Integrating literacy and computational thinking (CT) can broaden computer science education participation, especially for multilingual learners. This study examined how the Computing and AI for All (CAIforALL) Act 1 Curriculum, an ELA-integrated Scratch-based CT curriculum, impacts coding attitudes among elementary students in predominantly Latine and multilingual districts. The curriculum integrates literacy strategies into CT instruction as proposed by the National Academies of Sciences, Engineering, and Medicine (NASEM) to support multilingual learners. We conducted a cluster randomized controlled trial with 1,325 students in grades 3–5 across 23 schools in two suburban districts. The treatment group used an ELA-integrated CT curriculum for a school year while controls continued with business-as instruction. Pre- and post-surveys measured five coding attitude constructs: confidence, interest, utility, perceived coding values of social circles, and perceptions of young coders. We estimated treatment effects using a two-level hierarchical linear model, controlling for student and classroom characteristics. Findings show no statistically significant differences emerged in overall coding attitudes between groups. However, students exposed to a year of ELA-integrated CT curriculum showed significant increases in coding confidence. The curriculum did not significantly affect other attitude dimensions. Findings suggest that an ELA-integrated CT curriculum can enhance coding confidence among elementary students, demonstrating the value of early computing exposure and integrated approaches.

Informal computer science (CS) learning environments, such as after-school programs and summer camps, introduce students to computing outside traditional classrooms, helping broaden participation in CS. This poster reviews literature on curricula and instructor training across informal CS programs, highlighting significant variation in content, structures, and delivery. Evidence suggests that while these programs improve student attitudes and conceptual understanding, challenges in engagement, pacing, and instructor preparedness persist, particularly with reliance on lecture-style activities and varying student programming backgrounds. Through a case study of a California nonprofit's afterschool coding classes, common classroom management and engagement issues are identified. In response, we are developing a toolkit of supplemental resources to support teachers based on the available evaluation research in addition to collaboratively revising the curriculum of this afterschool program (which will be complete in 2026). We present the proposed toolkit in this poster. Our findings aim to inform future curriculum development and targeted teacher training, advancing the effectiveness of informal CS learning environments.

Despite widespread adoption of the K-12 Computer Science Standards published by the Computer Science Teachers Association (CSTA) in 2017, there remain few validated assessments for computer science that researchers and educators can use to measure students' conceptual understanding. Traditional assessments that do exist tend to consist largely of multiple-choice questions that do not adequately measure students' programming skills or their use of the computer science practices described in the standards. This project aims to develop and iteratively refine a mixed-format assessment for middle school computer science. The first phase of this work unpacked the middle school (6-8 grade band) CS standards to develop prototype items, including live coding tasks, that would assess the learning outcomes addressed therein. In phase 2, we tested the prototype items with students through cognitive interviews to better understand their response processes and the complex problem-solving and reasoning they demonstrated while taking the assessment.

There has been significant progress in increasing the access to computing education for many K-12 students, including states adopting computer science (CS) standards and/or requiring CS courses. This includes the creation of block-based programming languages to make programming more accessible to younger students. Despite this progress, a new challenge has emerged: Students often struggle to transfer conceptual knowledge when transitioning to a new programming language (e.g., transitioning to a text-based programming after learning a block-based programming language). This poster presents the results of teacher interviews regarding the examples of knowledge transfer they observe in their classrooms. These interviews are part of an overarching project that aims to address the challenge of knowledge transfer between programming languages by developing a framework to support such transfer and deliver curricular supports that can be used to aid students' productive knowledge transfer between programming languages.

Rich classroom discussion, or discourse, has long been a recommended pedagogical practice in K-12 math and science education. Research shows that discourse is beneficial for all learners, but especially for English learners and minoritized students in STEM. Discourse helps develop students' agency, academic language, and conceptual understanding. Some K-12 computer science (CS) curricula incorporate student discourse, but we believe it is under-used. In this paper, we review how discourse helps students learn, discuss the use of discourse in CS and math education, share ideas for promoting discourse in CS classrooms, and call on curriculum developers, teacher professional learning providers, and researchers to support the increased use of discourse in K-12 CS education.

