Measuring Student Self-Efficacy and Learning Trajectories For K-5 CS: ICER 2017 Trip Report

The 2017 International Computing Education Research (ICER) conference was held August 18-20 at the University of Washington in Tacoma. You can find the conference schedule here, and all the proceedings in the ACM Digital Library here. With 150 attendees (50 PhD students, and 88 first-timers), it was the largest ICER ever. You can read the play-by-play by searching for the hashtag #ICER2017 on Twitter.

ICER has two paper awards: the Chair’s Award and the John Henry Award. The Chair’s Award is awarded by the conference chairs based on reviewer scores. The John Henry Award is the “people’s choice” award, based on attendees’ votes for attempting a task that may seem impossible, but pushes “the upper limits of our pedagogy.”

The Chair’s Award this year when to Holger Danielsiek, Laura Toma, and Jan Vahrenhold, An Instrument to Assess Self-Efficacy in Introductory Algorithms Courses (see paper link here). Self-efficacy describes a person’s belief in their ability to succeed in specific situations or to accomplish specific tasks. We know the self-efficacy plays an important role in introductory computing. (In fact, the ICER 2016 Chair’s Award studied the impact of introductory course grades on self-efficacy, as described here.) Algorithms courses appear later in the curriculum, and so are a different set of situations and tasks. Since self-efficacy is an important factor in student retention, knowing how to measure self-efficacy is useful in understanding whether students will persist in their study of computing. Danielsiek and team developed an instrument and validated in three US institutions and one German institution. The computing education research community is still young (e.g., this was only the 12th ICER conference), so having carefully validated instruments is important for the research community.

The John Henry Award went to one of my favorite talks at the conference, K-8 Learning Trajectories Derived from Research Literature: Sequence, Repetition, Conditionals from Kathryn M. Rich, Carla Strickland, T. Andrew Binkowski, Cheryl Moran, and Diana Franklin. There are many efforts to define what elementary school students should learn about computer sciences, but almost none of them start from empirical data. What can we expect most elementary school students to do with programming? What concepts should students be able to learn first, and what comes later?

 

Katie Rich presenting the Conditionals trajectory

Rich’s team started from a corpus of 160 papers, and used those to identify what students could actually do — what evidence we had that students in elementary school could learn those computing concepts. They then constructed sequences of those concepts, drawing on either the mathematics literature (which has studied the trajectories of what elementary school students learn for many years) or their own contexts of working with elementary school students. The result are trajectories of concepts for sequencing, iteration, and conditionals, with more in progress. From these trajectories, they identify big ideas like ordering and precision for sequencing, and the binary nature of conditionals. This paper will be a significant resource for efforts to define curriculum standards around the world.

The Rich et al. trajectories paper is important for computing education research because it presents a set of testable hypotheses. The Advanced Placement “CS Principles” exam is built a set of big ideas, which start with “Creativity: Computing is a creative activity.” I don’t know how to test if that’s true that computing is a creative activity, and I don’t know how to test if a student has that big idea. The nodes, edges, and big ideas in the Rich et al. trajectories are testable. We might disagree with them, and they might even all be wrong. What is important is that each trajectory has an evidence-based rationalization, and further empirical testing can improve the trajectory. It’s a real theory that we can build on.

ICER continues to be a top conference for exploring how students come to understand computing and how to improve computing. These two paper awards show the breadth of research at ICER, from elementary school students (K-8) through undergraduates studying algorithms. Other research presented at ICER studied bootcamps, hackathons, and how teachers learn computing. These studies help us to understand and improve how students learn critical 21st century skills.