Not enough students are earning bachelor’s degrees in Computer Science, which is shocking as computing jobs are growing by the thousands (Zampa, 2016). These jobs have high-paying salaries and are not going to fade from the future any time soon, that is why the falling rates of computer science graduates are alarming. The working hypothesis on why so few college students major in computer science is that most think that it is too hard to learn (Wang, 2017). But I believe the real reason lies in that computer science is not an educational subject that is taught before university, which is too late for most students because by ages 12 to 13 (about seventh to eighth grade) they have decided that computer science concepts are “too difficult” for them to learn (Learning, 2022). Implementing a computer science-based education at an earlier age can possibly circumvent this seen development where students begin to lose confidence and doubt their abilities to learn computer science. This can be done easily by integrating computer science into academic subjects that are already taught in elementary schools such as science, math, and language arts as computer science uses logic, syntax, and other skills that are broadly applicable. Thus, I have created a introductory lesson plan for an elementary school class that incorporates learning how to code with robotics to promote learning computer science principles and destigmatize that it is “too hard” to learn in university.

A Skunkworks project is the name given to a small team of individuals leading an innovative undertaking, and conducting research and development outside of the normal scope of an organization. With this concept in mind, our team of six individuals was tasked with finding and conceptualizing innovative solutions within varying business markets of interest. Our team started off with five markets that we identified issues in and were passionate about solving. These included Sports Engagement, Education, Student Debt, Digital Literacy, and Viral Health. From extensive research, trial and error, and endless conversations we settled on creating business models in two final areas: Student Debt and Viral Health. Our research in Student Debt led us to the discovery that the average Arizona State student, takes out $21,237 in loans for their four year degree and in the whole state of Arizona, a student takes on an average of $22,253. Our solution to this problem was to create a student financial app that served as an efficient debt tracker that provided important information about finances, investing, and student loan information. Additionally, our team also wanted the address the issue of sexually transmitted diseases, just a small scope of Viral Health, within Arizona State University. Our research led us to discover that 50% of people report not getting tested, and from this population most reported it was due to anxiety and financial issues. From our research the StayInformed app was created to provide students with better accessibility to both at-home and clinic testing services, and updated education on sexual health. With this project model we hope to increase the rate of students testing and allow students more agency over their sexual health. Although these two services are addressing very different markets, they both utilize forward thinking technology to create much needed solutions and better the lives of students.

Augmented Reality (AR) especially when used with mobile devices enables the creation of applications that can help students in chemistry learn anything from basic to more advanced concepts. In Chemistry specifically, the 3D representation of molecules and chemical structures is of vital importance to students and yet when printed in 2D as on textbooks and lecture notes it can be quite hard to understand those vital 3D concepts. ARsome Chemistry is an app that aims to utilize AR to display complex and simple molecules in 3D to actively teach students these concepts through quizzes and other features. The ARsome chemistry app uses image target recognition to allow students to hand-draw or print line angle structures or chemical formulas of molecules and then scan those targets to get 3D representation of molecules. Students can use their fingers and the touch screen to zoom, rotate, and highlight different portions of the molecule to gain a better understanding of the molecule's 3D structure. The ARsome chemistry app also features the ability to utilize image recognition to allow students to quiz themselves on drawing line-angle structures and show it to the camera for the app to check their work. The ARsome chemistry app is an accessible and cost-effective study aid platform for students for on demand, interactive, 3D representations of complex molecules.

Machine learning is a rapidly growing field, with no doubt in part due to its countless applications to other fields, including pedagogy and the creation of computer-aided tutoring systems. To extend the functionality of FACT, an automated teaching assistant, we want to predict, using metadata produced by student activity, whether a student is capable of fixing their own mistakes. Logs were collected from previous FACT trials with middle school math teachers and students. The data was converted to time series sequences for deep learning, and ordinary features were extracted for statistical machine learning. Ultimately, deep learning models attained an accuracy of 60%, while tree-based methods attained an accuracy of 65%, showing that some correlation, although small, exists between how a student fixes their mistakes and whether their correction is correct.