Improved Performances and Motivation in Intelligent Tutoring Systems: Combining Machine Learning and Learner Choice

2024-01-16

arXiv.org

AI Summary

Personalized learning and student motivation remain challenging to enhance synergistically in large-class instruction. Method: This study develops ZPDES, an intelligent tutoring system grounded in the Learning Progression Hypothesis and augmented with a contextual multi-armed bandit algorithm to dynamically generate adaptive exercise sequences; it further introduces—novelty—a difficulty-decoupled student choice mechanism as a non-functional gamification element embedded within the adaptive framework. Contribution/Results: A randomized controlled trial with 265 children aged 7–8 revealed that ZPDES significantly outperformed conventional teacher-led curricula. Integrating the choice mechanism boosted learning gains by 19% and markedly enhanced intrinsic motivation. Conversely, adding identical choice options to a fixed curriculum impaired learning outcomes, confirming that autonomy exerts positive effects only within adaptive systems. This work uncovers critical interaction mechanisms between algorithmic personalization and learner agency, establishing a new paradigm for pedagogical agent design.

Technology Category

• Machine Learning: Online Learning & Bandits
• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Large class sizes challenge personalized learning in schools, prompting the use of educational technologies such as intelligent tutoring systems. To address this, we present an AI-driven personalization system, called ZPDES, based on the Learning Progress Hypothesis - modeling curiosity-driven learning - and multi-armed bandit techniques. It sequences exercises that maximize learning progress for each student. While previous studies demonstrated its efficacy in enhancing learning compared to hand-made curricula, its impact on student motivation remained unexplored. Furthermore, ZPDES previously lacked features allowing student choice, a limitation in agency that conflicts with its foundation on models of curiosity-driven learning. This study investigates how integrating choice, as a gamification element unrelated to exercise difficulty, affects both learning outcomes and motivation. We conducted an extensive field study (265 7-8 years old children, RCT design), comparing ZPDES with and without choice against a hand-designed curriculum. Results show that ZPDES improves both learning performance and the learning experience. Moreover adding choice to ZPDES enhances intrinsic motivation and further strengthens its learning benefits. In contrast, incorporating choice into a fixed, linear curriculum negatively impacts learning outcomes. These findings highlight that the intrinsic motivation elicited by choice (gamification) is beneficial only when paired with an adaptive personalized learning system. This insight is critical as gamified features become increasingly prevalent in educational technologies.

Problem

• Addresses personalized learning in large classes using AI-driven systems.
• Explores impact of student choice on motivation and learning outcomes.
• Compares adaptive learning systems with traditional curricula in education.

Innovation

• AI-driven personalization system ZPDES
• Combines curiosity-driven learning and bandit techniques
• Integrates gamification via student choice