Towards an authentic collaborative inquiry model for cultivating data science skills and attitudes: effects of SPIRE on secondary school students

• Published in: Education and information technologies Vol. 30; no. 14; pp. 19933 - 19959
• Main Authors: Zhu, Gaoxia, Teo, Chew Lee, Ong, Aloysius Kian-Keong, Yuan, Katherine Guangji, Ker, Chin Lee, Yang, Yuqin
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2025; Springer Nature B.V
• Subjects: Active Learning; Attitudes; Collaboration; Computer Appl. in Social and Behavioral Sciences; Computer Science; Computer Science Education; Computers and Education; Core curriculum; Curriculum Development; Data Analysis; Data science; Data visualization; Decision Making; Design; Discovery Processes; Education; Educational Opportunities; Educational Research; Educational Technology; Elementary Secondary Education; Enrichment Activities; Inferences; Information Systems Applications (incl.Internet); Inquiry; Instructional Materials; Interdisciplinary aspects; Introductory Courses; Knowledge; Learner Engagement; Learning; Literature Reviews; Mathematics Education; Middle schools; Prior Learning; Program Implementation; Programming; Science and Society; Science Curriculum; Science education; Science Instruction; Science programs; Secondary school students; Secondary schools; Society; Swimming; Teachers; Teaching; User Interfaces and Human Computer Interaction; Data science; K-12; Collaborative science inquiry; Knowledge building; Out-of-school program
• ISSN: 1360-2357; 1573-7608
• DOI: 10.1007/s10639-025-13579-5

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, S timulate, P ractice, I mprove and Re flect) 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.