Existing novice-friendly machine learning (ML) modeling tools center around a solo user experience, where a single user collects only their own data to build a model. However, solo modeling experiences limit valuable opportunities for encountering alternative ideas and approaches that can arise when learners work together; consequently, it often precludes encountering critical issues in ML around data representation and diversity that can surface when different perspectives are manifested in a group-constructed data set. To address this issue, we created Co-ML -- a tablet-based app for learners to collaboratively build ML image classifiers through an end-to-end, iterative model-building process. In this paper, we illustrate the feasibility and potential richness of collaborative modeling by presenting an in-depth case study of a family (two children 11 and 14-years-old working with their parents) using Co-ML in a facilitated introductory ML activity at home. We share the Co-ML system design and contribute a discussion of how using Co-ML in a collaborative activity enabled beginners to collectively engage with dataset design considerations underrepresented in prior work such as data diversity, class imbalance, and data quality. We discuss how a distributed collaborative process, in which individuals can take on different model-building responsibilities, provides a rich context for children and adults to learn ML dataset design.

翻译：现有的面向初学者的机器学习建模工具以单人用户为基础构建，用户仅收集自身数据来建立模型。然而，单人建模体验限制了学习者通过协作接触多元化观点与方法的机会，进而导致难以理解群体构建数据集中因视角差异而产生的数据表征多样性等机器学习的核心问题。为此，我们开发了Co-ML——一款基于平板电脑的应用程序，旨在通过端到端迭代建模流程支持学习者协作构建机器学习图像分类器。本文通过一个家庭（两名11岁与14岁儿童及其父母）在家庭场景中利用Co-ML开展引导式机器学习入门的深度案例研究，论证了协作建模的可行性与潜在价值。我们展示了Co-ML系统设计，并探讨了协作活动如何使初学者共同关注数据集设计中的关键问题——包括数据多样性、类别不平衡及数据质量——这些议题在既往研究中尚未得到充分重视。最后，本文论述了分布式协作过程（个体可承担不同建模职责）为儿童与成人学习机器学习数据集设计提供的丰富教育情境。