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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. 

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. 

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. 

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. 

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. 

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.