Combinatorial proofs of binomial identities involve establishing an identity by arguing that each side enumerates a certain set of outcomes. In this paper, we share results from interviews with experienced provers (mathematicians and upper-division undergraduate mathematics students) and examine one particular aspect of combinatorial proof, namely the kinds of contexts that experienced provers used to establish combinatorial proofs of binomial identities. Our findings show that overall, our participants used a variety of contexts in their work; we also demonstrate ways in which previous experiences influenced the contexts they chose, and we offer some instances in which features of a context supported their combinatorial proof production. We offer some theoretical implications of our work, and we conclude with a discussion of limitations and avenues for future work.

Research has shown that solving counting problems correctly can be difficult for students at all levels, and mathematics educators have sought to identify strategies and interventions to help students reason conceptually about combinatorial tasks. A set-oriented perspective (Lockwood, 2014) is a way of thinking about counting problems that emphasizes the importance of reasoning about the set of outcomes being counted. From a set-oriented perspective, one possible type of intervention is to have students focus on the sets of outcomes rather than formulas and expressions, and specifically to reason about the structure of the set of outcomes. Yet, reasoning about sets of outcomes is not sufficient for students to make connections between outcomes and counting processes. In this paper, we investigate tasks where students wrote computer code to enumerate the set of outcomes in a specific order by implementing listing processes, and they were then asked to determine a specific numbered outcome in their list by using the structure of their enumeration scheme. We clarify particular aspects of a set-oriented perspective that were productive for students, and we demonstrate that tasks that asked students to name a specific outcome in their list elicited meaningful connections between counting processes and sets of outcomes. Further, such tasks reinforce desirable mathematical practices such as leveraging structure and connecting representations.

In this paper, I discuss undergraduate students' engagement in basic Python programming while solving combinatorial problems. Students solved tasks that were designed to involve programming, and they were encouraged to engage in activities of prediction and reflection. I provide data from two paired teaching experiments, and I outline how the task design and instructional interventions particularly supported students' combinatorial reasoning. I argue that emergent computational representations and the prompts for prediction and reflection were especially useful in supporting students' reasoning about fundamental combinatorial ideas. I argue that this particular mathematical example informs broader notions of disciplinary reflexivity and representational heterogeneity, providing insight into computational thinking practices in the domain of mathematics. Ultimately, I aim to explore the nature of computing and enumeration, shedding light on why the two disciplines are particularly well-suited to support each other. I conclude with implications and avenues for future research.

Combinatorial proof is an important topic both for combinatorics education and proof education researchers, but relatively little has been studied about the teaching and learning of combinatorial proof. In this paper, we focus on one specific phenomenon that emerged during interviews with mathematicians and students who were experienced provers as they discussed and engaged in combinatorial proof. In particular, participants used a wide variety of cognitive models to interpret multiplication by a constant when reasoning about binomial identities, some of which seemed to be more (or less) effective in helping produce a combinatorial proof. We present these cognitive models and describe episodes that illustrate implications of these cognitive models for our participants' work on proving binomial identities. Our findings both inform research on combinatorial proof and highlight the importance of understanding subtleties of the familiar operation of multiplication.

Computational thinking and activity are vital aspects of what it means to conduct scientific and mathematical work. In light of this, some propose that students' mathematical education should include an integration of computing into their mathematical experiences, giving students opportunities to engage with computational tools as they reason about mathematical concepts. In this commentary, we make a case that the international RUME community should focus on studying the integration of computing in research in undergraduate mathematics education. We situate this discussion within existing literature. Then, we suggest ways in which researchers can incorporate ideas related to computing, and we propose ideas for how investigations into computing might practically be incorporated into our already-existing research foci. Ultimately, we hope to motivate other members of the RUME community to join us in what we consider to be a timely and exciting endeavor.

Counting problems are difficult for students to solve, and there is a perennial need to investigate ways to help students solve counting problems successfully. One promising avenue for students' successful counting is for them to think judiciously about how they encode outcomes - that is, how they symbolize and represent the outcomes they are trying to count. We provide a detailed case study of two students as they encoded outcomes in their work on several related counting problems within a computational setting. We highlight the role that a computational environment may have played in this encoding activity. We illustrate ways in which by-hand work and computer programming worked together to facilitate the students' successful encoding activity. This case demonstrates ways in which the activity of computation seemed to interact with byhand work to facilitate sophisticated encoding of outcomes.

Combinatorics is an area of mathematics with accessible, rich problems and applications in a variety of fields. Combinatorial proof is an important topic within combinatorics that has received relatively little attention within the mathematics education community, and there is much to investigate about how students reason about and engage with combinatorial proof. In this paper, we use Harel and Sowder's (1998) proof schemes to investigate ways that students may characterize combinatorial proofs as different from other types of proof. We gave five upper-division mathematics students combinatorial-proof tasks and asked them to reflect on their activity and combinatorial proof more generally. We found that the students used several of Harel and Sowder's proof schemes to characterize combinatorial proof, and we discuss whether and how other proof schemes may emerge for students engaging in combinatorial proof. We conclude by discussing implications and avenues for future research.

When solving counting problems, students often struggle with determining what they are trying to count (and thus what problem type they are trying to solve and, ultimately, what formula appropriately applies). There is a need to explore potential interventions to deepen students' understanding of key distinctions between problem types and to differentiate meaningfully between such problems. In this paper, we investigate undergraduate students' understanding of sets of outcomes in the context of elementary Python computer programming. We show that four straightforward program conditional statements seemed to reinforce important conceptual understandings of four canonical combinatorial problem types. We also suggest that the findings in this paper represent one example of a way in which a computational setting may facilitate mathematical learning.