , T. Barnes n & M. Chi n

Event: Springer International Publishing

author keywords: Critical decisions; Reinforcement learning; ITS
Sources: Web Of Science, ORCID, NC State University Libraries, Crossref
Added: November 28, 2022

2021 article

Leveraging Granularity: Hierarchical Reinforcement Learning for Pedagogical Policy Induction

Zhou, G., Azizsoltani, H., Ausin, M. S., Barnes, T., & Chi, M. (2021, August 16). INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION, Vol. 8.

By: G. Zhou n, H. Azizsoltani n, M. Ausin  n, T. Barnes n & M. Chi n

author keywords: Hierarchical reinforcement learning; Decision granularity; Pedagogical policy
TL;DR: An offline, off-policy Gaussian Processes based Hierarchical Reinforcement Learning (HRL) framework is proposed and applied to induce a hierarchical pedagogical policy that makes adaptive, effective decisions at both the problem and step levels. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: August 23, 2021

2019 article

Hierarchical Reinforcement Learning for Pedagogical Policy Induction

ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2019), PT I, Vol. 11625, pp. 544–556.

By: G. Zhou n, H. Azizsoltani n, M. Ausin  n, T. Barnes n & M. Chi n

author keywords: Hierarchical Reinforcement Learning; Pedagogical policies
TL;DR: This paper proposes and applies an offline, off-policy Gaussian Processes based Hierarchical Reinforcement Learning (HRL) framework to induce a hierarchical pedagogical policy that makes decisions at both problem and step levels and shows that the HRL policy is significantly more effective than a Deep Q-Network induced policy and a random yet reasonable baseline policy. (via Semantic Scholar)
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16. Peace, Justice and Strong Institutions (OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: December 2, 2019

2015 conference paper

Data-driven worked examples improve retention and completion in a logic tutor

Artificial intelligence in education, aied 2015, 9112, 726–729.

By: B. Mostafavi, G. Zhou, C. Lynch, M. Chi & T. Barnes

Source: NC State University Libraries
Added: August 6, 2018

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