Ten Years of Research on Intelligent Educational Games for Learning Spelling and Mathematics

TL;DR

This paper reviews a decade of research on intelligent educational games for spelling and math, highlighting personalized training, multi-modal content representation, and data-driven student modeling.

Contribution

It introduces a comprehensive architecture for adaptive educational games, integrating content representation, student modeling, and analytics based on extensive data.

Findings

Effective personalized training through student modeling

Insights into learning patterns from 20,000+ children

Machine learning tools for analytics and visualization

Abstract

In this article, we present our findings from ten years of research on intelligent educational games. We discuss the architecture of our training environments for learning spelling and mathematics, and specifically focus on the representation of the content and the controller that enables personalized trainings. We first show the multi-modal representation that reroutes information through multiple perceptual cues and discuss the game structure. We then present the data-driven student model that is used for a personalized, adaptive presentation of the content. We further leverage machine learning for analytics and visualization tools targeted at teachers and experts. A large data set consisting of training sessions of more than 20,000 children allows statistical interpretations and insights into the nature of learning.