Real-Time Narrative-Centered Tutorial Planning for Story-Based Learning

Interactive story-based learning environments offer significant potential for crafting narrative tutorial guidance to create pedagogically effective learning experiences that are tailored to individual students. This paper reports on an empirical evaluation of machine-learned models of narrative-centered tutorial planning for story-based learning environments. We investigate differences in learning gains and in-game performance during student interactions in a rich virtual storyworld. One hundred and eighty-three middle school students participated in the study, which had three conditions: Minimal Guidance, Intermediate Guidance, and Full Guidance. Results reveal statistically significant differences in learning and in-game problem-solving effectiveness between students who received minimal guidance and students who received full guidance. Students in the full guidance condition tended to demonstrate higher learning outcomes and problem-solving efficiency. The findings suggest that machine-learned models of narrative-centered tutorial planning can improve learning outcomes and in-game efficiency.