A Generalized Apprenticeship Learning Framework for Modeling Heterogeneous Student Pedagogical Strategies

TL;DR

This paper introduces EM-EDM, a novel apprenticeship learning framework that effectively models diverse student pedagogical strategies in e-learning environments, outperforming existing methods in handling heterogeneity and continuous state spaces.

Contribution

The paper proposes EM-EDM, a generalized apprenticeship learning framework capable of modeling heterogeneous pedagogical policies from demonstrations, addressing limitations of prior algorithms in continuous spaces and heterogeneity.

Findings

EM-EDM outperforms four AL baselines across all metrics.

EM-EDM surpasses two DRL policies in effectiveness.

The framework effectively models complex pedagogical decision-making.

Abstract

A key challenge in e-learning environments like Intelligent Tutoring Systems (ITSs) is to induce effective pedagogical policies efficiently. While Deep Reinforcement Learning (DRL) often suffers from sample inefficiency and reward function design difficulty, Apprenticeship Learning(AL) algorithms can overcome them. However, most AL algorithms can not handle heterogeneity as they assume all demonstrations are generated with a homogeneous policy driven by a single reward function. Still, some AL algorithms which consider heterogeneity, often can not generalize to large continuous state space and only work with discrete states. In this paper, we propose an expectation-maximization(EM)-EDM, a general AL framework to induce effective pedagogical policies from given optimal or near-optimal demonstrations, which are assumed to be driven by heterogeneous reward functions. We compare the effectiveness of the policies induced by our proposed EM-EDM against four AL-based baselines and two policies induced by DRL on two different but related tasks that involve pedagogical action prediction. Our overall results showed that, for both tasks, EM-EDM outperforms the four AL baselines across all performance metrics and the two DRL baselines. This suggests that EM-EDM can effectively model complex student pedagogical decision-making processes through the ability to manage a large, continuous state space and adapt to handle diverse and heterogeneous reward functions with very few given demonstrations.