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Worldwide, national initiatives have led to many school districts implementing computing curricula at the primary level. At that age, students are learning the foundational skills of reading and math. It is important to understand how computing can influence the development of these skills. While some argue that learning computing sharpens problem-solving skills that are applicable to other subjects, evidence supporting this belief is thin. In a quasi-experimental study of fourth-grade (ages 9-10) students, we compared state reading and math test scores of students receiving computing instruction with students who did not. Our findings demonstrated that a more open-ended, less scaffolded form of computing instruction was linked to performance gains in math, but not in reading (𝐹 (2, 232) = 11.08, 𝑝 < .01, 𝜂𝑝2 = .0625). When looking at students who face academic challenges that can impact reading and math, the same trend applied to students with economic disadvantages and students with limited English proficiency, but not for students with disabilities. These results suggest that moderately scaffolded computing instruction supports the development of skills applicable to math, a step towards better understanding the relationship between learning opportunities in computing and outcomes in other subjects. 

With the growth of Computer Science (CS) and Computational Thinking (CT) instruction in the primary/elementary domain, it is important that such instruction supports diverse learners. Four categories of students ś students in poverty, multi-lingual students, students with disabilities, and students who have below-grade-level proficiency in reading and math, may face academic challenges that can hinder their learning in CS/CT curricula. However, little is known about how to support these students in CS/CT instruction, especially at this young age. TIPP&SEE, a meta-cognitive strategy that scaffolds learning by proceduralizing engagement through example code, may offer some support. A quasi-experimental study revealed that the gaps between students with and without academic challenges narrowed when using the TIPP&SEE strategy, indicating its promise in providing equitable learning opportunities in CS/CT. 

As computer science instruction gets offered to more young learn- ers, transitioning from elective to requirement, it is important to explore the relationship between pedagogical approach and student behavior. While different pedagogical approaches have particular motivations and intended goals, little is known about to what degree they satisfy those goals. In this paper, we present analysis of 536 students’ (age 9-14, grades 4-8) work within a Scratch-based, Use-Modify-Create (UMC) curriculum, Scratch Encore. We investigate to what degree the UMC progression encourages students to engage with the content of the lesson while providing the flexibility for creativity and exploration. Our findings show that this approach does balance structure with flexibility and creativity, allowing teachers wide variation in the degree to which they adhere to the structured tasks. Many students utilized recently-learned blocks in open-ended activities, yet they also explored blocks not formally taught. In addition, they took advantage of open-ended projects to change sprites, backgrounds, and integrate narratives into their projects. 

With many school districts nationwide integrating Computer Science (CS) and Computational Thinking (CT) instruction at the K-8 level, it is crucial researchers closely inspect the relationship between program expression and student understandings. In this study, we propose and report on our use of Scratch Charades, a game in which students act out Scratch scripts while others build them. The purpose of Scratch Charades is to familiarize students with scripts and blocks without the cognitive overhead of the complex user interface. However, in this study, we also used it to elicit student understandings about Scratch blocks and scripts to design mnemonics to help students debug their code. We propose two building and/or debugging strategies based on our observations. 

As many school districts nationwide continue to incorporate Computer Science (CS) and Computational Thinking (CT) instruction at the K-8 level, it is crucial that we understand the factors and skills, such as reading and math proficiency, that contribute to the success of younger learners in a computing curriculum and are typically developed at this age. Yet, little is known about the relationship between reading and math proficiency, and the learning of key CS concepts at the elementary level. This study focused on 4th-grade students (ages 9-10) who were taught events, sequence, and repetition through an adaptation of the Creative Computing Curriculum. While all students benefited from access to such a curriculum, there were statistically-significant differences in learning outcomes, especially between students whose reading and math proficiency are below grade-level, and students whose proficiency are at or above grade-level. This performance gap suggests the need for curricular improvement and learning strategies that are CS specific for students who struggle with reading and math. 

The CS community has struggled to assess student learning at the K-8 level, with techniques ranging from one-on-one interviews to written assessments. While scalable, automated techniques exist for analyzing student code, a scalable method for assessing student comprehension of their own code has remained elusive. This study is a first step in bridging the gap between the knowledge gained from interviews and the time efficiency and scalability of written assessments and automated analysis. The goal of this study is to understand how student answers on various types of questions differ depending on whether they are being asked about their own code or generic code. We find that while there were no statistically-significant differences in overall scores, questions about generic and personalized code of comparable complexity are far from equivalent. Our qualitative analyses revealed interesting patterns in student responses, inviting further research into this assessment technique. In particular, students answered differently from students with generic code when presented with individual blocks from their code taken out of context and placed into different code snippets, and students answered in a way that demonstrates a functional, instead of structural, understanding on Explain in Plain English (EiPE) questions.