The demand for cybersecurity professionals with advanced static analysis expertise has grown exponentially, driven by the increasing sophistication of malware, advanced persistent threats, and nation-state cyber attacks. Our project provides a design and implementation of an innovative static analysis course that integrates large language models (LLMs) to enhance student learning and engagement. Our approach leverages LLMs as intelligent assistants within a Capture The Flag (CTF) framework, enabling students to collaborate with LLMs to solve complex binary analysis tasks. We present our course structure, AI facilitation process, and evaluation results, highlighting how LLM integration impacts students' understanding of reverse engineering and symbolic execution concepts, how students adapt their learning strategies when working with LLMs, and cons and pros of LLMs in reverse engineering. This report provides valuable insights for educators seeking to incorporate LLMs technologies into cybersecurity curricula, addressing both technical and motivational challenges in static analysis education.

Peer code review, which involves programmers examining and evaluating one other's code for quality, has been adopted in Computer Science (CS) classrooms to provide authentic learning experiences, support collaboration, develop programming skills, and facilitate peer learning. Although a body of research has explored the effectiveness of peer code review as learning activities, limited work focused on how the role of reviewer/reviewee impacts students' learning during the peer code review process. This work shares preliminary findings from the implementation of peer code review in a senior CS course. The instructor designed a double-blind peer code review activity in which students first individually review an assigned submission by completing a code review checklist and then, engage in group discussion. Through surveys, we examined students' perceptions of learning through peer code review. We found that students highly valued the opportunity to review others' solutions, thought it somewhat challenging to provide quality feedback, and had mixed opinions about being reviewed.

Computer Science (CS) professional development has increased opportunities to broaden K12 teachers' and students' exposure to CS learning. However, many Indigenous-serving teacher professional development (PD) participants could not facilitate in-classroom implementation without significant follow-up and support. This study aims to understand how the factors emerging from our CS PD affected teachers' PD transition to in-classroom implementation. We analyzed multiple data sources collected over three years of our project. We use logistic regression to explore the data from the PD course. Through analysis, our study indicates that providing teachers with targeted mentorship and ensuring the completion of detailed lesson plans are two factors in the transition of teachers' learning from PD to teachers' implementations in the classroom.

Native Americans (NA) have historically been the most underrep-resented population when it comes to participating in STEM and computing careers. The Navajo are one of the country's largest NA groups, and understanding the barriers and developing solutions to increase their participation will have far-reaching consequences on informing the research and practice on how computing can be taught at NA-serving high schools. The paper describes the experience gained over three years of working in this region via project Let's Talk Code, which aims to help math, science, and art teachers from Navajo high schools develop CS-based projects in their existing courses and provide mentorship and guidance. Let's Talk Code is constructed as a research-practice partnership (RPP) where the teachers (practitioners) work with a multi-institutional team of researchers and CS educators to improve the capacity-building needs of its partners (high schools). The paper details the evolution of the project over the years and highlights challenges, barriers, and strategies that were used to impact a significant number of teachers throughout the project.

Educators should provide access to all students with inclusive and equitable computer science (CS) and computational thinking (CT) learning outcomes. Yet access to CS and CT is not always available for students with disabilities. This qualitative cross-case study examined the barriers and strengths three exemplar teachers faced, explored the supports and resources they provided, and presented how these teachers defined successful inclusive CS learning outcomes for their students. The data set included analysis of the semistructured teachers' interviews and teaching materials. The results included seven successful strategies the teachers identified: 1) using physical computing, 2) pair programming, 3)connecting CS and Individual Education Plans (IEP), 4)applying hands-on activities, 5) CT integration, 6) using CS vocabulary, and 7)open-ended pedagogy. Three resources and supports the teacher provided emerged from the data set as follows: 1) accessible instructional materials, 2) projects with multiple entry points, and 3) essential scaffolding supports. Whereas four barriers teachers faced; 1. subject matter, 2) accessible tools, 3) students receiving support, 3) the role of CS in instruction, and 4) the role of time served as additional findings. The results suggested that school-based practitioners, including administrators, can overcome the barriers and promote successful strategies that lead to asset-based CS-inclusion in the classroom.

Decomposition is a foundational computational thinking construct that is often introduced early as students are learning computer science in the elementary grades. Although decomposition is often described in early computational activities, little research exists about how to teach and assess students’ understanding of decomposition. In this mixed-methods research study, 173 third-grade students from eight elementary school classrooms in the Midwest were taught eight lessons that integrated decomposition as well as other computational thinking practices into their mathematics instruction. They completed a computational thinking assessment after the first four lessons and again after the second four lessons. Analyses included the distribution of correct decomposition item responses, confirmatory factor analysis, and item-level error analysis. Results indicate wide variability in students’ performance on the decomposition assessment items as well as in performance on items contextualized within mathematics. This study highlights the need for additional considerations about assessing computational understanding, implications for assessment within integrated contexts, and the use of paper-and-pencil tests compared to embedded assessments.

