作者：Zhu, G., Teo, C. L., Ong, A. K.-K., Yuan, K. G., Ker, C. L., & Yang, Y.* (杨玉芹)

出版刊物：Education and Information Technologies

出版时间：2025年

内容摘要：

Preparing the new generation to be data-literate citizens is a pressing challenge, and some explorations have been made to cultivate K-12 students’ data science skills and attitudes. However, there is a lack of instructional models to guide the design of data science programs in K-12 due to its complex and interdisciplinary nature as well as the involvement of diverse communities in its research targeting various audiences. To address this research gap, we proposed an authentic collaborative inquiry model (SPIRE, Stimulate, Practice, Improve and Reflect) that integrates science inquiry procedures (emphasizing students’ hands-on engagement in data science workflow) and the Knowledge Building approach (highlighting students’ continuous and collaborative work on real-world problems and questions). Following the mode, we developed and engaged 67 secondary school students in an out-of-school Data Science program through two cycles. We examined how students’ data science skills, perceived learning and attitudes changed during and after the program. The findings show that the groups of participants could engage in complete data science processes, demonstrating strong skills in identifying variables, aligning data with investigative questions, and interpreting results in their final artifacts. However, they performed relatively poorly in explaining the rationale of the investigation, comprehensive data analysis and considering other factors beyond those included in the investigative questions. Participants perceived learning significantly increased over the inquiry phases, and their perceived data science skills significantly increased after the program. Overall, the results were positive and uncovered skills requiring more support and scaffolding. Future research and practice can further examine how to apply the SPIRE model in K-12 data science education in schools in subjects such as data science, science, and mathematics and study how to enhance the data science skills that students do not perform well.

培养具备数据素养的未来公民是一项紧迫挑战，已有探索尝试在K-12阶段培育学生的数据科学技能与态度。然而，由于数据科学具有跨学科复杂性且涉及多元参与群体，当前仍缺乏指导K-12数据科学课程设计的系统性教学模式。为此，本研究提出整合科学探究流程与知识建构理念的真实性协作探究模型，并据此开展为期两轮的校外数据科学项目，组织67名中学生参与实践。研究通过考察项目过程中及结束后学生在数据科学技能、学习感知与态度方面的变化发现：参与者能够完整参与数据科学实践流程，在最终成果中展现出较强的变量识别、数据与问题匹配及结果解读能力；但在阐释研究设计原理、开展综合性数据分析以及考量研究问题外其他影响因素方面表现相对不足。项目期间，学生的学习感知随探究阶段推进显著提升，项目结束后其数据科学技能自评也呈现显著增长。总体而言，研究结果验证了该模型的有效性，同时揭示了需要进一步提供支持的技能维度。未来研究与实践可深入探索如何将该模型应用于学校数据科学、科学、数学等学科教学，并针对学生的薄弱技能维度设计增强型教学支架。