Beyond Black-Boxing: Building Intuitions of Complex Machine Learning Ideas Through Interactives and Levels of Abstraction
Beyond Black-Boxing: Building Intuitions of Complex Machine Learning Ideas Through Interactives and Levels of Abstraction
复制标题
超越黑盒：通过交互和抽象层次建立复杂机器学习思想的直觉
DOI：
10.1145/3501709.3544273
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Babb, Derek
中科院分区：
文献类型：
--
作者：
Broll, Brian;Grover, Shuchi;Babb, Derek
现有的人工智能和机器学习教学方法通常侧重于使用预先训练的模型或微调现有的黑盒架构。我们相信，在适当的支持下，高中早期学生可以学习高级机器学习主题，例如优化和对抗性示例。我们的方法侧重于让学生对这些复杂的概念产生深刻的直觉，首先通过交互式工具、预编程的游戏和精心设计的编程活动让新手能够理解这些概念。然后，学生能够通过有意义的实践经验来理解这些概念，这些经验涵盖从数据收集到模型优化和检查的整个机器学习过程。
Existing approaches to teaching artificial intelligence and machine learning often focus on the use of pre-trained models or fine-tuning an existing black-box architecture. We believe advanced ML topics, such as optimization and adversarial examples, can be learned by early high school age students given appropriate support. Our approach focuses on enabling students to develop deep intuition about these complex concepts by first making them accessible to novices through interactive tools, pre-programmed games, and carefully designed programming activities. Then, students are able to engage with the concepts via meaningful, hands-on experiences that span the entire ML process from data collection to model optimization and inspection.
将建构主义学习理论与计算思维课堂活动相结合
DOI：
10.15388/infedu.2019.03
发表时间：
2019
期刊：
Informatics Educ.
影响因子：
--
作者：
A. Csizmadia;Bernhard Standl;Jane Waite
通讯作者：
Jane Waite