Data science education can help broaden participation in computer science (CS) because it provides rich, authentic contexts for students to apply their computing knowledge. Data literacy, particularly among underrepresented students, is critical to everyone in this increasingly digital world. However, the integration of data science into K-12 schools is nascent, and the pedagogical training of CS teachers in data science remains limited. Our research-practice partnership modified an existing data science unit to include two pedagogical techniques known to support minoritized students: rich classroom discourse and personally-relevant problem-solving. This paper describes the iterative design process we used to revise and pilot this new data science unit.

Understanding mathematical equivalence is foundational for developing early algebraic thinking. Despite its importance, many young students struggle with the concept and use incorrect strategies when solving equivalence problems. Immediate, computer-based feedback may help students learn correct strategies. However, designing effective feedback for math equivalence is challenging. In this work-in-progress paper, we discuss usability studies and child-centered design with young students. Our goal is to better understand how students make sense of different kinds of feedback, what kinds of formats they prefer, and what actions they take after receiving the feedback. By centering young students’ perspectives in the design process, we can avoid blind spots created by our adult researcher perspectives and increase the likelihood of student engagement and knowledge gains. We describe the extended iterative process required to help students develop a formal understanding of math equivalence by creating usable and effective computer-based feedback.

Computing education through informal (out-of-school-time) programs provide a pathway for students of all ages to learn introductory programming and enter computing careers. Many of these programs explicitly aim to broaden participation in computing careers by recruiting and meeting the needs of students from historically underrepresented groups in computer science (CS).

The CS education community has over the years recognized the importance of data science by including it in the seminal K-12 CS Framework. The move is prompted by research that shows data science is a great tool to broaden participation in CS because it offers students an opportunity to apply their computing knowledge to socially relevant problems. Broadening participation, particularly among underrepresented students, is critical to the future health and stability of the field. However, data science is still a relatively new in the context of K-12 schools and few CS teachers are pedagogically trained in data science. In order to test whether or not data science can be a tool to increase student representation in CS and help schools implement more data science curriculum, our project partnered with a local school district to modify an existing data science unit. This work explores the process of how our research practice partnership tackled the development of the new data science unit.

Background and Context. With increasing efforts to bring computing education opportunities into elementary schools, there is a growing need for assessments, with arguments for validity, to support research evaluation at these grade levels. After successfully piloting a 10-question computational thinking assessment (Assessment of Computing for Elementary Students – ACES) for 4th graders in Spring 2020, we used our analyses of item difficulty and discrimination to iterate on the assessment. Objectives. To increase the number of potential items for ACES, we created isomorphic versions of existing questions. The nature of the changes varied from incidental changes that we did not believe would impact student performance to more radical changes that seemed likely to influence question difficulty. We sought to understand the impact of these changes on student performance. Method. Using these isomorphic questions, we created two versions of our assessment and piloted them in Spring 2021 with 235 upper-elementary (4th grade) students. We analyzed the reliability of the assessments using Cronbach’s alpha. We used Chi-squared tests to analyze questions that were identical across the two assessments to form a baseline of comparison and then ran Chi-Squared and Kruskal-Wallis H tests to analyze the differences between the isomorphic copies of the questions. Findings. Both assessment versions demonstrated good reliability, with identical Cronbach’s alphas of 0.868. We found statistically similar performance on the identical questions between our two groups of students, allowing us to compare their performance on the isomorphic questions. Students performed differently on the isomorphic questions, indicating the changes to the questions had a differential impact on student performance. Implications. This paper builds on existing work by presenting methods for creating isomorphic questions. We provide valuable lessons learned, both on those methods and on the impact of specific types of changes on student performance.

