Jeff Erickson

Graph data structures and algorithms play an essential role in computer science, and one of the ultimate goals of learning graphs is to solve more complicated algorithm design problems with them. A common way to solve a novel, complex problem is to reduce the problem to a standard graph problem, which often requires modeling a graph, and one essential way to model a graph is a technique called graph layering. Graph layering is often considered difficult by students and rarely studied by computer science education researchers despite its significance in algorithm design. To understand students' struggles with graph layering and improve teaching of algorithm designs, we conducted this qualitative study using think-aloud interviews with current students from an algorithm course. Participants were asked to solve algorithm design problems meant to be solved with graph layering. We used thematic analysis to extract difficulties observed in these interviews. We share our preliminary findings in this poster, and propose next steps for this study and future research.

Theory of Computing (ToC) is an important aspect of nearly every undergraduate CS curriculum, as it concerns what computation fundamentally means. However, there has been little research into ToC pedagogy, both within the classroom and how it fits within its institutional context. We propose in this working group to create a survey of current ToC pedagogy. Our goals are to create a standard for teaching ToC, find trends, determine under-researched areas, and to build a community among ToC educators.

Deterministic and nondeterministic finite automata (DFAs and NFAs) are abstract models of computation commonly taught in introductory computing theory courses. These models have important applications (such as fast regular expression matching), and are used to introduce formal language theory. Undergraduate students often struggle with understanding these models at first, due to the level of abstraction. As a result, various pedagogical tools have been developed to allow students to practice with these models. We introduce the FSM Builder, a new pedagogical tool enabling students to practice constructing DFAs and NFAs with a graphical editor, giving personalized feedback and partial credit. The algorithms used for generating these are heavily inspired by previous works. The key advantages to its competitors are greater flexibility and scalability. This is because the FSM Builder is implemented using efficient algorithms from an open source package, allowing for easy extension and question creation. We discuss the implementation of the tool, how it stands out from previous tools, and takeaways from experiences of using the tool in multiple large courses. Survey results indicate the interface and feedback provided by the tool were useful to students.