【作 者】：
张 屹， 马静思， 周平红， 刘金芳， 王 康， 高晗蕊， 彭 景
【关 键 词 】：
计算思维实践； 游戏化学习； 人工智能课程； 高中生
【栏 目】：
课程与教学
【中文摘要】：
推动中小学人工智能教育培养学生的计算思维已成为国内外教育政策和课程标准的共同目标与要求。目前中小学人工智能课程主要关注理论知识的学习和智能技术的体验，忽视了学生思维能力的培养。文章基于输入—过程—结果（IPO）游戏化学习模型，将人工智能课程内容、游戏元素与计算思维实践要素有效融合，以设计“挑战Alpha井字棋”游戏为例，开展游戏化学习，促进高中生计算思维实践的培养。通过分析测试题、游戏设计任务、调查问卷和游戏作品探究241名高中生计算思维实践、人工智能学科知识、情感态度等方面的提升效果。研究结果表明，人工智能课程中开展游戏化学习能够显著提高高中生计算思维实践整体水平和分解、抽象、模式识别、算法、调试五个要素水平，促进人工智能学科知识，增强学习兴趣、动机、自信心，降低认知负荷。该游戏化学习设计框架适用于人工智能课程中培养学生的计算思维实践，通过运用游戏元素发展和创新人工智能课程的教与学方法。
【英文摘要】：
Promoting artificial intelligence education in primary and secondary schools to cultivate students' computational thinking has become a common goal and requirement of education policies and curriculum standards at home and abroad. At present, the artificial intelligence courses in primary and secondary schools mainly focuses on the learning of theoretical knowledge and the experience of intelligent technology, ignoring the cultivation of students' thinking skills. Based on the Input-Process-Outcome (IPO) gamification learning model, this paper effectively integrates the content of artificial intelligence courses, game elements and practical elements of computational thinking, and takes the game "Challenge Alpha Tic Tac Toe" as an example to carry out gamification learning to promote the cultivation of computational thinking practice in high school students. Through the analysis of tests, game design tasks, questionnaires and game works, this paper investigates the effects of gamification learning on 241 high school students' computational thinking practice, artificial intelligence subject knowledge, and emotional attitudes. The results show that gamification learning in artificial intelligence courses can significantly improve high school students' overall level of computational thinking practices and five elements of decomposition, abstraction, pattern recognition, algorithm and debugging, promote students' artificial intelligence subject knowledge, and enhance students' learning interest, motivation, self-confidence, and reduce cognitive load. This gamification learning design framework is applicable to develop students' computational thinking practice in artificial intelligence courses, and develop and innovate teaching and learning methods in artificial intelligence courses by using game elements.