There is a burgeoning population of new CS teachers who are looking for additional support in their first few years of teaching, particularly around equitable and inclusive pedagogy. At the same time, there are a sizable number of teachers with multiple years of CS teaching experience who are looking for growth opportunities without taking on new courses. To address these needs, we are designing an innovative, equity-focused peer mentorship program for high school CS teachers. Mentors and mentees work together to support the mentee in identifying and achieving goals aligned to three of the CSTA Standards for CS Teachers: equity and inclusion, instructional design, and classroom practice. Mentors are provided with training and participate in a monthly community of practice focused on effective mentoring. The poster will share findings from our first year of implementation as well as examples of the materials we developed to support mentors and mentees.

The expansion of computer science (CS) into K-12 contexts has resulted in a diverse ecosystem of curricula designed for various grade levels, teaching a variety of concepts, and using a wide array of different programming languages and environments. Many students will learn more than one programming language over the course of their studies. There is a growing need for computer science assessment that can measure student learning over time, but the multilingual learning pathways create two challenges for assessment in computer science. First, there are not validated assessments for all of the programming languages used in CS classrooms. Second, it is difficult to measure growth in student understanding over time when students move between programming languages as they progress in their CS education. In this position paper, we argue that the field of computing education research needs to develop methods and tools to better measure students' learning over time and across the different programming languages they learn along the way. In presenting this position, we share data that shows students approach assessment problems differently depending on the programming language, even when the problems are conceptually isomorphic, and discuss some approaches for developing multilingual assessments of student learning over time.

As computing education expands to ''all'' students, so too must the assessment of computational learning. However, there are many challenges to designing and using computing assessments in a valid and reliable way. This is especially true with respect to pre-tests, or assessments given at the beginning of an intervention, course, or study. For elementary and middle school interventions, it is still likely that many students in any given study sample will have had no prior experience with computing. For high school interventions, students may have a wide range of prior experiences. How do you design or select a pre-test for these situations? In this poster, we discuss the design of pre-tests for the computing education research community. We outline the fundamental principles of pre-tests and the different purposes they serve in research studies. We complement these principles with examples of pre-tests used in current computing education research. This poster aims to provide guidance on how to intentionally develop and use pre-tests to strengthen the validity of our research findings and better inform on student learning outcomes.

The 1980s and 1990s saw a robust connection between computer science education and cognitive psychology as researchers worked to understand how students learn to program. More recently, academic disciplines such as science and engineering have begun drawing on cognitive psychology research and theories of learning to create instructional materials and teacher professional development materials based on theories of learning, to some success. In this paper, we follow a similar approach by highlighting common areas of interest between computer science education and cognitive psychology–specifically theories of analogical transfer–and discuss how cross-pollination of theoretical constructs between disciplines can support research on the teaching and learning of multiple programming languages. We will also discuss areas where computing education research can adapt the existing theories from cognitive psychology to develop domain-specific theories of knowledge transfer in computing and feed back into cognitive psychology research to inform larger debates about the nature of cognition and learning.

As reliance on technology increases in practically every aspect of life, all students deserve the opportunity to learn to think computationally from early in their educational experience. To support the kinds of computer science curriculum and instruction that makes this possible, there is an urgent need to develop and validate computational thinking (CT) assessments for elementary-aged students. We developed the Assessment of Computing for Elementary Students (ACES) to measure the CT concepts of loops and sequences for students in grades 3-5. The ACES includes block-based coding questions as well as non-programming, Bebras-style questions. We conducted cognitive interviews to understand student perspectives while taking the ACES. We piloted the assessment with 57 4th grade students who had completed a CT curriculum. Preliminary analyses indicate acceptable reliability and appropriate difficulty and discrimination among assessment items. The significance of this paper is to present a new CT measure for upper elementary students and to share its intentional development process.

Evaluating student learning in K-12 computer science programs is challenging due to the limited number of validated assessments, especially below the high school level. Because computer science is often taught as an engineering-oriented discipline in K-12, it seems natural to use students' programming projects as evidence of student learning. However, this approach is time-consuming and can overestimate what students understand. In this paper, we share our experience with using students' programming projects as a tool for evaluating a middle school computer science program at San Francisco Unified School District. The scoring and analysis of programming projects did generate useful insights about the nature of student learning in the program. More importantly, we found that the process of co-developing a scoring rubric and scoring projects alongside CS classroom teachers generated rich discussions and knowledge-sharing about the current state of the district's CS program, its values, and its long-term goals.

