Improving Student Problem Solving in Narrative-Centered Learning Environments: a Modular Reinforcement Learning Framework

Abstract

Narrative-centered learning environments comprise a class of game-based learning environments that embed problem solving in interactive stories. A key challenge posed by narrative-centered learning is dynamically tailoring story events to enhance student learning. In this paper, we investigate the impact of a data-driven tutorial planner on students’ learning processes in a narrative-centered learning environment, Crystal Island. We induce the tutorial planner by employing modular reinforcement learning, a multi-goal extension of classical reinforcement learning. To train the planner, we collected a corpus from 453 middle school students who used Crystal Island in their classrooms. Afterward, we investigated the induced planner’s impact in a follow-up experiment with another 75 students. The study revealed that the induced planner improved students’ problem-solving processes—including hypothesis testing and information gathering behaviors—compared to a control condition, suggesting that modular reinforcement learning is an effective approach for tutorial planning in narrative-centered learning environments.