Markel Sanz Ausin

Works (6)

Updated: November 8th, 2024 05:00

2023 chapter book

A Unified Batch Hierarchical Reinforcement Learning Framework for Pedagogical Policy Induction with Deep Bisimulation Metrics

By: M. Ausin n , M. Abdelshiheed n , T. Barnes n & M. Chi n

author keywords: Deep Reinforcement Learning; Pedagogical Policy
topics (OpenAlex): Intelligent Tutoring Systems and Adaptive Learning; Reinforcement Learning in Robotics; Online Learning and Analytics
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Sources: Web Of Science, ORCID, Crossref, NC State University Libraries
Added: December 3, 2023

2022 article

The Impact of Batch Deep Reinforcement Learning on Student Performance: A Simple Act of Explanation Can Go A Long Way

Ausin, M. S., Maniktala, M., Barnes, T., & Chi, M. (2022, November 28). International Journal of Artificial Intelligence in Education.

By: M. Ausin n , M. Maniktala n, T. Barnes n & M. Chi n

author keywords: Deep reinforcement learning; Pedagogical policy; Explanation
topics (OpenAlex): Intelligent Tutoring Systems and Adaptive Learning; Evolutionary Algorithms and Applications; Online Learning and Analytics
TL;DR: The results suggest that pairing simple explanations with the Batch DRL policy with explanations can be an important and effective technique for applying RL to real-life, human-centric tasks. (via Semantic Scholar)
Sources: ORCID, Web Of Science
Added: November 29, 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
topics (OpenAlex): Intelligent Tutoring Systems and Adaptive Learning; Online Learning and Analytics; Reinforcement Learning in Robotics
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

2021 article

Tackling the Credit Assignment Problem in Reinforcement Learning-Induced Pedagogical Policies with Neural Networks

Ausin, M. S., Maniktala, M., Barnes, T., & Chi, M. (2021, January 1). Lecture Notes in Computer Science.

By: M. Ausin n , M. Maniktala n, T. Barnes n & M. Chi n

author keywords: Pedagogical agent; Credit assignment problem; Deep reinforcement learning
topics (OpenAlex): Intelligent Tutoring Systems and Adaptive Learning; Reinforcement Learning in Robotics; Online Learning and Analytics
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Sources: ORCID, Web Of Science
Added: June 16, 2021

2020 article

Exploring the Impact of Simple Explanations and Agency on Batch Deep Reinforcement Learning Induced Pedagogical Policies

Ausin, M. S., Maniktala, M., Barnes, T., & Chi, M. (2020, January 1). Lecture Notes in Computer Science, pp. 472–485.

By: M. Ausin n , M. Maniktala  n, T. Barnes  n & M. Chi  n

author keywords: Deep reinforcement learning; Pedagogical policy; Explanation
topics (OpenAlex): Online Learning and Analytics; Explainable Artificial Intelligence (XAI); Intelligent Tutoring Systems and Adaptive Learning
TL;DR: The results suggest that pairing simple explanations with induced RL policies can be an important and effective technique for applying RL to real-life human-centric tasks. (via Semantic Scholar)
Source: ORCID
Added: September 14, 2020

2019 article

Hierarchical Reinforcement Learning for Pedagogical Policy Induction

Zhou, G., Azizsoltani, H., Ausin, M. S., Barnes, T., & Chi, M. (2019, January 1). Lecture Notes in Computer Science, 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
topics (OpenAlex): Intelligent Tutoring Systems and Adaptive Learning; Online Learning and Analytics; Data Stream Mining Techniques
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)
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
16. Peace, Justice and Strong Institutions (OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: December 2, 2019

Employment

Updated: October 4th, 2025 14:00

Universitas Airlangga Surabaya, East Java, ID
Director, LLM

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