When creating assessments, computer science educators and researchers must balance items? cognitive complexity and authenticity against scoring efficiency. In this poster, the author reports results from an end-of-course assessment administered to over 500 high school students in an introductory block-based programming course. The poster focuses on three atypical multiple-choice items, in which students had to select all the correct responses. The items were designed to be more cognitively complex than simple multiple choice questions while remaining easy to score. Results show that this type of item was challenging for students but was predictive of their overall performance.

Pedagogical content knowledge (PCK) is specialized knowledge necessary to teach a subject. PCK integrates subject-matter content knowledge with knowledge of students and of teaching strategies so that teachers can perform the daily tasks of teaching. Studies in mathematics education have found correlations between measures of PCK and student learning. Finding robust, scalable ways for developing and measuring computer science (CS) teachers' PCK is particularly important in CS education in the United States, given the lack of formal CS teacher preparation programs and certifications. However, measuring pedagogical content knowledge is a challenge for all subject areas. It can be difficult to write assessment items that elicit the different aspects of PCK and there are often multiple appropriate pedagogical choices in any given teaching scenario. In this paper, we describe a framework and pilot data from a questionnaire intended to elicit PCK from teachers of high school introductory CS courses and we propose future directions for this work.

Educators, researchers, politicians, tech companies, and others continue to advocate for the importance of K-12 students learning computer science in our increasingly tech-driven society. One way school districts in the United States address this growing demand is by allowing teachers certified in other disciplines to lead computer science courses. Summer and weekend professional development opportunities support these educators in developing the expertise needed for effective computer science teaching, but a great portion of their learning to teach computer science will occur through on-the-job experiences. Our four-year NSF EHR grant explores how a job-embedded professional development program that pairs high school teachers with tech industry professionals supports educators in acquiring computer science teaching knowledge. The research presented in this poster focuses on the third year of the study and includes (a) a theoretical component focused on creating a framework to explain on-the-job computer science teaching knowledge development based on case studies with six teachers, and (b) an empirical component focused on the creation and administration of a computer science teaching knowledge assessment. By the time of the SIGCSE symposium, we expect to have pre-test results from the first administration of our teaching knowledge assessment, completed by both high school teachers and their collaborating tech industry professionals. This poster will present our theoretical framework, resultant teaching knowledge assessment with sample items, and analysis of participants' assessment responses and their relationship to specific teaching experiences.

One of the critical barriers to increasing pre-collegiate computer science course offerings in the U.S. is a lack of qualified computer science teachers. Programs such as TEALS, a teacher preparation program pairing high school teachers with computing professionals to offer CS courses, provide opportunities for in-service teachers to gain experience teaching computer science. However, it is not clear whether the high school teachers develop sufficient pedagogical expertise to sustain high-quality computer science course offerings at their schools. Furthermore, the field of computer science education lacks valid and reliable ways of measuring pedagogical content knowledge (PCK), a construct that describes the knowledge teachers need for effective instruction. In this poster, the authors present these results from the first year of a three-year NSF grant to study how TEALS participation influences novice computer science teachers' PCK: 1) a theoretical framework describing the critical components of CS PCK, 2) the results of the first field test of a CS PCK assessment, including the psychometric properties of the assessment, and 3) a comparison of how teachers performed on the assessment at the beginning and end of their first year of computer science teaching and how they performed relative to their computing professional mentors.

In recent years, augmented reality (AR) applications for children's entertainment have been gaining popularity, and educational organizations are increasingly interested in applying this technology to children's educational games. In this paper we describe our collaboration with teachers and game designers, in order to explore educational potential for AR technology. This paper specifically investigates the topics of: What mathematics curriculum topics should technological innovations address in the Grade 1-3 classrooms? Which of the topics are suitable for AR games? And, how can we facilitate an efficient dialogue between educators and game designers?