The "Computer Science for All" initiative advocates for universal access to computer science (CS) instruction. A key strategy toward this end has been to establish CS content standards outlining what all students should have the opportunity to learn. Standards can support curriculum quality and access to quality CS instruction, but only if they are used to inform curriculum design and instructional practice. Professional learning offered to teachers of CS has typically focused on learning to implement a specific curriculum, rather than deepening understanding of CS concepts. We set out to develop a set of educative resources, formative assessment tools and teacher professional development (PD) sessions to support middle school CS teachers' knowledge of CS standards and standards-aligned formative assessment literacy. Our PD and associated resources focus on five CS standards in the Algorithm and Programming strand and are meant to support teachers using any CS curriculum or programming language. In this experience report, we share what we learned from implementing our standards-based PD with four middle school CS teachers. Teachers initially perceived standards as irrelevant to their teaching but they came to appreciate how a deeper understanding of CS concepts could enhance their instructional practice. Analysis of PD observations and exit surveys, teacher interviews, and teacher responses to a survey assessing CS pedagogical content knowledge demonstrated the complexity of using content standards as a driver of high-quality CS instruction at the middle school level, and reinforced our position that more standards-focused PD is needed.

The development of the CS content standards underscores the importance of curricula aligned with the standards, ensuring equitable coverage of CS concepts for all students. Because standards are broad, we emphasize the need for CS curricula to specify not only the standards they align with but also which aspects of the standards they align with and how. We map one common middle school CS curriculum to a few standards to demonstrate this need.

The rapid expansion of K-12 CS education has made it critical to support CS teachers, many of whom are new to teaching CS, with the necessary resources and training to strengthen their understanding of CS concepts and how to effectively teach CS. CS teachers are often tasked with teaching different curricula using different programming languages in different grades or during different school years, and tend to receive different professional development (PD) for each curriculum they are required to teach. This often leads to a lack of deep understanding of the underlying CS concepts and how different curricula address the same concepts in different ways. Empowering teachers to develop a deep understanding of CS standards, and use formative assessments to recognize common student challenges associated with the standards, will enable teachers to provide more effective CS instruction, irrespective of the curriculum and/or programming language they are tasked with using. This position paper advocates supporting CS teacher professional learning by supplementing existing curriculum-specific teacher PD with standards-aligned PD that focuses on teachers' conceptual understanding of CS standards and ability to adapt instruction based on student understanding of concepts underlying the CS standards. We share concrete examples of how to design standards-aligned educative resources and instructionally supportive tools that promote teachers' understanding of CS standards and common student challenges and develop teachers' formative assessment literacy, all essential components of CS pedagogical content knowledge.

The K-12 CS Framework provides guidance on what concepts and practices students are expected to know and demonstrate within different grade bands. For these guidelines to be useful in CS education, a critical next step is to translate the guidelines to explicit learning targets and design aligned instructional tools and assessments. Our research and development goal in this paper is to design a playful, curriculum-neutral assessment aligned with the 'Data and Analysis' concept (grades 6-8) from the CS framework. Using Evidence Centered Design and Participatory Design, we present a set of assessment guidelines for assessing data and analysis, as well as a set of design considerations for integrating data and analysis across middle school curricula in CS and non-CS contexts. We outline these contributions, describe how they were applied to the development of a game-based formative assessment for data and analysis, and present preliminary findings on student understanding and challenges inferred from student gameplay.

In today's increasingly digital world, it is critical that all students learn to think computationally from an early age. Assessments of Computational Thinking (CT) are essential for capturing information about student learning and challenges. Several existing K-12 CT assessments focus on concepts like variables, iterations and conditionals without emphasizing practices like algorithmic thinking, reusing and remixing, and debugging. In this paper, we discuss the development of and results from a validated CT Practices assessment for 4th-6th grade students. The assessment tasks are multilingual, shifting the focus to CT practices, and making the assessment useful for students using different CS curricula and different programming languages. Results from an implementation of the assessment with about 15000 upper elementary students in Hong Kong indicate challenges with algorithm comparison given constraints, deciding when code can be reused, and choosing debugging test cases. These results point to the utility of our assessment as a curricular tool and the need for emphasizing CT practices in future curricular initiatives and teacher professional development.

