no code implementations • 15 May 2023 • Xi Yang, Ge Gao, Min Chi
Apprenticeship learning (AL) is a process of inducing effective decision-making policies via observing and imitating experts' demonstrations.
no code implementations • 23 Apr 2023 • Mark Abdelshiheed, John Wesley Hostetter, Tiffany Barnes, Min Chi
This work leverages Deep Reinforcement Learning (DRL) in providing adaptive metacognitive interventions to bridge the gap between the three knowledge types and prepare students for future learning across Intelligent Tutoring Systems (ITSs).
no code implementations • 17 Apr 2023 • Mark Abdelshiheed, John Wesley Hostetter, Tiffany Barnes, Min Chi
In two consecutive semesters, we conducted two classroom experiments: Exp.
no code implementations • 18 Mar 2023 • Mark Abdelshiheed, John Wesley Hostetter, Xi Yang, Tiffany Barnes, Min Chi
In this work, students were trained on a logic tutor that supports a default forward-chaining (FC) and a backward-chaining (BC) strategy.
no code implementations • 18 Mar 2023 • Mark Abdelshiheed, John Wesley Hostetter, Preya Shabrina, Tiffany Barnes, Min Chi
Deductive domains are typical of many cognitive skills in that no single problem-solving strategy is always optimal for solving all problems.
no code implementations • 18 Feb 2023 • Ge Gao, Song Ju, Markel Sanz Ausin, Min Chi
Reinforcement learning (RL) has been extensively researched for enhancing human-environment interactions in various human-centric tasks, including e-learning and healthcare.
1 code implementation • 28 Jan 2023 • Qitong Gao, Ge Gao, Min Chi, Miroslav Pajic
In this work, we propose the variational latent branching model (VLBM) to learn the transition function of MDPs by formulating the environmental dynamics as a compact latent space, from which the next states and rewards are then sampled.
no code implementations • 27 Jul 2022 • Preya Shabrina, Behrooz Mostafavi, Mark Abdelshiheed, Min Chi, Tiffany Barnes
Backward problem-solving strategy is closely related to the process of subgoaling, where problem solving iteratively refines the goal into a new subgoal to reduce difficulty.
no code implementations • 7 Jul 2022 • Mehak Maniktala, Min Chi, Tiffany Barnes
In this paper, we present a novel data-driven approach to incorporate students' hint usage in predicting their need for help.
1 code implementation • 7 Jun 2022 • Yang Shi, Min Chi, Tiffany Barnes, Thomas Price
Knowledge tracing (KT) models are a popular approach for predicting students' future performance at practice problems using their prior attempts.
1 code implementation • 15 Mar 2022 • Ge Gao, Farzaneh Khoshnevisan, Min Chi
Real-world Electronic Health Records (EHRs) are often plagued by a high rate of missing data.
1 code implementation • NeurIPS 2021 • Mitchell Plyler, Michael Green, Min Chi
Rationales, snippets of extracted text that explain an inference, have emerged as a popular framework for interpretable natural language processing (NLP).
no code implementations • 6 May 2021 • Yeo Jin Kim, Min Chi
Much of DRL work has been focused on sequences of events with discrete time steps and ignores the irregular time intervals between consecutive events.
no code implementations • 2 May 2021 • Markel Sanz Ausin, Hamoon Azizsoltani, Song Ju, Yeo Jin Kim, Min Chi
Overall, our results show that the effectiveness of InferNet is robust against noisy reward functions and is an effective add-on mechanism for solving temporal CAP in a wide range of RL tasks, from classic RL simulation environments to a real-world RL problem and for both online and offline learning.
1 code implementation • 15 Jan 2021 • Daniel Shen, Min Chi
Dynamic time warping (DTW) plays an important role in analytics on time series.
no code implementations • 26 Oct 2020 • Farzaneh Khoshnevisan, Min Chi
We evaluate our framework for early diagnosis of an extremely challenging condition, septic shock, using two real-world EHRs from distinct medical systems in the U. S.
no code implementations • 8 Oct 2020 • Mehak Maniktala, Christa Cody, Amy Isvik, Nicholas Lytle, Min Chi, Tiffany Barnes
A core problem in solving the assistance dilemma is the need to discover when students are unproductive so that the tutor can intervene.
no code implementations • 28 Sep 2020 • Mehak Maniktala, Christa Cody, Tiffany Barnes, Min Chi
Within intelligent tutoring systems, considerable research has investigated hints, including how to generate data-driven hints, what hint content to present, and when to provide hints for optimal learning outcomes.