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Engineering design that requires mathematical analysis, scientific understanding, and technology is critical for preparing students for solving engineering problems. In simulated design environments, students are expected to learn about science and engineering through their design. However, there is a lack of understanding concerning linking science concepts with design problems to design artifacts. This study investigated how 99 high school students applied science concepts to solarize their school using a computer-aided engineering design software, aiming to explore the interaction between students’ science concepts and engineering design behaviors. Students were assigned to three groups based on their design performance: the achieving group, proficient group, and emerging group. By mining log activities, we explored the interactions among students’ application of science concepts, engineering design behaviors, design iterations, and their design performance. We found that the achieving group has a statistically higher number of design iterations than the other two performance groups. We also identified distinctive transition patterns in students’ applying science concepts and exercising design behaviors among three groups. The implications of this study are then discussed. 

Abstract Music and computer science (CS) have profound historical and structural connections, with programming music offering a promising avenue for engaging children in CS through creative expression. To foster this engagement, our team developed M-Flow, a flow-based music programming platform designed to introduce students to CS via music. Despite extensive existing research in music and CS education, experience reports and empirical studies on K-12 teachers' implementation and its impact on young kids' learning are limited. Therefore, we recruit elementary school teachers and students with no or limited prior programming experience, introducing them to M-Flow and its curriculum through a professional development workshop, a semester's job embedded support, and classroom implementation. We describe the experiences of teachers as they attempt to integrate music and CS, the challenges they face, and the influence on students' attitudes toward learning computing concepts. Specifically, we reflect on our intervention by conducting a sequential mixed-method evaluation. During the qualitative phase, we collected multiple sources of data from three teachers through focus groups and debriefings after a semester of classroom implementation. Thematic analysis of workshop activities, interviews, and debrief videos revealed three themes with seven sub-themes on teachers' integration of flow-based music programming and two themes with five sub-themes on challenges faced by the teachers. In the quantitative phase, we gathered data on attitudes and self-efficacy from 75 students taught by these teachers. Results indicate that the flow-based music programming environment provided an engaging programming experience for students and significantly increased their self-efficacy towards learning programming. 

According to NASEM (2018), data science has foundations in computing, mathematics, and statistics. However, at the K-12 level, these foundations are usually taught as standalone courses that are unconnected with each other. Students may struggle to see their connections. We proposed a framework unifying those foundations using mathematical logic. A core concept in mathematical logic is function. A general function has one or more possibly non-number inputs and an output. Data science motivates a comprehensive understanding of functions and provides extensive culturally relevant, real-world, and data-rich problems and applications for students to practice their understanding. It is interesting to know how well students understand functions. We developed a six-lesson online module with more than 100 in-lesson questions. Initial analysis of the students’ answers to the questions shows that students can understand the basics of the general functions but have more difficulties in involved applications of functions. 

Silicon-based spin qubits represent a promising technology for scalable quantum computing. However, the complex nature of this field, which requires a deep understanding of quantum mechanics, materials science, and nanoelectronics, poses a significant challenge in making it accessible to future engineers and scientists. Spin Quantum Gate Lab, a spin qubit simulation tool, is proposed in this paper to address this obstacle. This tool is designed to introduce key concepts of spin qubit to undergraduate students, enabling the simulation of single-qubit rotational gates and two-qubit controlled-phase gates. By providing hands-on experience with quantum gate operations, it effectively links theoretical quantum concepts to practical experience, fostering a deeper understanding of silicon-based quantum computing. 

Data science is revolutionizing academia and industry, creating a high demand for a workforce fluent in this field. While the availability of data science courses has increased recently, few curricula rigorously build on mathematical logic. The LogicDS Project addresses this gap by engaging high school students from rural communities in an online data science course integrating mathematics, statistics, and programming into a unified framework based on logic and reasoning. A one-week course, consisting of six lessons, was developed and 110 participants were recruited. Pre- and post-intervention data, along with students' LMS activity logs, were collected to analyze engagement. Results indicate that the Logic-Based framework effectively engages students from diverse backgrounds, with participants finding the course valuable for learning data science skills. Notably, entropy analysis of student activity logs correlated with other mixed methods analyses, providing insights into engaging K-12 students in data science education. 

Abstract: There is a consensus to introduce data science to secondary schools. However, data science is interdisciplinary in nature and not easy to teach in K−12 settings. We proposed a new approach to integrate mathematics, statistics, and programming—the foundations of data science—for high school students based on set theory and logic. We developed an 8-week data science foundation course and implemented it in a public high school. We conducted semistructured group interviews to collect students’ feedback on the course and the new approach. Students thought the approach could well connect the topics and helped them learn the interdisciplinary content.