High school students’ data modeling practices and processes: From modeling unstructured data to evaluating automated decisions
High school students’ data modeling practices and processes: From modeling unstructured data to evaluating automated decisions
复制标题
高中生数据建模实践和流程：从非结构化数据建模到评估自动化决策
DOI：
10.1080/17439884.2023.2189735
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Chao, Jie
中科院分区：
文献类型：
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作者：
Jiang, Shiyan;Tang, Hengtao;Tatar, Cansu;Rosé, Carolyn P.;Chao, Jie
作为第一代在人工智能环境中成长起来的高中生，培养他们的人工智能素养至关重要，他们必须了解数据驱动的人工智能技术的工作机制，并批判性地评估预测模型的自动决策。虽然已经努力通过开发机器学习模型来吸引年轻人了解人工智能，但很少有人对细微的学习过程有深入的了解。在本研究中，我们考察了高中生的数据建模实践和过程。28名学生开发了带有文本数据的机器学习模型，用于对冰淇淋店的负面和正面评论进行分类。我们确定了九个数据建模实践，描述了学生的模型探索、开发和测试过程，以及关于评估数据技术自动化决策的两个主题。研究结果为设计可访问的数据建模体验提供了启示，帮助学生理解数据正义，以及数据建模者在创建人工智能技术中的作用和责任。
It’s critical to foster artificial intelligence (AI) literacy for high school students, the first generation to grow up surrounded by AI, to understand working mechanism of data-driven AI technologies and critically evaluate automated decisions from predictive models. While efforts have been made to engage youth in understanding AI through developing machine learning models, few provided in-depth insights into the nuanced learning processes. In this study, we examined high school students’ data modeling practices and processes. Twenty-eight students developed machine learning models with text data for classifying negative and positive reviews of ice cream stores. We identified nine data modeling practices that describe students’ processes of model exploration, development, and testing and two themes about evaluating automated decisions from data technologies. The results provide implications for designing accessible data modeling experiences for students to understand data justice as well as the role and responsibility of data modelers in creating AI technologies.