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[2602.20527] A Generalized Apprenticeship Learning Framework for Capturing Evolving Student Pedagogical Strategies
Summary
This article presents a generalized apprenticeship learning framework, THEMES, designed to enhance pedagogical strategies in e-learning by capturing evolving student behaviors through reinforcement learning techniques.
Why It Matters
The research addresses significant challenges in applying deep reinforcement learning to educational technologies, particularly in improving the efficiency of learning systems. By introducing a framework that adapts to dynamic student behaviors, it has the potential to transform intelligent tutoring systems and enhance educational outcomes.
Key Takeaways
- Introduces THEMES, a framework for capturing evolving student learning strategies.
- Demonstrates superior performance over six state-of-the-art baselines in pedagogical policy induction.
- Achieves high predictive accuracy using minimal data from previous semesters.
- Addresses sample inefficiency and reward function design challenges in reinforcement learning.
- Highlights the potential for improved intelligent tutoring systems through advanced learning frameworks.
Computer Science > Machine Learning arXiv:2602.20527 (cs) [Submitted on 24 Feb 2026] Title:A Generalized Apprenticeship Learning Framework for Capturing Evolving Student Pedagogical Strategies Authors:Md Mirajul Islam, Xi Yang, Adittya Soukarjya Saha, Rajesh Debnath, Min Chi View a PDF of the paper titled A Generalized Apprenticeship Learning Framework for Capturing Evolving Student Pedagogical Strategies, by Md Mirajul Islam and 3 other authors View PDF HTML (experimental) Abstract:Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) have advanced rapidly in recent years and have been successfully applied to e-learning environments like intelligent tutoring systems (ITSs). Despite great success, the broader application of DRL to educational technologies has been limited due to major challenges such as sample inefficiency and difficulty designing the reward function. In contrast, Apprenticeship Learning (AL) uses a few expert demonstrations to infer the expert's underlying reward functions and derive decision-making policies that generalize and replicate optimal behavior. In this work, we leverage a generalized AL framework, THEMES, to induce effective pedagogical policies by capturing the complexities of the expert student learning process, where multiple reward functions may dynamically evolve over time. We evaluate the effectiveness of THEMES against six state-of-the-art baselines, demonstrating its superior performance and highlighting its potential as a p...