Variable is a common computer science (CS) concept and is being introduced to upper elementary students in computational thinking (CT)-integrated instruction. However, there is scant empirical evidence of when and how elementary students should learn variables. For example, national computer science (CS) standards advise introducing variables in grades 3-5 and a K-8 variable learning trajectory (LT) synthesized learning goals from the literature and hypothesized four levels of thinking in working with variables. Yet, little empirical research lies behind these. This mixed methods study examined elementary students' understanding of variables. Participants were sampled from two fourth-grade classes from a Midwestern elementary school that implemented a series of CT-integrated math lessons. Students' written responses to variables assessment items were analyzed. Additionally, cognitive think-aloud interviews were conducted with nine students to elicit students' understanding while solving the variables assessment items. Our findings suggested that most students lacked a conceptual understanding of using variables to create generalized problem solutions that could work with any set of inputs. Additionally, students had difficulty with specific mechanics of using variables such as storing user input in a variable, updating variable values, and using the values stored in variables. This study underscores the need for careful design, use, and analysis of elementary CT-integrated lessons and assessments to introduce and reinforce the conceptual understanding and specific mechanics of variables for elementary students.

This basic interpretive qualitative study investigated four students’ debugging behaviors in Zoombinis, a game-based computational thinking (CT) environment. Analysis involved deductive coding of students’ debugging behaviors using videos of students’ computer screens. The findings revealed a range of debugging behaviors and strategies. Findings also indicated that students could articulate an intermediate understanding of debugging as related to the debugging LT [7].

Elementary schools throughout the United States are attempting to integrate computational thinking (CT) into their instruction, often without guidance from research about effective approaches for achieving particular CT goals. This cross-case study investigated the school-wide integration of CT in three elementary schools in a large urban school district in the Northeast that has a district-led CS for All initiative. Data included interviews with teachers, professional development providers, and school administrators as well as surveys from teachers and classroom observations in each participating school. Findings revealed three distinct approaches to integration: (a) single teacher leader-driven model, (b) scaffolded professional development model, and (c) intensive coaching model. These approaches reflect the visions set by administrators and teachers, methods used by professional development providers, and cultures of each school. Across the case studies, common pedagogical approaches included strategic use of both unplugged and plugged activities with a range of computational tools, a focus on collaborative project-based learning, and the use of CT-specific academic language to anchor new CT learning within the academic disciplines. The study highlighted advantages and challenges within each integration approach with implications for schools considering CT integration.

Debugging has been an expanding topic in K-12 computer science (CS) education research. However, few studies have focused on in-depth analysis of elementary students' debugging in block-based visual programming environments. Thus, using the video analysis technique, this basic interpretive qualitative study aimed to explore what debugging behaviors students exhibited and how these debugging behaviors mapped with an existing K-8 debugging learning trajectory (LT). Findings revealed five types of debugging behaviors and four primary challenges. These debugging behaviors mapped to five consensus goals in the K-8 debugging learning trajectory. Future research will focus on students' efficiency in using debugging strategies and understanding of debugging.

For K-8 computer science (CS) education to continue to expand, it is essential that we understand how students develop and demonstrate computational thinking (CT). One approach to gaining this insight is by having students articulate their understanding of CT through cognitive interviews. This study presents findings of a cognitive interview study with 13 fourth-grade students (who had previously engaged in integrated CT and mathematics instruction) working on CT assessment items. The items assessed four CT concepts: sequence, repetition, conditionals, and decomposition. This study analyzed students\textquotesingle articulated understanding of the four CT concepts and the correspondence between that understanding and hypothesized learning trajectories (LTs). We found that 1) all students articulated an understanding of sequence that matched the intermediate level of the Sequence LT; 2) a majority of students\textquotesingle responses demonstrated the level of understanding that the repetition and decomposition items were designed to solicit (8 of 9 responses were correct for repetition and 4 of 6 were correct for decomposition); and 3) less than half of students\textquotesingle responses articulated an understanding of conditionals that was intended by the items (4 of 9 responses were correct). The results also suggested questioning the directional relationships of two statements in the existing Conditionals LT. For example, unlike the LT, this study revealed that students could understand "A conditional connects a condition to an outcome'' before "A condition is something that can be true or false.''

This study investigated school-wide integration of computational thinking (CT) in elementary schools of: 1) systems-level approaches to integration; 2) teachers' understanding and implementation of CT integration, and 3) challenges to integration. Data sources include interviews with teachers, professional development (PD) providers, principals as well as implementation observations. Findings revealed three distinct approaches: (a) Lone STEM teacher implementer, (b) PD scaffolded approach, and (c) Whole school coach-in-residence approach. Teachers generally viewed CT in the context of problem-solving. Although struggles and challenges existed in all three schools, administrators, PD providers, and teachers all had a high commitment to CT integration.