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Dynamic Programming (DP) is commonly regarded as one of the most difficult topics in the upper-level algorithms curriculum. The teaching of metacognitive strategies may prove effective in helping students learn to design DP algorithms. To explore both whether students learn and use these strategies on their own and the effect of guidance about using these strategies, we conducted think-aloud interviews with structured guidance at two points in a college algorithms course: once immediately after students learned the concept and once at the end of the course. We explore 1) what metacognitive strategies are commonly employed by students, 2) how effectively they help students solve problems, and 3) to what extent structured guidance about using metacognitive strategies is effective. We find that these strategies generally help students make progress in solving DP problems, but that they can mislead students as well. We also find that the adoption of these strategies is an individualized process and that structured strategy guidance is often insufficient in allowing students to solve individual DP problems, indicating the need for more extensive strategy instruction. 

Though the increased availability of Large Language Models (LLMs) presents signi!cant potential for change in the way students learn to program, the text-based nature of the available tools currently preclude block-based languages from much of that innovation. In an attempt to remedy this, we identify the strengths and weaknesses of using a transpiler to leverage the existing learning in commercially available LLMs and Scratch, a visual block-based programming language. Using only prompt engineering, we evaluate an LLM’s performance on two common classroom tasks in a Scratch curriculum. We evaluate the LLM’s ability to: 1) Create project solutions that compile and satisfy project requirements and 2) Analyze student projects’ completion of project requirements using natural language. In both cases, we !nd results indicating that prompt-engineering alone is insu"cient to reliably produce high-quality results. For projects of medium complexity, the LLM-generated solutions con- sistently failed to follow correct syntax or, in the few instances with correct syntax, produce correct solutions. When used for auto- grading, we found a correlation between scores assigned by the official Scratch Encore autograder and those generated by the LLM, nevertheless the discrepancies between the ‘real’ scores and the scores assigned by the LLM remained too great for the tool to be reliable in a classroom setting. 

According to an ecological affordances perspective, any static curriculum has a set of affordances, and differences in teachers, students, and the teaching environment change how those affordances are viewed and used. Therefore, teaching is a relationship between the curriculum, the teacher, and the students. As such, it is not only possible but expected that a teacher will diverge from the details of a lesson plan to better accommodate the needs of themselves as a teacher and their students as learners. In this study, we report on a mixed-methods investigation that explores the different ways upper-elementary and middle-school (7- 13 y.o. students) teachers implement the Scratch-based TIPP&SEE learning strategy and the reasoning for their approaches. As expected, we find that teachers across grade levels often deviate from lesson plan details to cater to their own classrooms. For example, teachers serving younger grades were far more likely to keep scaffolds that lesson plans suggest removing. The varied degree of deviation suggests that the repeated use of a learning strategy, alongside lesson plans that present a variety of scaffolded implementations, is beneficial in enabling teachers to adapt lesson content to serve the needs of their specific classroom. 

Quantum computing presents a paradigmatic shift in the field of computation, in which unintuitive properties of quantum mechanics can be harnessed to change the way we approach a wide range of problems. However, due to the mathematics and physics perspective through which quantum computing is traditionally presented, most resources are inaccessible to many undergraduate students, let alone the general public. It is thus imperative to develop resources and best-practices for quantum computing instruction accessible to students at all levels. In this paper, we describe the development and results of our Massive Open Online Course (MOOC) "Introduction to Quantum Computing for Everyone." This course presents an introduction to quantum computing with few technical prerequisites. In the first half of the course, quantum computing concepts are introduced with a unique, purely visual representation, allowing students to develop conceptual understanding without the burden of learning new mathematical notation. In the second half, students are taught the formal notation for concepts and objects already introduced, reinforcing student understanding of these concepts and providing an applicable context for the technical material. Most notably, we find that introducing the math content in the curriculum's second stage led to no drops in engagement or student performance, suggesting that our curriculum's spiral structure eased the technical burden. 

Quantum computing (QC) is an emerging field at the intersection of computer science and physics. Harnessing the power of quantum mechanics, QC is expected to solve otherwise intractable problems significantly faster, including in encryption, drug development, and optimization. High-quality and accessible QC resources are needed to help students develop the critical skills and confidence to contribute to the field. However, existing programs are often aimed at college students with an advanced mathematics or physics background, shutting out potential innovators. To make quantum learning resources for a broad, young audience, we designed Qupcakery, a puzzle game that introduces players to several core QC concepts: quantum gates, superposition, and measurement. We present preliminary testing results with both middle school and high school students. Using in-game data, observation notes, and focus group interviews, we identify student challenges and report student feedback. Overall, the game is at an appropriate level for high school students but middle school students need more levels to practice when new concepts are introduced.