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Found 18 papers, 5 papers with code
An Offline Time-aware Apprenticeship Learning Framework for Evolving Reward Functions
Apprenticeship learning (AL) is a process of inducing effective decision-making policies via observing and imitating experts' demonstrations.
Bridging Declarative, Procedural, and Conditional Metacognitive Knowledge Gap Using Deep Reinforcement Learning
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).
Leveraging Deep Reinforcement Learning for Metacognitive Interventions across Intelligent Tutoring Systems
In two consecutive semesters, we conducted two classroom experiments: Exp.
Mixing Backward- with Forward-Chaining for Metacognitive Skill Acquisition and Transfer
In this work, students were trained on a logic tutor that supports a default forward-chaining (FC) and a backward-chaining (BC) strategy.
The Power of Nudging: Exploring Three Interventions for Metacognitive Skills Instruction across Intelligent Tutoring Systems
Deductive domains are typical of many cognitive skills in that no single problem-solving strategy is always optimal for solving all problems.
HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare
Reinforcement learning (RL) has been extensively researched for enhancing human-environment interactions in various human-centric tasks, including e-learning and healthcare.
Variational Latent Branching Model for Off-Policy Evaluation
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.
Investigating the Impact of Backward Strategy Learning in a Logic Tutor: Aiding Subgoal Learning towards Improved Problem Solving
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.
Enhancing a Student Productivity Model for Adaptive Problem-Solving Assistance
In this paper, we present a novel data-driven approach to incorporate students' hint usage in predicting their need for help.
Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks
Knowledge tracing (KT) models are a popular approach for predicting students' future performance at practice problems using their prior attempts.
Reconstructing Missing EHRs Using Time-Aware Within- and Cross-Visit Information for Septic Shock Early Prediction
Real-world Electronic Health Records (EHRs) are often plagued by a high rate of missing data.
Making a (Counterfactual) Difference One Rationale at a Time
Rationales, snippets of extracted text that explain an inference, have emerged as a popular framework for interpretable natural language processing (NLP).
Time-Aware Q-Networks: Resolving Temporal Irregularity for Deep Reinforcement Learning
Much of DRL work has been focused on sequences of events with discrete time steps and ignores the irregular time intervals between consecutive events.
InferNet for Delayed Reinforcement Tasks: Addressing the Temporal Credit Assignment Problem
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.
TC-DTW: Accelerating Multivariate Dynamic Time Warping Through Triangle Inequality and Point Clustering
Dynamic time warping (DTW) plays an important role in analytics on time series.
An Adversarial Domain Separation Framework for Septic Shock Early Prediction Across EHR Systems
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.
Extending the Hint Factory for the assistance dilemma: A novel, data-driven HelpNeed Predictor for proactive problem-solving help
A core problem in solving the assistance dilemma is the need to discover when students are unproductive so that the tutor can intervene.
Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor
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.
