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.

Algorithms courses are a foundational part of an undergraduate computer science degree that require abstract thinking and creativity and are known to be challenging for many students. Recently researchers have been developing auto-graded tools to scaffold students through the problem-solving process. We examine student's perceptions of such a tool in a required upper-division Algorithms course at a R1 University. The goal of the tool is to improve student experience in three ways: (1) help students break down the problem-solving process into clear steps; (2) increase students' self-efficacy by raising their confidence and understanding of the material; (3) have low ''cost'', by being easy to use, enjoyable, and a good use of students' time. The tool itself is designed to provide these benefits to students at every level of mastery through instantaneous feedback over increasingly challenging problems. It is designed as an addition to and not complete replacement of the written homework in the course. Based on a survey of almost 1000 students across four semesters, each with a different instructor, we examine whether student feedback is favorable over all four offerings, and for groups of students with different course outcomes. Using qualitative and quantitative methods, we found that across each of the four semesters and across letter grades A, B, C, and D students favored the tool as compared to written homework.

Manually grading free response questions remains a persistent challenge in education. While such questions offer valuable opportunities for student learning and critical thinking, their evaluation often requires substantial time and effort from instructors or teaching assistants. In addition to the grading workload, open-ended responses are susceptible to inconsistencies in scoring and may reflect unclear expectations, both of which can undermine the effectiveness and fairness of the assessment process. To address these challenges, we employed an AI-based grading system integrated in PrairieLearn to automatically evaluate student submissions to free response questions using a predefined set of rubric items. This approach not only streamlines the grading process but also enables direct comparison between AI-generated rubric applications and human judgments, providing insight into alignment and potential discrepancies. These discrepancies provided valuable insight, allowing us to iteratively revise and clarify the rubric items. Our experiences with using the AI grading system across several computing courses suggest that even experienced educators face difficulties articulating rubrics that are both specific and interpretable. We furthermore argue that more attention should be given to the iterative development and evaluation of rubrics.

In this paper, we describe a CS+Social Science Python project that can be integrated directly into high school classrooms, enabling students to explore social science questions using computer science. The project uses the pandas library and Google Colab to give students an authentic experience with data science tools. We present teachers' experiences and students feedback from implementing the project in three high school classes, one non-STEM class and two AP CS classes. The project is designed to be simple enough for students with no CS background to succeed, but creative and open-ended enough to allow students with experience to develop their skills further. Students from both courses report the project was interesting and useful. Our work builds upon the body of literature examining ways to include CS in non-STEM high school courses, but also appears to fit well into CS curricula.

Background: Proof Blocks is a software tool that enables students to construct proofs by assembling prewritten lines and gives them automated feedback. Prior work on learning gains from Proof Blocks has focused on comparing learning gains from Proof Blocks against other learning activities such as writing proofs or reading.

Broadening participation in computing (BPC) is a focus in industry and academia. Extant research focuses on what broadening participation in computing (BPC) efforts are pursued, while we propose focusing on how change happens. Our qualitative study applied John Kotter's (2012) eight-stage change framework to analyze interviews with faculty and staff engaged in BPC efforts. Illustrative examples from our interviews elucidate each of the eight stages and how they can be applied to pursue organizational change efforts that support BPC.

Racial and gender representation among computer science (CS) students continues to lag behind national demographics in the U.S. One way to improve students' interests in CS is to connect CS to other fields to expand students' perceptions of what constitutes CS. While CS+X programs, which combine CS and another field into a single interdisciplinary degree, are expanding at the undergraduate level, there is room to further expand related opportunities in K-12 spaces to encourage more students to pursue CS. To this end, in this experience report we present a new CS+X topics course for high school students that teaches about the intersections of CS with several non-STEM "+X" fields. The course was designed by a team of educators with experience in K-12 curriculum design and broadening participation programs. We piloted the course at a high school in Spring 2023 with 11 students. We present our course design and breakdown of decisions made during the course design process. Further, we provide results from our evaluation survey, featuring thematic analysis of students' commentary and a breakdown of course topics and components students favored. Our students reported that their interests in computing and understanding of computing's broad impacts on society improved. We provide a reflection on the course's future refinements and our plans for further testing of the course in more high school environments to prepare it for wider community adoption.

Algorithm design is a vital skill developed in most undergraduate Computer Science (CS) programs, but few research studies focus on pedagogy related to Algorithms coursework. To understand the work that has been done in the area, we present a systematic survey and characterization of existing studies in the CS Education literature related to the teaching of algorithm design at the undergraduate level. Across all papers in the ACM Digital Library, we only find 97 applicable papers. We classify these papers by topic, evaluation metric, evaluation methods, and intervention target. We present the results of these classifications alongside insights about existing knowledge, rigor, and contribution rates. We hope that this work not only provides a detailed representation of the current corpus of CS Education work related to algorithm design but also demonstrates that the body of knowledge is sparse and supports further research in the area. For future work, we intend to investigate and synthesize the conclusions reached by these papers.

