The LTEC project has a new paper out in the SIGCSE 2019 proceedings, authored by myself and my colleagues Carla Strickland, Andrew Binkowski, and Diana Franklin. It’s a new addition to our series of SIGCSE and ICER papers that detail learning trajectories that we developed through review of CS education literature. This time, the trajectory is about debugging (Rich, Strickland, Binkowski, & Franklin, 2019).
(If it’s helpful, you can read my description of what a learning trajectory is here.)
Although the overall approach we used to develop all of our trajectories was basically the same, we’ve tried to make a unique contribution in each publication by making particular parts of our process transparent through each paper. In our paper from SIGCSE 2017, we talked about the overall literature review and what we noticed as we examined the learning goals embedded in the pieces we read. In our paper from ICER 2017, we shared how we adapted our overall process from other work in mathematics education and focused on our synthesis of learning goals into consensus goals. In our paper from ICER 2018, we focused on one trajectory to give us room to discuss every decision we made in ordering the consensus goals.
This time, in addition to sharing a new trajectory, we also highlighted how we used the theoretical construct of dimensions of practice (Schwarz et al., 2009) to help us organize our consensus goals. We’re also really excited to be able to share more about the role that our learning trajectories played in the curriculum development we’ve been working on for two years now. We’re dedicating a significant piece of our presentation at SIGCSE to sharing an activity we are really proud of and how the trajectory shaped its development.
If you’ll be at SIGCSE, we hope you’ll come and check us out on Friday at 2:10 in Millennium: Grand North! (If you don’t come for me, come for Carla! She’s a great speaker whose PD facilitation is famous on Twitter.)
If not, please check out the paper if you are interested. Right now, the link above and the one on my CV page take you to the normal ACM digital library page. I’ll be switching the link in my CV to a paywall free version as soon as the Author-izer tool links this paper to my author page. At that time, we’ll also be sure to add a paywall-free link to the LTEC project page.
Although we have one more learning trajectory (on variables) that has been developed but not yet published, I suspect this might be the last conference paper from this work that I first author. The project is continuing to do wonderful work and you’ll be hearing more from us, but I’m into the thick of graduate school and not nearly as involved in the work any more. So, I just want to say that working with my colleagues at UChicago STEM Education on this line of work has been among my proudest and most gratifying professional experiences. I want to thank all of my collaborators, and also say a particular thank you to Andy Isaacs and George Reese, as without their graciousness I never would have had the opportunity to co-PI the project.
I’d also like to say thanks to all the folks in the CS education community who have been so receptive of our work and offered us such wonderful and helpful feedback. We’re particularly gratified for the shoutout that Mark Guzdial is giving us in his SIGCSE keynote this year.
From the bottom of my heart, thanks to all of you for making this longtime math educator who wandered into the CS education space feel welcome and like her contributions are worthwhile.
Rich, K. M., Strickland, C., Binkowski, T. A., & Franklin, D. (2019). A K – 8 debugging learning trajectory derived from research literature. In Proceedings of the 2019 ACM SIGCSE Technical Symposium on Computer Science Education (pp. 745–751). New York: ACM.
Schwarz, C. V., Reiser, B. J., Davis, E. A., Kenyon, L., Acher, A., Fortus, D., Shwartz, Y., Hug, B., and Krajcik, J. (2009). Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching, 46(6), 632–645.