Background: Context personalization, the practice of adapting learning materials to students’ personal interests, has been shown to increase student learning and engagement. Within computer science education, research has found that LLMs can generate high-quality contextualized introductory programming exercises. Objective: In this paper, we evaluate the capability of LLMs to generate technically correct and thematically integrated contextualized algorithm design problems. Methods: In a series of three iterative studies, we use LLMs to generate contextualized algorithm design problems from a given base problem and theme, evaluating over 500 generated problems for technical and thematic alignment. Results: We find that LLM-generated algorithm design problems exhibit significantly more issues than prior work has found for introductory programming problems. We identify issues specific to the algorithm design context and then mitigate these issues with prompt engineering techniques and model choice. With these adjustments, we produce LLM-generated contextualized algorithm design problems that are technically strong, deeply themed, and largely realistic, though realism drops with more culturally and locally specific themes. Implications: We demonstrate a viable workflow for generating contextualized algorithm design problems using LLMs, including prompt design, model selection, and identification of specific issues to review for.

Teaching assistants (TAs) are essential to support growing Computer Science (CS) programs. In our university's TA training program, we taught 53 CS TAs a framework for how to develop solutions that focus on helping students with the problem-solving process. The framework provides TAs with tools to create a solution narrative that finds common ground with students, explicitly points out how to get started, emphasizes the trial and error process of problem-solving, and suggests how to recover from errors. In this poster we share our framework and preliminary findings that participants positively rated the quality of the lesson content and their narratives prior to our lesson do not already include this content. We suggest steps for future work.

Using formal methods to evaluate software and hardware enhances system reliability, which is crucial for safety-critical applications such as airplanes and autonomous vehicles. Formal methods are mathematical modeling techniques that can be used to verify the safety of systems. The use of formal methods is limited in industry due to a shortage of trained engineers. Educators in formal methods often report that many students do not see the benefit of formal methods and perceive the involved math as not worth the effort for their future careers as software engineers. This study aims to understand the current state of student beliefs and how using a formal verification tool affects student motivation to learn about formal methods. We used an Expectancy Value Cost Lite survey to measure student motivation. Students completed this survey multiple times while designing algorithms to control vehicles in different scenarios, both with and without a formal verification tool. We found that students in an autonomy class are motivated to use formal methods. Although the findings are not statistically significant, we observed a slight increase in motivation after using the tool. Additionally, using a formal verification tool solely for modeling may contribute to increased motivation. These results suggest that incorporating tools into coursework may be a useful step in motivating more students to study formal methods and enter the workforce with these skills.

This Birds of a Feather (BoF) session serves to build a community of graduate student researchers in Computer Science Education (CSEd) at SIGCSE TS and beyond. Many graduate students lack a CSEd research community within their own institution. This BoF session will serve to provide a space for SIGCSE TS graduate students to build their community across institutions, both during and after the conference. During the session, we will discuss the successes and challenges that come with being a CSEd graduate student, including work/life balance, advisor-student relationships, and developing collaborations. Attendees will leave with an opportunity to connect with other CSEd graduate students beyond the conference through a dedicated CSEd graduate student Slack channel.

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.

Abstract We present the Verse library with the aim of making hybrid system verification more usable for multi-agent scenarios. In Verse, decision making agents move in a map and interact with each other through sensors. The decision logic for each agent is written in a subset of Python and the continuous dynamics is given by a black-box simulator. Multiple agents can be instantiated, and they can be ported to different maps for creating scenarios. Verse provides functions for simulating and verifying such scenarios using existing reachability analysis algorithms. We illustrate capabilities and use cases of the library with heterogeneous agents, incremental verification, different sensor models, and plug-n-play subroutines for post computations.

Suppose we aim to build a phylogeny for a set of taxa X using information from a collection of loci, where each locus offers information for only a fraction of the taxa. The question is whether the pattern of data availability, called a taxon coverage pattern, suffices to construct a reliable phylogeny. The problem can be expressed combinatorially as follows. We call a taxon coverage pattern decisive if for any binary phylogenetic tree$T$for X, the collection of phylogenetic trees obtained by restricting$T$to the subset of X covered by each locus uniquely determines$T$. We relate the problem of checking whether a taxon coverage pattern is decisive to a hypergraph coloring problem. Using this connection, we (1) show that checking decisiveness is co-NP complete; (2) obtain lower bounds on the amount of coverage needed to achieve decisiveness; (3) devise an exact algorithm for decisiveness; (4) develop problem reduction rules, and use them to obtain efficient algorithms for inputs with few loci; and (5) devise Boolean satisfiability and integer linear programming formulations that allow us to analyze real data sets. For data sets that are not decisive, we use these formulations to obtain decisive subsets of the data.