Search for: All records

Abstract Science educators are integrating more and more computational thinking (CT) activities into their curricula. Proponents of CT offer two motivations: familiarizing students with a realistic depiction of the computational nature of modern scientific practices and encouraging more students from underrepresented backgrounds to pursue careers in science, technology, engineering, and mathematics. However, some studies show that increasing exposure to computing may not necessarily translate to the hypothesized gains in participation by female students and students of color. Therefore, paying close attention to students' engagement in computationally intense science activities is important to finding more impactful ways to promote equitable science education. In this paper, we present an in‐depth analysis of the interactions among a small, racially diverse group of high school students during a chemistry unit with tightly integrated CT activities. We find a salient interaction between the students' engagement with the CT activities and their social identification with publicly recognizable categories such as “enjoys coding” or “finds computing boring.” We show that CT activities in science education can lead to numerous rich interactions that could, if leveraged correctly, allow educators to facilitate more inclusive science classrooms. However, we also show that such opportunities would be missed unless teachers are attentive to them. We discuss the implications of our findings on future work to integrate CT across science curricula and teacher education. 

There has been a growing interest in the use of computer-based models of scientific phenomena as part of classroom curricula, especially models that learners create for themselves. However, while studies show that constructing computational models of phenomena can serve as a powerful foundation for learning science, this approach has struggled to gain widespread adoption in classrooms because it not only requires teachers to learn sophisticated technological tools (such as computer programming), but it also requires precious instructional time to introduce these tools to students. Moreover, many core scientific topics such as the kinetic molecular theory, natural selection, and electricity are difficult to model even with novice-friendly environments. To address these limitations, we present a novel design approach called phenomenological programming that builds on students' intuitive understanding of real-world objects, patterns, and events to support the construction of agent-based computational models. We present preliminary case studies and discuss their implications for STEM content learning and the learnability and expressive power of phenomenological programming.