Personalizing Mathematical Game-based Learning for Children: A Preliminary Study

TL;DR

This study introduces an AI-based framework for personalizing mathematical game levels in game-based learning, using a classifier trained on player-generated levels to enhance engagement and learning.

Contribution

It presents a novel AI-driven approach to classify and predict valid game levels, improving personalization in math game-based learning systems.

Findings

Random Forest outperforms other classifiers in accuracy.

206 game levels were used to train the classifier.

AI integration can personalize game levels for learners.

Abstract

Game-based learning (GBL) is widely adopted in mathematics education. It enhances learners' engagement and critical thinking throughout the mathematics learning process. However, enabling players to learn intrinsically through mathematical games still presents challenges. In particular, effective GBL systems require dozens of high-quality game levels and mechanisms to deliver them to appropriate players in a way that matches their learning abilities. To address this challenge, we propose a framework, guided by adaptive learning theory, that uses artificial intelligence (AI) techniques to build a classifier for player-generated levels. We collect 206 distinct game levels created by both experts and advanced players in Creative Mode, a new tool in a math game-based learning app, and develop a classifier to extract game features and predict valid game levels. The preliminary results show that the Random Forest model is the optimal classifier among the four machine learning classification models (k-nearest neighbors, decision trees, support vector machines, and random forests). This study provides insights into the development of GBL systems, highlighting the potential of integrating AI into the game-level design process to provide more personalized game levels for players.