A Generalized Apprenticeship Learning Framework for Capturing Evolving Student Pedagogical Strategies
arXiv:2602.20527v1 Announce Type: new 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
arXiv:2602.20527v1 Announce Type: new 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 powerful alternative for inducing effective pedagogical policies and show that it can achieve high performance, with an AUC of 0.899 and a Jaccard of 0.653, using only 18 trajectories of a previous semester to predict student pedagogical decisions in a later semester.
Executive Summary
This article proposes a generalized Apprenticeship Learning framework, THEMES, to capture evolving student pedagogical strategies. The framework leverages expert demonstrations to infer underlying reward functions and derive decision-making policies. The authors evaluate THEMES against state-of-the-art baselines, demonstrating its superior performance in predicting student pedagogical decisions. The framework achieves high performance using only 18 trajectories of a previous semester, showing its potential as a powerful alternative for inducing effective pedagogical policies.
Key Points
- ▸ Introduction of a generalized Apprenticeship Learning framework, THEMES
- ▸ Application of THEMES to capture evolving student pedagogical strategies
- ▸ Evaluation of THEMES against six state-of-the-art baselines
Merits
Effective use of expert demonstrations
THEMES leverages a few expert demonstrations to infer the expert's underlying reward functions, reducing the need for extensive data and improving sample efficiency.
Demerits
Limited generalizability
The framework's performance may be limited to the specific context and student population used in the evaluation, requiring further testing to ensure generalizability.
Expert Commentary
The proposed framework, THEMES, offers a promising approach to capturing the complexities of student learning processes. By leveraging expert demonstrations, THEMES can infer underlying reward functions and derive effective pedagogical policies. The evaluation results demonstrate the framework's potential, but further research is needed to ensure generalizability and address potential limitations. The implications of this work are significant, as it can inform the development of personalized learning systems and improve education policy.
Recommendations
- ✓ Further testing of THEMES in diverse educational contexts to ensure generalizability
- ✓ Exploration of the framework's potential applications in other fields, such as workforce training and development.
