提出ICE-T教学框架，打破机器学习黑箱教学困局
ICE-T: A Multi-Faceted Concept for Teaching Machine Learning
- 构建多维度教学框架ICE-T，融合多种认知方式
- 批判现有工具仅展示结果，缺乏对数据与算法的深层理解
- 适合课程设计者与教育平台开发者提升ML教学效果
人工智能与机器学习正逐步进入教育体系。为帮助学生理解，现有平台、可视化工具与数字游戏已广泛用于引入机器学习概念。本文分析计算机科学教学原则，制定评估标准，并据此评价多个主流教学工具。我们指出当前普遍将机器学习视为黑箱的问题，导致对数据、算法与模型的理解缺失。为此，提出融合多模态迁移、计算思维与解释性思维的ICE-T教学框架，作为现有教学原则的扩展。该多维概念旨在帮助课程设计者、平台开发者与教育工作者优化机器学习教学。
原文摘要 · Abstract (English)
The topics of Artificial intelligence (AI) and especially Machine Learning (ML) are increasingly making their way into educational curricula. To facilitate the access for students, a variety of platforms, visual tools, and digital games are already being used to introduce ML concepts and strengthen the understanding of how AI works. We take a look at didactic principles that are employed for teaching computer science, define criteria, and, based on those, evaluate a selection of prominent existing platforms, tools, and games. Additionally, we criticize the approach of portraying ML mostly as a black-box and the resulting missing focus on creating an understanding of data, algorithms, and models that come with it. To tackle this issue, we present a concept that covers intermodal transfer, computational and explanatory thinking, ICE-T, as an extension of known didactic principles. With our multi-faceted concept, we believe that planners of learning units, creators of learning platforms and educators can improve on teaching ML.
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