Much debate surrounds the choice of programming language for teaching computer science. Our institution's replacement of a visual programming language (RAPTOR) with a textual programming language (Python) provided a novel opportunity to explore the impacts of the programming language on students' learning and perception of programming. We conducted a randomized comparative study that involved 1083 students who took our introductory computing course in the 2019-2020 academic year. A unique aspect of our work stems from our course being a general education requirement; thus, our study includes students with a wide variety of backgrounds and majors. This report presents a comparison of student performance in each version of the course, including the impact of the programming language on underrepresented groups, and provides a summary of student feedback. Our results show that students in our introductory course performed similarly overall, but overwhelmingly perceived Python to be more valuable.

The lack of large and diverse datasets of student code samples limits some forms of computer science education research. To address this problem, we created FalconCode, a novel collection of over 1.5 million Python programs from over two thousand undergraduate students at the United States Air Force Academy. FalconCode captures over five semesters worth of code samples from our introduction to computing course, which is taken by every student regardless of their academic major. The dataset contains student code submissions for over 800 programming assignments, as well as additional metadata such as the prompt for each assignment, the testcase(s) used to evaluate student submissions, and the specific skills needed to solve each problem. In this paper, we describe the methodology used to create FalconCode and the steps taken to anonymize the data. We then describe FalconCode's data schema, and show how it can support a wide range of research---including those utilizing machine learning (ML) and artificial intelligence (AI). FalconCode is provided free-of-charge, and is available upon request for computer science education research.

In this full research paper, we compare the impact of learning a visual versus textual programming language in an introductory computing course that is a general education requirement at our institution. We conducted a randomized comparative study with "experimental" sections that were taught using Python instead of RAPTOR, a flowchart-based programming language. The populations of students learning each programming language were similar with respect to gender, race, and predicted performance based upon standardized test scores and prior post-secondary education. Although students' performance on the whole was similar regardless of the programming language taught, predicted performance is correlated with SAT Math scores, grades in mathematics courses (specifically Calculus II), and, for lower-performing students, grades in other courses that satisfy general education requirements. That is, students from these groups who had lower predicted performance and learned Python performed worse on average than their peers who learned RAPTOR, and students with higher predicted performance outperformed (on average) their peers who learned RAPTOR. In addition, students' performance in subsequent computer science courses was not correlated with their performance and the language they learned in our introductory computing course. Our results raise important questions about the role of an introductory computing course in promoting equity and engaging students from historically underrepresented groups in computing fields.

Brain Computer Interface (BCI) technologies have the potential to improve the lives of millions of people around the world, whether through assistive technologies or clinical diagnostic tools. Despite advancements in the field, however, at present consumer and clinical viability remains low. A key reason for this is that many of the existing BCI deployments require substantial data collection per end-user, which can be cumbersome, tedious, and error-prone to collect. We address this challenge via a deep learning model, which, when trained across sufficient data from multiple subjects, offers reasonable performance out-of-the-box, and can be customized to novel subjects via a transfer learning process. We demonstrate the fundamental viability of our approach by repurposing an older but well-curated electroencephalography (EEG) dataset and benchmarking against several common approaches/techniques. We then partition this dataset into a transfer learning benchmark and demonstrate that our approach significantly reduces data collection burden per-subject. This suggests that our model and methodology may yield improvements to BCI technologies and enhance their consumer/clinical viability.

As artificial intelligence (AI) becomes more widely utilized, there is a need for non-computer scientists to understand 1) how the technology works, and 2) how it can impact their lives. Currently, however, computer science educators have been reluctant to teach AI to non-majors out of concern that the topic is too advanced. To fill this gap, we propose an AI and machine learning (ML) curriculum that is specifically designed for first-year students. In this paper, we describe our curriculum and show how it covers four key content areas: core concepts, implementation details, limitations, and ethical considerations. We then share our experiences teaching our new curriculum to 174 randomly-selected Freshman students. Our results show that non-computer scientists can comprehend AI/ML concepts without being overwhelmed by the subject material. Specifically, we show that students can design, code, and deploy their own intelligent agents to solve problems, and that they understand the importance and value of learning about AI in a general-education course.

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