As K-12 computer science (CS) education initiatives scale throughout the U.S., researchers seek to understand the context-specific relationships between CS instruction and student learning. Evaluation of instruction requires valid measures of curriculum implementation. We have developed measures for identifying conditions for successful implementation of an introductory high school computer science curriculum along two-dimensions: teaching quality and curriculum enactment. Additionally, we have defined three types of instructional strategies for teaching quality. Quantitative and qualitative data were collected from 53 teachers through surveys and interviews. Data were aggregated and integrated to derive scaled measures for the instructional strategies and curriculum adaptation, and implementation measures were correlated with student end-of-unit assessment data. We found potential factors that can enhance or impede the successful implementation of CS curriculum materials, and we have identified several broad issues associated with scaling up CS curricular implementation.

This study investigated patterns in the development of computational thinking practices in the context of the Exploring Computer Science (ECS) program, a high school introductory CS course and professional development program designed to foster deep engagement through equitable inquiry around CS concepts. Past research indicates that the personal relevance of the ECS experience influences students' expectancy-value towards computer science. Expectancy-value is a construct that is predictive of career choices. We extended our research to examine whether expectancy-value influences the development of computational thinking practices. This study took place in the context of two ECS implementation projects across two states. Twenty teachers, who implemented ECS in 2016-17, participated in the research. There were 906 students who completed beginning and end of year surveys and assessments. The surveys included demographic questions, a validated expectancy-value scale, and questions about students' course experiences. The assessments were developed and validated by SRI International as a companion to the ECS course. Overall, student performance statistically increased from pretest to posttest with effect size of 0.74. There were no statistically significant differences in performance by gender or race/ethnicity. These results are consistent with earlier findings that a personally relevant course experience positively influences students' expectancy for success. These results expanded on prior research by indicating that students' expectancy-value for computer science positively influenced student learning.

As K-12 computer science (CS) initiatives scale throughout the U.S., educators face increasing pressure from their school systems to provide evidence about student learning on hard-to-measure CS outcomes. At the same time, researchers studying curriculum implementation and student learning want reliable measures of how students apply their CS knowledge. This paper describes a two-year validation study focused on end-of-unit and cumulative assessments for Exploring Computer Science, an introductory high school CS curriculum. To develop the assessments, we applied a principled methodology called Evidence-Centered Design (ECD) to (1) work with various stakeholders to identify the important computer science skills to measure, (2) map those skills to a model of evidence that can support inferences about those skills, and (3) develop assessment tasks that elicit that evidence. Using ECD, we created assessments that measure the practices of computational thinking, in contrast to assessments that only measure CS conceptual knowledge. We iteratively developed and piloted the assessments with 941 students over two years and collected three types of validity evidence based on contemporary psychometric standards: test content, internal structure, and student response processes. Results show that reliability was moderate to high for each of the unit assessments; the assessment tasks within each assessment are well aligned with each other and with the targeted learning goals; and average scores were in the 60 to 70 percent range. These results indicate that the assessments validly measure students' computational thinking practices covered in the introductory CS curriculum. We discuss the broader issues we faced of balancing the need to use the assessment results for evaluation and research, and demands from teachers for use in the classroom.

Nationwide, efforts are focusing on taking computer science (CS) to scale in high school classrooms through the Exploring Computer Science (ECS) and AP CS Principles (CSP) courses. Recent inroads are also being made to take structured introductory curricula to middle school classrooms. Often, a starting point for teaching CS in middle and high school is a discussion around the seemingly simple question "What is a computer?" The question is aimed to help learners understand through debate and discussion what makes a computer a computer. This paper reports our analysis of (a) middle school students' discussions around this question, and (b) high school students' responses to an assessment question measuring this understanding. Our analyses of students' comments and responses reveal that a discussion around "what is a computer?" may be problematic for students, as it tends to focus on the tool, the "computer." We suggest that the discussion needs re-framing to focus instead on computing and computation.