Background : Proof Blocks is a software tool that enables students to construct proofs by assembling prewritten lines and gives them automated feedback. Prior research has shown that students learn as much from an activity where they use Proof Blocks as where they write proofs. However, in both cases students first read a book chapter. Prior research was not able to differentiate between the learning gains achieved from reading versus proof practice. Purpose : This study aims to measure learning gains from reading a book chapter versus completing Proof Blocks. Methods : We conducted a randomized controlled trial with three experimental groups: one that only read a book chapter, one that only completed Proof Blocks, and one that did both. Findings : The group that completed only Proof Blocks had the smallest learning gains. The group that read the book chapter and completed the Proof Blocks activity performed marginally better than students who only read the book chapter, but it is not clear if the source of this improvement was the Proof Blocks or just exposure to more examples.

Proof Blocks is a software tool that provides students with a scaffolded proof-writing experience, allowing them to drag and drop prewritten proof lines into the correct order instead of starting from scratch. In this paper we describe a randomized controlled trial designed to measure the learning gains of using Proof Blocks for students learning proof by induction. The study participants were 332 students recruited after completing the first month of their discrete mathematics course. Students in the study took a pretest and read lecture notes on proof by induction, completed a brief (less than 1 hour) learning activity, and then returned one week later to complete the posttest. Depending on the experimental condition that each student was assigned to, they either completed only Proof Blocks problems, completed some Proof Blocks problems and some written proofs, or completed only written proofs for their learning activity. We find that students in the early phases of learning about proof by induction are able to learn just as much from reading lecture notes and using Proof Blocks as by reading lecture notes and writing proofs from scratch, but in far less time on task. This finding complements previous findings that Proof Blocks are useful exam questions and are viewed positively by students.

A central focus of modern cryptography is the construction of efficient, high-level cryptographic tools (e.g., encryption schemes) from weaker, low-level cryptographic primitives (e.g., one-way functions). Of interest are both the existence of such constructions and their efficiency. Here, we show essentially tight lower bounds on the best possible efficiency of any black-box construction of some fundamental cryptographic tools from the most basic and widely used cryptographic primitives. Our results hold in an extension of the model introduced by Impagliazzo and Rudich and improve and extend earlier results of Kim, Simon, and Tetali. We focus on constructions of pseudorandom generators, universal one-way hash functions, and digital signatures based on one-way permutations, as well as constructions of public- and private-key encryption schemes based on trapdoor permutations. In each case, we show that any black-box construction beating our efficiency bound would yield the unconditional existence of a one-way function and thus, in particular, prove $P \neq NP$.

A central focus of modern cryptography is to investigate the weakest possible assumptions under which various cryptographic algorithms exist. Typically, a proof that a "weak" primitive (e.g., a one-way function) implies the existence of a "strong" algorithm (e.g., a private-key encryption scheme) proceeds by giving an explicit construction of the latter from the former. In addition to showing the existence of such a construction, an equally important research direction is to explore the efficiency of such constructions.Among the most fundamental cryptographic algorithms are digital signature schemes and schemes for public- or private-key encryption. Here, we show the first lower bounds on the efficiency of any encryption or signature construction based on black-box access to one-way or trapdoor one-way permutations. If S is the assumed security of the permutation π (i.e., no adversary of size S can invert π on a fraction larger than 1/S of its inputs), our results show that:

We prove that, somewhat surprisingly, there is no black-box reduction of (poly-to-one) trapdoor functions to trapdoor predicates (equivalently, to public-key encryption schemes). Our proof follows the methodology that was introduced by R. Impagliazzo and S. Rudich (1989), although we use a new, weaker model of separation.

In this paper we study the relationships among some of the most fundamental primitives and protocols in cryptography: public-key encryption (i.e. trapdoor predicates), oblivious transfer (which is equivalent to general secure multi-party computation), key agreement and trapdoor permutations. Our main results show that public-key encryption and oblivious transfer are incomparable under black-box reductions. These separations are tightly matched by our positive results where a restricted (strong) version of one primitive does imply the other primitive. We also show separations between oblivious transfer and key agreement. Finally, we conclude that neither oblivious transfer nor trapdoor predicates imply trapdoor permutations. Our techniques for showing negative results follow the oracle separations of R. Impagliazzo and S. Rudich (1989).

Abotract'An (;)-OT protocol (also denoted "all or nothing discloxwc of secrets") allows Bob to secretly choose one of n occret bits held hy Alice, in a way that at the end of the protocol Bob learnn only a oinglo bit of his choice, and Alice learns nothing about Bob% choice.