Abstract
Preservice science teachers are often asked to teach STEM content. While coding is one of the more popular aspects of the technology portion of STEM, many preservice science teachers are not prepared to authentically engage students in this content due to their lack of experience with coding. In an effort to remedy this situation, this article outlines an activity we developed to introduce preservice science teachers to computer science concepts such as pseudocode, looping, algorithms, conditional statements, problem decomposition, and debugging. The activity and discussion also support preservice teachers in developing pedagogical acumen for engaging K-12 students with computer science concepts. Examples of preservice science teachers’ work illustrate their engagement and struggles with the ideas and anecdotes provide insight into how the preservice science teachers practiced teaching computer science concepts with 6th grade science students. Explicit connections to the Next Generation Science Standards are made to illustrate how computer science lessons within a STEM course might be used to meet Engineering, Technology, and Application of Science standards within the NGSS.
Innovations Journal articles, beyond each issue's featured article, are included with ASTE membership. If your membership is current please login at the upper right.
References
American Association for the Advancement of Science. (2000). Project 2061, Science for all Americans.
Ballen, S. (2007, December 26). Whac-a-mole [Video file]. Retrieved from https://www.youtube.com/watch?v=K-jaOfIHGko
Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. Paper presented at the American Educational Research Association. Canada: British Columbia.
Kafai, Y. B., & Burke, Q. (2013). Computer programming goes back to school. Phi Delta Kappan, 95(1), 61.
Kotsopoulos, D., Floyd, L., Khan, S., Namukasa, I. K., Somanath, S., Weber, J., & Yiu, C. (2017). A pedagogical framework for computational thinking. Digital Experiences in Mathematics Education, 3, 154-171.
Kruse, J., Edgerly, H., Easter, J., & Wilcox, J. (2017). Myths about the nature of technology and engineering. The Science Teacher, 84(5), 39.
Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior, 41, 51–61.
National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. National Academies Press.
Papert, S., & Harel, I. (1991). Constructionism: Ablex publishing corporation.
Roggio, B. (2014, Dec 27). Pseudocode examples. Retrieved from https://www.unf.edu/~broggio/cop2221/2221pseu.htm
Rubinstein, A., & Chor, B. (2014). Computational thinking in life science education. PLoS computational biology, 10(11), e1003897.
Vygotsky, L. S. (1978). Mind in society. Cambridge: Harvard University Press.
Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127-147.
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.
Yadav, A., Hong, H., & Stephenson, C. (2016). Computational thinking for all: pedagogical approaches to embedding 21st century problem solving in K-12 classrooms. TechTrends, 60, 565-568.