用AI自动筛选儿童数学游戏关卡，提升个性化学习体验。
Personalizing Mathematical Game-based Learning for Children: A Preliminary Study
- 基于自适应学习理论，用AI分析玩家自制关卡特征
- 随机森林模型在4种算法中表现最优，准确率超其他模型
- 为数学游戏提供个性化关卡推荐，适合教育科技研发者
游戏化学习（GBL）广泛应用于数学教育，能有效提升学习者参与度与批判性思维。然而，如何通过数学游戏实现内在学习仍面临挑战。高效的GBL系统需大量高质量关卡，并能根据学习者能力精准推送。为此，本文提出一种基于自适应学习理论的框架，利用人工智能技术构建玩家生成关卡的分类器。研究收集了206个由专家和高级玩家在数学游戏应用新功能“创造模式”中设计的游戏关卡，开发分类器提取关卡特征并预测其有效性。初步结果显示，在四种机器学习模型（k近邻、决策树、支持向量机、随机森林）中，随机森林表现最佳。该研究为游戏化学习系统开发提供了新思路，展示了将AI融入关卡设计流程以实现个性化推荐的潜力。
原文摘要 · Abstract (English)
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.
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