Modeling and Measuring High School Students’ Computational Thinking Practices in Science

• Published in: Journal of science education and technology Vol. 29; no. 1; pp. 137 - 161
• Main Authors: Arastoopour Irgens, Golnaz, Dabholkar, Sugat, Bain, Connor, Woods, Philip, Hall, Kevin, Swanson, Hillary, Horn, Michael, Wilensky, Uri
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.02.2020; Springer; Springer Nature B.V
• Subjects: Achievement Gains; Assessments; Biology; Computer applications; Computer Science Education; Core curriculum; Curricula; Discourse Analysis; Education; Educational Technology; Elementary Secondary Education; High School Students; Learning; Mathematical models; Measurement; Pretests Posttests; Science Education; Science Instruction; Sciences education; Scores; Secondary School Curriculum; Secondary School Science; Secondary schools; Software; STEM Education; Students; Technical education; Thinking Skills; Units of Study; Computational thinking; Learning analytics; Biology; Science learning; Assessment
• DOI: 10.1007/s10956-020-09811-1
• ISSN: 1059-0145

Despite STEM education communities recognizing the importance of integrating computational thinking (CT) into high school curricula, computation still remains a separate area of study in K-12 contexts. In addition, much of the research on CT has focused on creating generally agreed-upon definitions and curricula, but few studies have empirically tested assessments or used contemporary learning sciences methods to do so. In this paper, we outline the implementation of an assessment approach for a 10-day high school biology unit with computational thinking activities that examines student pre-post responses as well as responses to embedded assessments throughout the unit. Using pre-post scores, we identified students with both positive and negative gains and examined how each group’s CT practices developed as they engaged with the curricular unit. Our results show that (1) students exhibited science and computational learning gains after engaging with a science unit with computational models and (2) that the use of embedded assessments and discourse analytics tools reveals how students think differently with computational tools throughout the unit.